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Electronic Theses, Treatises and Dissertations
The Graduate School
2008
Deliberate Practice, Mental Representations,
and Skilled Performance in Bowling
Kevin R. (Kevin Russell) Harris
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FLORIDA STATE UNIVERSITY
COLLEGE OF ARTS AND SCIENCES
DELIBERATE PRACTICE, MENTAL REPRESENTATIONS, AND SKILLED
PERFORMANCE IN BOWLING
By
Kevin R. Harris, M.S.
A Dissertation submitted to the
Department of Psychology
In partial fulfillment of the
Requirements for the degree of
Doctor of Philosophy
Degree Awarded:
Summer Semester, 2008
The members of the Committee approve the dissertation of Kevin Russell Harris defended
on May 15, 2008.
K. Anders Ericsson
Professor Directing Dissertation
J. Michael Spector
Outside Committee Member
Colleen M. Kelley
Committee Member
Neil H. Charness
Committee Member
E. Ashby Plant
Committee Member
The Office of Graduate Studies has verified and approved the above named committee
members.
ii
ACKNOWLEDGEMENTS
First and foremost, I would like to thank Connie, my wonderful and loving wife,
without whom it easily could have been the graduate career that wasn’t.
I gratefully acknowledge Coach Michael Fine and the bowling teams at Florida
State University.
I also would like to thank those most responsible for my development throughout
graduate school. I specifically wish to thank my mentor at Florida State University,
Professor K. Anders Ericsson, for patiently developing me into a much stronger scholar. I
am also eternally grateful for the friendship, mentorship, and guidance of Dr. Paul Ward
and Dr. David Eccles, without whom things would be boring indeed. I would like to thank
Dr. Colleen Kelley, Dr. Neil Charness, Dr. Ashby Plant, and Dr. Mike Spector for their
input and guidance on this project. I would by remiss if I failed to thank my mentors from
Mississippi State University, Dr. Gary Bradshaw, Dr. Tracie Stewart, and Dr. Stephen
Paul.
I would like to thank those that assisted in maintaining my sanity over the years,
my lab-mates. I specifically want to acknowledge Lauren Tashman, Jason Ramrattan, Dr.
David Rodrick, Dr. Len Hill, Steve Lemmey, and Brian Peace, thanks.
Finally, I would like to thank all of my family for supporting me over the years.
The contributions here are too numerous to mention everyone individually but I would like
to specifically thank my parents, my brother Jeff and my sister Delores.
iii
TABLE OF CONTENTS
List of Tables.......................................................................................................
v
List of Figures .....................................................................................................
viii
Abstract ..............................................................................................................
x
INTRODUCTION...............................................................................................
1
THE PRESENT STUDY ....................................................................................
24
METHOD............................................................................................................
31
RESULTS............................................................................................................
34
THE ROLE OF ENVIRONMENTAL INFORMATION DURING TASK
PERFORMANCE ...............................................................................................
48
A MORE REFINED ANALYSIS.......................................................................
64
THE RELATIONSHIP BETWEEN PARTICIPANTS’ DELIBERATE
PRACTICE HISTORIES, SKILL-LEVEL, AND PERFORMANCE................
93
GENERAL DISCUSSION..................................................................................
107
APPENDICES.....................................................................................................
119
REFERENCES....................................................................................................
152
BIOGRAPHICAL SKETCH...............................................................................
157
iv
LIST OF TABLES
Table 1. Mean Variability (cm) for Each Marker by Skill Level and Marker
Item Loadings on the First Component during Spare Trials from Principal
Component Analysis ...........................................................................................
39
Table 2. Mean Variability (cm) for Each Marker by Skill Level and Marker
Item Loadings on the First Component during Strike Trials from Principal
Component Analysis ...........................................................................................
41
Table 3. Correlations between Execution Variability, Success Rate, and Skill
Level During Spare Trials ...................................................................................
45
Table 4. Correlations between Execution Variability, Success Rate, and Skill
Level During Spare Trials ...................................................................................
46
Table 5. Mean Variability (cm) for Each Marker by Skill Level and Marker
Item Loadings on the First and Second Components during Occluded Spare
Trials from Principal Component Analysis.........................................................
53
Table 6. Mean Variability (cm) for Each Marker by Skill Level and Marker
Item Loadings on the First Component during Occluded Strike Trials from
Principal Component Analysis............................................................................
57
Table 7. Correlations between Execution Variability, Success Rate, and Skill
Level During Occluded Spare Trials...................................................................
59
Table 8. Correlations between Execution Variability, Success Rate, and Skill
Level During Occluded Strike Trials .................................................................
60
Table 9. Mean Variability (cm) of Each Marker at the Three Events and
Marker Item Loadings Related to Variability Scores on a Single Factor during
Normal Spare Trials from Principal Component Analysis .................................
68
Table 10. Mean Variability (cm) of Each Marker at the Three Events and
Marker Item Loadings Related to Variability Scores on a Single Factor during
Occluded Spare Trials from Principal Component Analysis ..............................
70
Table 11. Mean Variability (cm) of Each Marker at the Three Events and
Marker Item Loadings Related to Variability Scores on a Single Factor during
Normal Strike Trials from Principal Component Analysis .................................
72
Table 12. Mean Variability (cm) of Each Marker at the Three Events and
Marker Item Loadings Related to Variability Scores on a Single Factor during
Occluded Strike Trials from Principal Component Analysis..............................
74
v
Table 13. Correlations between Execution Variability at the Three Events,
Success Rate, and Skill Level during Normal Spare Trials ................................
76
Table 14. Correlations between Execution Variability at the Three Events,
Success Rate, and Skill Level during Occluded Spare Trials .............................
77
Table 15. Correlations between Execution Variability at the Three Events,
Success Rate, and Skill Level during Normal Strike Trials ................................
77
Table 16. Correlations between Execution Variability at the Three Events,
Success Rate, and Skill Level during Occluded Spare Trials .............................
78
Table 17. Marker Item Loadings Related to Variability Scores on a Single
Factor during Spare Trials from Principal Component Analysis ........................
81
Table 18. Marker Item Loadings Related to Variability Scores on a Single
Factor during Strike Trials from Principal Component Analysis .......................
84
Table 19. The Frequency of the Amount of Pins Hit by Skilled Participants in
the Normal Compared to Occluded Strike Trials................................................
90
Table 20. Means from Skilled and Novice Participants’ Accumulated and
Current Practice Histories ...................................................................................
95
Table 21. Correlations between Accumulated and Current Practice Histories,
House Average, Skill Level and Success Rate during Normal Spare and Strike
Trials
..............................................................................................................
100
Table 22. Correlations between Accumulated and Current Practice Histories,
House Average, Skill Level and Success Rate during Occluded Spare and
Strike Trials .........................................................................................................
101
Table 23. Correlations between Accumulated and Current Practice Histories,
and Execution Variability (Component Score Loadings) on Normal Spare
Trials
..............................................................................................................
102
Table 24. Correlations between Accumulated and Current Practice Histories,
and Execution Variability (Component Score Loadings) on Normal Strike
Trials
..............................................................................................................
103
Table 25. Correlations between Accumulated and Current Practice Histories,
and Execution Variability (Component Score Loadings) on Occluded Spare
Trials
..............................................................................................................
104
vi
Table 26. Correlations between Accumulated and Current Practice Histories,
and Execution Variability (Component Score Loadings) on Occluded Strike
Trials
..............................................................................................................
vii
105
LIST OF FIGURES
Figure 1. Position of Motion Analysis Markers on Participants .........................
32
Figure 2. Mean Success Rate on Spare Trials as a Function of Skill Level........
35
Figure 3. Mean Success Rate on Strike Trials as a Function of Skill Level .......
36
Figure 4. Demonstration of Marker Tracking and Parameters of Task Area......
37
Figure 5. Example Calculation of Within-Participant Consistency Average for
Each of the Marker Positions on Spare Trials.....................................................
38
Figure 6. Scree Plot from the Principal Component Analysis of Markers
during Spare Conditions......................................................................................
40
Figure 7. Marker Traces Representing Five Spare Trials for a Skilled (top)
and a Novice (bottom) Participant .....................................................................
42
Figure 8. Scree Plot from the Principal Component Analysis of Markers
during Strike Conditions .....................................................................................
43
Figure 9. Marker Traces Representing Five Strike Trials for a Skilled (top)
and a Novice (bottom) Participant ......................................................................
44
Figure 10. Mean Success Rate on Spare Trials as a Function of Skill Level
and Visual Condition...........................................................................................
50
Figure 11. Mean Success Rate on Strike Trials as a Function of Skill Level
and Visual Condition...........................................................................................
51
Figure 12. Scree Plot from the Principal Component Analysis of Markers
during Occluded Spare Conditions .....................................................................
54
Figure 13. Marker Traces for a Skilled Participant during a Spare Trial during
Normal (top) and Occluded (bottom) Conditions ...............................................
55
Figure 14. Marker Traces for a Novice Participant during a Spare Trial during
Normal (top) and Occluded (bottom) Conditions ...............................................
56
Figure 15. Scree Plot from the Principal Component Analysis of Markers
during Occluded Strike Conditions .....................................................................
61
Figure 16. Marker Traces for a Skilled Participant during a Strike Trial during
Normal (top) and Occluded (bottom) Conditions ...............................................
62
viii
Figure 17. Marker Traces for a Novice Participant during a Strike Trial during
Normal (top) and Occluded (bottom) Conditions ...............................................
63
Figure 18. Representation of the Events Created by the Final Three Steps of
the Participant......................................................................................................
65
Figure 19. Representation of the Components Used in Calculating the
Variability Scores ................................................................................................
67
Figure 20. Scree Plot from the Principal Component Analysis of Variability
Scores during Normal Spare Conditions .............................................................
69
Figure 21. Scree Plot from the Principal Component Analysis of Variability
Scores during Occluded Spare Conditions..........................................................
71
Figure 22 Scree Plot from the Principal Component Analysis of Variability
Scores during Normal Strike Conditions ............................................................
73
Figure 23. Scree Plot from the Principal Component Analysis of Variability
Scores during Occluded Strike Conditions .........................................................
75
Figure 24. Scree Plot from the Principal Component Analysis of Variability
Scores during Combined Occluded and Normal Spare Trials ............................
80
Figure 25. Scree Plot from the Principal Component Analysis of Variability
Scores during Combined Occluded and Normal Spare Trials ............................
83
Figure 26. Example of a Shortened Approach in the Occluded Condition
(bottom) as Compared to the Normal Condition.................................................
86
ix
ABSTRACT
The acquisition of skilled performance via deliberate practice is posited to result in
consistency of task performance via refined representations of task requirements (Ericsson
& Lehmann, 1996). In the present study, skilled ten-pin bowling performance was captured
with standardized tasks and the execution variability of the bowling movements were
monitored and related to success rate. Two approaches are introduced, the expert
performance approach, which proposes cognitive mediation of task performance via
mental representations, and the ecological/dynamical systems approach, which proposes
that environmental information is the primary mediator of performance. Skilled and novice
participants were asked to bowl twenty times each under a combination of full- and
occluded- environmental (visual and auditory) conditions for spares and strikes. Skilled
participants were found to exhibit low levels of execution variability and high success rates
during normal spare and strike conditions. Analysis of the estimates of hours engaged in
deliberate practice activities support the development of reduced variability through
engagement in such activities. The role of environmental information on task performance
was directly tested during the occluded conditions. The findings indicate that skilled
participants maintain low execution variability during occluded conditions for both spare
and strike trials. However, skilled participants maintain their performance advantage for
success rate during occluded-environmental conditions for strike trials but not the spare
trials. Further investigation of the decrease in success rate despite low execution variability
revealed that participants were scaling down (shortening) the approach, likely due to a fear
of falling by crossing the foul line. The accumulated evidence of the present study supports
a mental representations approach but future research is proposed to buttress the current
findings.
x
CHAPTER ONE
GENERAL INTRODUCTION
Deliberate Practice, Mental Representations, and Skilled Performance
As a society, we are consistently impressed and often amazed by the top performers
within a given domain. For example, Tiger Woods, Michael Jordon, and Peyton Manning
perform (or have performed) athletic feats that, at times, appear to defy what is thought to
be possible. Moreover, a world-class musician can eloquently perform a difficult piece
seamlessly or a skilled actor can transport us to another place, both making it appear
effortless. In the sciences, the groundbreaking researcher contributing to the advancement
of a discipline invigorates his or her peers.
The high levels of performance demonstrated by these top performers were
traditionally explained as a function of superior innate abilities, a view largely attributed to
Sir Francis Galton (1869/1979). More recently, advocates of the expert performance
approach (Ericsson & Smith, 1991) provided an alternate account of exceptional
performance (see Ericsson, Nandagopal, & Roring, 2005, 2007). More specifically, the
expert performance approach provides a framework for objectively (thus scientifically)
examining superior performance by requiring that the designation of “expert” is restricted
to those consistently exhibiting high-level performance attainments and rejecting
designations of expertise based upon subjective criteria (Charness & Schultetus, 1999;
Ericsson & Lehmann, 1996). Moreover, representative tasks under controlled laboratory
conditions should be used to capture this high level performance and because of these
controlled conditions, direct comparisons of performance levels can be made and
designations of expert performers given. The framework of the expert performance
approach has been unable to identify innate capacities and abilities as being necessary to
reach expert performance with the exception of height and body size (Ericsson, 2007a,
2007b).
1
For instance, in a landmark study of expert performance by musicians, Ericsson,
Krampe, and Tesch-Römer (1993) identified a specific training methodology (designated
“deliberate practice”) to be the primary factor in the development of expert performance.
Activities designed by a teacher or coach to address specific weaknesses in one’s
performance and requiring effort beyond one’s current practice level comprise deliberate
practice. To achieve world-class performance levels, daily deliberate practice sustained for
at least ten years or 10,000 hours, is necessary. Moreover, world-class performers differ in
the amount of deliberate practice and dedication to practice than less accomplished
performers.
A consequence of expert performers engaging in extended deliberate practice activities
are superior refined representations of task performance, and resultant enhanced skills
(e.g., planning, increased motor control). Thus, expert performers develop superior
performance capabilities through consistent application of precise training methods.
Properly administered deliberate practice leads to improvements in planning, analysis,
problem-solving, and motor control via refined representations of the task domain
(Ericsson & Lehmann, 1996). These representations include representations of: a) the
desired performance goal, b) how to execute the performance, and c) the monitoring of
one’s performance (Ericsson, 2002). According to Ericsson, a performer begins with the
desired performance goal, uses his or her representation of how to execute performance,
uses the representation to monitor performance and makes a new performance goal if
performance is not satisfactory. This reiterative process allows for a consistency of task
performance that may be absent in less-skilled performers. For example, expert performers
in music are able to consistently reproduce musical performances and expert performers in
chess are consistently capable of selecting the best available move (Ericsson, 2002;
Ericsson & Lehmann, 1996).
Thus, is consistency of task performance a characteristic of skilled performance in tenpin bowling? For tasks involving motor performance, consistency of task performance can
be considered on at least three levels, a) an individual’s reproducibility of the bodily
motions associated with the actions of a task over successive trials, execution variability
(Müller & Sternad, 2004), b) an individual’s ability to minimize the variance of the final
2
position, result variability (Müller & Sternad, 2004), or c) the rate of achieving the task
goal, success rate.
Two of these components, execution variability and success rate, are considered in the
present study. Moreover, two approaches to understanding task performance, in general,
and skilled performance, in particular, will be introduced. More specifically, the expert
performance approach and the ecological/dynamical systems approach will both be
introduced along with each approach’s specific claims with regard to consistency during
task performance.
Consistency of Task Performance
As mentioned above, tasks involving motor performance, consistency of task
performance can be considered on at least three levels, execution variability, result
variability, and success rate. For example, in a game of darts, a participant may exhibit
little variability in the positions and path of motion of his or her arm (low execution
variability) over ten trials but fail to hit the target once (low success rate). However, if the
dart strikes the dart-board in the same position every time then result variability will be
low despite the low success rate. Conversely, a participant may exhibit high execution
variability by not incorporating a consistent arm position or motion but still achieve a high
success rate by achieving the task goal (i.e., hitting the bull’s eye) which, in this example,
requires somewhat low result variability. Moreover, a high success rate is a necessary
component of expert performance since being highly consistent dart-thrower (in execution
variability terms) would be of little use if you consistently miss the preferred target and
thus fail to score points.
The focus of the present study is limited to individual self-paced tasks for which the
nature of the tasks would not lead to a performance disadvantage by reproducing the
detailed execution of the task from one attempt to the next (e.g., ten-pin bowling). This is
in contrast to tasks such as a jump shot in basketball or returning a serve in tennis for
which reproducing the execution in the same manner would be a disadvantage, as the jump
shot would likely be blocked by an opponent or the swing of the racquet would miss the
tennis serve. The two approaches will now be introduced and discussed regarding the role
of consistency during task performance.
3
The Expert Performance Approach
The expert-performance approach proposes that representations of task performance
take the form of internalized mental representations (Ericsson, 1998; see also Hill, 2003).
These internalized representations allow for the consistency of task performance, as in the
above mentioned examples of music and chess. Moreover, the enhanced skills, such as
increased motor control, derived from refined mental representations afford skilled
performers the capacity to accurately reproduce performances. For instance, a golfer’s
swing has been found to be highly reproducible at high levels of competition (Ericsson,
2002). It is this consistency of performance acquired through extended deliberate practice
that distinguishes skilled performers in golf; it is the necessary component for skilled golf
performance.
Mental representations have been proposed as an explanation of expert performance for
tasks such as chess, musical performance, medical diagnoses, physics, sports, and horsebetting, to name a few (see Ericsson & Lehmann, 1996 for a review). Lehmann (1997)
articulated the posited use of mental representations by expert musicians. Mental
representations allowing skilled performance are comprised of representations of: a) the
desired performance goal, b) production aspects of the task, and c) representations of
actual task performance. Moreover, Lehmann specified the representations necessary for
expert sight reading performance as compared to expert soloist performances. Specifically,
representations for sight-readers include what is possible to play under the circumstances
(performance goal), representations for execution are posited to include anticipated
musical pieces (following the soloist’s lead), and representations of performance reflect the
degree of timing with the soloist. However, the representations of an expert soloist reflect
the task goal of matching the current execution with rehearsed performances. Thus the
production representation of expert soloist performance supports accurate recall and
control of rehearsed performances and the representation of actual performance must allow
minute adjustments to the unforeseen conditions of the live performance (e.g., instrument
anomalies).
Moreover, expert sight-readers were significantly more accurate (correct pitch and
timing) than expert soloists accompanying (sight-reading) a tape-recorded soloist in an
experimentally controlled setting (Lehmann & Ericsson, 1997; as cited in Lehmann, 1997),
4
during the first un-rehearsed test. The expert soloists also improved significantly more than
the sight-readers by the fourth test, resulting in an overall significant interaction. The
importance of these findings is that the sight-readers appeared to develop a sufficient
mental representation for sight reading performance. Furthermore, the improvement
exhibited by the soloists is indicative of the ability to develop mechanisms (new mental
representations) allowing improved sight-reading performance.
Following the four tests, three component tasks were administered: a) a cued recall task
of reproducing parts of the sight reading task from memory, b) an improvisation task of
creating a probable sequence of notes, and c) a test of tactile orientation during which the
musicians were required to make large leaps while goggles prevented them from looking at
the keyboard. There were no differences between the two groups on these component
tasks. Thus, mental representations allow for control of task performance and the
representations are developed through engagement in extensive deliberate practice
activities.
The Ecological/Dynamical Systems Approach
An alternative approach to the expert performance approach is the
ecological/dynamical systems approach, advocates of which argue that performance does
not rely upon an internalized representation but that task performance is reliant only upon
motor responses to environmental information (Gibson 1954, 1979; Nakayama, 1994), that
motor system is a self-organizing entity for which systematic constraints (i.e., degrees of
freedom) are also self generated (Frank, Peper, Daffertshofer, & Beek, 2006), and that the
system automatically makes necessary adjustments to achieve the appropriate state or
regain the previous state (Van Gelder and Port 1995, as cited in Treur 2005).
Müller and Sternad (2004) noted the historical investigations of tasks such as
hammering an anvil (e.g., Bernstein, 1935; as cited in Müller & Sternad, 2004) in which
hitting the same spot on the anvil (low result variability and high success rate) was
achieved with different multi-joint arm trajectories (high execution variability) and
investigated the relationship between execution variability and result variability in a virtual
skittles task. Skittles is a game in which a player puts a ball suspended by a string attached
to a post into motion with the goal of knocking over an object (the skittle) opposite the post
with respect to the player. Because of the location of the skittle relative to the player, the
5
path of motion enacted upon the ball by the player determines the likelihood of
successfully knocking over the skittle. The authors posited that execution variability would
never reach zero and that this inherent level of variability could play a functional role,
allowing an exploration of the tolerable performance ranges of the task space. In other
words, deviations of execution are corrected only if the deviations are likely to interfere
with achieving the task goal. However, Müller and Sternad proposed that both execution
and result variability will decrease with practice via a reduction of stochastic noise,
exploitation of task tolerance, and covariation of central variables. This covariation can be
described as a releasing of the degrees of freedom which may have been enforced had the
components been considered individually. For example, the angle of the hand during a dart
release may compensate for the particular angle of the elbow at the time of the release,
possibly allowing result variability to remain low despite a higher than expected execution
variability. Consistent with the authors’ predictions, the participants’ accuracy on the
virtual skittles task increased with practice and the increased accuracy was primarily
attributed to the reduction of stochastic noise and exploitation of task tolerance, but also
(modestly) to covariation of central variables. While interesting, Müller and Sternad’s
(2004) findings are limited on the basis that the researchers employed an artificial task, a
virtual reality version of a skittles game.
Traditionally, execution variability was considered as systematic noise whereas some
recent advocates of a dynamical approach view variability (i.e., increased degrees of
freedom for body motion) as a component of skill (Newell & Corcos, 1993). These
particular ecological/dynamical systems advocates argue that execution variability is of
little importance provided that the task-goal is achieved (high success rate) and that the
performance of skilled performers could be hindered by attempting to achieve task
performance with little execution variability arguing that system generated systemic
adjustments may be needed to achieve the task-goal (Newell, van Emmerick, & Sprague,
1993). Because of this system-generated self organization, covariation of component
processes during task completion can be described as a releasing of the degrees of freedom
which may have been enforced had the components been considered individually (Müller
& Sternad, 2004). In the aforementioned example, the angle of the hand during a dart
release may compensate for the particular angle of the elbow at the time of the release. It is
6
this compensatory action of the two components covarying that allows result variability to
remain low despite a higher than expected execution variability. However, other advocates
of a dynamical/ecological systems approach argue that task performance on well-rehearsed
tasks, particularly on tasks for which skill has developed, will likely exhibit low degrees of
execution and result variability (Williams, David, Williams, 1999). These low degrees of
variability are presumed to reflect an efficient system, as very little system organization is
required.
The Role of Environmental Information during Task Performance
Despite this potential agreement that skilled performance on well-rehearsed tasks may
exhibit low degrees of execution variability, the two approaches are in more direct
disagreement regarding the role of environmental information in task performance. More
specifically, proponents of the ecological/dynamical systems approach would argue that
ongoing environmental information (e.g., visual input) is necessary for task performance,
with many positing a direct perception-action link (e.g., Bootsma & van Wieringen, 1990).
Conversely, proponents of a mental representations approach argue that task performance
can be accomplished in the absence of environmental information (e.g., visual input). For
example, in Hill’s (1999; as cited in Ericsson, 2002) comparison of skilled and novice
golfers’ ability to generate appropriate changes to hypothetical conditions (i.e., no actual
visual appraisal), there was a significant expert advantage for the most complex
hypothetical situations. Additional evidence for mental representations will be presented
below in a dedicated section. However, a straightforward way to test the assumptions of a
mental representations approach is to remove external information which imposes reliance
on other sources of information for task performance. Thus, can high performance levels
be maintained following the removal of environmental information?
Ecological/Dynamical Systems Approach
Proponents of an ecological/dynamical systems approach propose that the motor
system is self-organizing entity for which systematic constraints (i.e., degrees of freedom)
are also self generated (Frank, Peper, Daffertshofer, & Beek, 2006). Thus, as forces
applied at a given time will potentially enact changes to the current state, the system
automatically makes necessary adjustments to achieve the appropriate state or regain the
previous state (Van Gelder and Port 1995, as cited in Treur 2005).
7
Moreover, supporters of the approach argue that task performance does not require
internalized representations but that perception of environmental information generates
appropriate motor responses (Gibson 1954, 1979; Nakayama, 1994). The rapidity of
retuning actions (105 to 122 ms) of skilled performers in table tennis (Bootsma & van
Wieringen, 1990) for example, were seen as evidence that motor responses were occurring
within a time-frame for which internal cognitive processing (at least representational-like
processing) could not occur. However, the superior ability of skilled performers, as
compared to novices, to anticipate opponents’ actions in domain tasks such as returning
tennis serves (e.g., Williams, Ward, Knowles, & Smeeton, 2002) can explain this rapidity
of action (see also Ericsson & Lehmann, 1996; Hill, 2003).
In addition to the proposed insufficient time-frame for internal cognitive processing to
occur (e.g., Bootsma & van Wieringen, 1990), the findings of high degrees of variability
for some performances were proposed as arguing for the influence of environmental
information in several ways, a) environmental information (e.g., vision) is required for
successful task performance (e.g., long jump approach; Lee, Lishman, & Thomson, 1982),
b) task performance is modified based upon environmental information (e.g., Lee et al.,
1982), and c) execution variability at intermediate points of skilled performance is
expected (based upon the proposed self-correcting nature of the system) and variability
reduces near the end of task performance, e.g., result variability (e.g., Bootsma & van
Wieringen, 1990).
Example Ecological/Dynamical Accounts of Performance
In this section, investigations of long jumping (Berg, Wade, & Greer, 1994; Lee et al.,
1982), table-tennis (Bootsma & van Wieringen, 1990), and cascade juggling (Beek &
Turvey, 1992; Huys, Daffertshofer, & Beek, 2003; Post, Daffertshofer, & Beek, 2000; Van
Santvoord & Beek, 1996) will be presented as examples of claims made by adherents of
the ecological/dynamical systems approach.
Long-jumping. Gait during the long-jump approach was proposed to be visually
regulated in studies of skilled long jumpers (Lee, Lishman, & Thomson, 1982). Longjumpers were filmed sprinting to the launch pad during practice long-jump approaches.
Long jumpers started the approach in a highly stereotyped manner and gait became more
variable as the launch pad was approached. Moreover, the higher the variability in the early
8
stages of the approach, the more variable was the gait during the final few steps as the long
jumper approached the launching point. The authors interpreted these findings as evidence
for visual regulation of the long-jump approach, particularly among skilled long-jumpers.
Berg, Wade, and Greer (1994) expanded the findings of Lee et al. (1982) by including
both skilled and novice long-jumpers during competitive events. Berg et al. replicated the
previous findings with regard to skilled long jumpers but found similar effects with novice
long-jumpers. The finding that both skilled and novice long-jumpers modify gait during a
long-jump approach in a similar manner was interpreted as indicative of a general effect of
visual information on gait adjustment rather than a component of skill.
An expert performance approach counter: long-jumpers. It should be noted that while
visual regulation was suggested as being responsible for the observed adjustments made
during task performance in the studies of long-jumpers reported above (Berg et al., 1994;
Lee et al. 1982), mental representations of performance serving to regulate performance
could not be ruled out. The authors of both studies sought only to dispel the idea that a
long-jump approach was completely stereotyped. The authors did provide evidence that the
long-jump approach was, in fact, somewhat variable as the athlete approaches the launch
pad for both skilled (Lee et al., 1982) and novice (Berg et al., 1994) long-jumpers.
However, given that the outcomes of the long-jumps were not stated in either the Berg et
al. (1994) or Lee et al. (1982) studies, the variability of gait during the long-jump approach
may not be the most informative task feature for determining expert performance in longjumping. For instance, gait variability may interact with attained speed or some other
variable to distinguish skilled and less-skilled performers.
Finally, we propose the visual information may have served only to provide
information to the athlete that was then compared with an internalized representation.
Moreover, a skilled athlete would likely have the capability to fill in missing
environmental information, if such information was removed during the long-jump
approach, allowing successful long-jump completion. Similar, to the anticipatory skills of
skilled athletes described above, internalized representations of task performance would
allow maintenance of high performance via anticipation of the future state based upon
considerations of the current state or past state (post-occlusion). Thus, the additional
factors mentioned above (e.g., speed) can be actively and internally monitored by the
9
skilled performer leading to a more successful outcome (a longer jump), even when
environmental information is removed during task performance.
Whereas, Berg et al. (1994) interpreted the lack of a skill difference in the visual
regulation of a long-jump approach as evidence that visual regulation is not a skill but a
natural task-component, we argue that the skill would lie in the successful regulation of the
long-jump approach and a successful jump. Moreover, although vision may have served a
role in long-jump approach performance, the necessity for vision to accomplish the task
could not be fully addressed by the Berg et al. and Lee et al. (1982) studies as vision was
never occluded. This is somewhat surprising given the reported debate among track-andfield coaches advocating (although rarely; McNab, 1972, as cited in Berg, et al.) and
opposing (Teel, 1981, as cited in Berg, et al.) coaching the use of vision during the long
jump approach.
Table-tennis. The two studies investigating variability in long jump approach (Berg et
al., 1994; Lee et al. 1982) found greater execution variability in gait as the performer
neared the target (i.e., launch pad) but low result variability (defined as the point of contact
with the launch pad). In an investigation of variability of table-tennis strikes (Bootsma &
van Wieringen, 1990), high execution variability was found during the early stages of
performance, which was likely due to the nature of the task. This initial high level of
execution variability resulted in a “funneling” effect, as the findings again indicated low
result variability (defined as hand position during ball-racquet contact). Specifically, tabletennis players were instructed to return table-tennis balls with an attacking forehand as
quickly and accurately as possible. A ball projector served table-tennis balls to a former
table-tennis champion who then returned the balls to the participants. A circular target
served as a measure of accuracy. The task was filmed and movement was analyzed.
Analysis of the direction of the travel of the bat indicated considerable variability of bat
direction 200 ms prior to bat-to-ball contact, progressively less variability beginning
around 75 ms prior to bat-to-ball contact, and culminating with virtually no variability
from 50 ms prior to bat-to-ball contact until contact. While acknowledging the difficulty in
claiming continuous perceptual guidance based upon their findings, Bootsma and van
Wieringen (1990) concluded that perception-action requirements are met simultaneously
given the small increments of time allowed for performance.
10
An expert performance approach counter: table-tennis. Bootsma and van Wieringen
(1990) concluded that perception-action requirements are met simultaneously given that
the small increments of time allowed for performance would not allow sufficient time for
internalized cognitive processing. However, as stated above, the anticipatory capabilities of
skilled performers (e.g., Williams, Ward, Knowles, & Smeeton, 2002) are sufficient for
allowing such performance.
With regard to expert performer identification, the authors selected Dutch players
competing at the highest levels. However, since these players were never compared to lessskilled players, an objective performance comparison could not be made. Related to this
issue, because individual differences were not established by performance comparisons,
the implemented task of the study may not be indicative of skilled performance (i.e., not a
representative task). Thus, the findings do not allow one to make claims regarding skilled
performance. Although the return times of the participants were rapid, skilled performance
may be associated with other skills (e.g., strategic ball placement). As a final note, given
the potential for variability in the delivery of the ball to the participant (it was delivered by
a table-tennis player) the assumptions that can be derived from the variability of the
participant prior to ball contact are limited. Specifically, the early execution variability
exhibited by the participants may reflect the slight variations associated with the variability
of ball delivery by a human.
Cascade juggling. Peter Beek and his colleagues (Beek & Turvey, 1992; Huys,
Daffertshofer, & Beek, 2003; Post, Daffertshofer, & Beek, 2000; Van Santvoord & Beek,
1996) have actively promoted an ecological/dynamical systems approach both within and
outside cognitive psychology. Cascade juggling was extensively studied due to the taskrequirement constraints that allow mathematical formulation of the task. Beek and Turvey
(1992) mathematically specified the temporal and spatial constraints of cascade juggling.
Given the severe task constraints arising from cascade juggling (e.g., avoiding object
collisions), the authors predicted a consistent primary ratio of ¾. The weight, type, and
number of objects were experimentally manipulated to test this hypothesis. The results
provided limited support for the authors’ predictions. More specifically, when three balls
were juggled, frequency locks near the predicted k value of ¾ were observed. This ratio
was found despite a change in the weight of the juggled objects. However, when scarves or
11
more than three balls were juggled, varied k values were observed. Relevant to the present
studies, when task requirements remain relatively constant (except for the weight of the
balls) the skilled juggler was able to adjust accordingly and maintain performance. Beek
and Turvey interpret the findings as ecological/dynamical phenomena; however, we again
argue that mental representations of performance cannot be ruled out.
Van Santvoord and Beek (1996) focused on expert and intermediate cascade jugglers
and measured the spatial-temporal variability of performer throwing, performer catching
and ball path. Specifically, the researchers provided temporal constraints in the form of an
externally provided beat and spatial constraints in the form of a pre-determined target
height. There was no overall effect of type of task constraint on variability. In general, the
release during throwing exhibited the least amount of variability followed by the height of
the ball path (zenith) and catching exhibited the greatest amount of variability. Temporally,
ball flight intervals exhibited the least amount of variability overall, followed by loaded
hand and empty hand, which exhibited the greatest amount of variability. However, when
considered individually, ball path was less variable in the spatially constrained condition
when compared to the temporally constrained condition. With regard to the performers’
hands, the throwing component was less variable than the catching component for both
conditions. The authors concluded that jugglers seek to throw an object to a predetermined height and use a consistent throwing movement to achieve the desired specified
height. Furthermore, Van Santvoord and Beek claimed the observed increased variability
associated with the catching motion is indicative of adjustments made during juggling
performance to maintain the more consistent variables (i.e., flight path and throwing
motion). Throwing the ball to an adequate height allows expert and intermediate jugglers
to maintain performance consistency by allowing necessary adjustments to be enacted.
Also relevant to the present studies, the authors claimed that the findings were evidence
that “the temporal properties of coordinated movements are not prescribed a priori by some
internal entity but emerge from the dynamics of the act as defined over the actorenvironment system” (Van Santvoord & Beek, 1996, pp. 148-149).
An additional study was conducted by Post et al. (2000) on the control structure of
cascade juggling. The researchers manipulated juggling speed by having participants
juggle at a normal speed based on the participant’s preference and at an accelerated rate.
12
The findings indicated that despite the juggling condition, catches were used to maintain
timing of the act. These findings lend support to previous findings of Van Santvoord and
Beek (1996) indicating that during the act of juggling, catches exhibit the greatest degree
of variability. Specifically, the variability exhibited in the catch during cascade juggling
allows the remaining components (e.g., ball flight) to remain less variable.
Again proceeding from an ecological/dynamical systems approach, Huys et al. (2003)
investigated novices learning to cascade juggle. The authors were interested in identifying
the structure of the subsystems which allows one to juggle and how the subsystems were
organized as learning advanced. A premise of the investigation was that for every complex
task there were essential components and non-essential components (e.g., Scholz et al,
2000). As the essential components of cascade juggling have been established (e.g., Beek
& Turvey, 1992), the authors sought to investigate the progression of learning as related to
these principal components. Thus, six novices practiced daily (one hour) for twenty days
and juggling performance was assessed on seven of these days. Although measures, such
as body sway and respiration, were taken in order to determine the coupling of these
measures with juggling components, no evidence of coupling was found. More
importantly, spatio-temporal variability of the juggling patterns was found to decrease with
practice. The authors interpreted the findings as an improved organization and thus a
resulting performance of a dynamical system.
However, we argue that the findings of Huys et al. (2003) can be interpreted as
evidence for a developing mental representation. More specifically, the decreased
variability resulted from the initial refinements in the mental representation. Similarly, we
would argue that an expert juggler has a refined mental representation sufficient to
accurately perform tasks, even when the demands have been modified (e.g., Post et al.
2000). However, since verbal reports were not taken during the acquisition of juggling skill
(Huys et al.) or during task modification (e.g., Post et al), direct evidence of such
representations was not provided.
An expert performance approach counter: cascade juggling. The findings from the
juggling studies described above were proposed as evidence of an ecological/dynamical
systems approach. As the representative task of juggling is juggling, this component of the
expert performance approach was met. Furthermore, in a comparison of spatial and
13
temporal variability between expert and intermediate jugglers (Van Santvoord & Beek,
1996), performance classification (i.e., skill level) was determined based upon performance
comparisons (the ability to juggle more than 5 balls versus 3 balls or less). The findings
indicated that the expert jugglers were less variable than novices. Furthermore, the primary
area of adjustment for expert jugglers was identified. Although Van Santvoord and Beek,
(1996) argued that this consistent point of variability was indicative of an
ecological/dynamical approach, we argue that a mental representations approach could not
be ruled out. Some degree of variability would be expected during any task performance.
Additionally, Huys at al. (2003) found that novice jugglers exhibited progressively less
variability following twenty days of training when sampled seven times during training, a
finding possibly indicative of a developing mental representation.
Section Summary
In summary, proponents of an ecological/dynamical systems approach would argue
that performance does not require internalized representations but that perception of
environmental information generates appropriate motor responses. Studies of long-jumping
(Berg et al., 1994; Lee et al., 1982), table-tennis (Bootsma & van Wieringen, 1990), and
cascade juggling (Beek & Turvey, 1992; Huys et al., 2003; Post et al., 2000; Van
Santvoord & Beek, 1996) were presented and the authors’ interpretations with regard to an
ecological/dynamical systems approach were discussed. Vision was purported to mediate
performance in the studies of long-jumping (Berg et al., 1994; Lee et al., 1982) and tabletennis (Bootsma & van Wieringen, 1990). Moreover, ongoing visual regulation of action
was posited as the source of performance-modifying corrections. Although some degree of
visual information (or other perceptual cues) is required for tasks such as table-tennis, an
internalized representation cannot be ruled out based upon the findings described in the
current section. The studies investigating cascade juggling (e.g., Beek & Turvey, 1992)
provided an accurate mathematical formulation of the task of cascade juggling.
Furthermore, the primary source of variability during cascade juggling was identified (Post
et al., 2000; Van Santvoord & Beek, 1996) and taken as evidence of an ecological/dynamic
system. However, Huys et al. (2003) found that novice jugglers exhibited progressively
less variability following twenty days of training when sampled seven times during
training. Thus, can the Huys et al. finding be taken as evidence for a developing mental
14
representation? The mental representations approach will now be introduced in the
following section.
Mental Representations Approach
The basic tenet of the ecological/dynamical systems approach is an ongoing perceptual
monitoring (e.g., visual monitoring) of task performance and appropriate adjustments
based upon this information without representational processing. In contrast, the basic
tenet of mental representations approach is that internalized mental representations allow
for performance in general and skilled performance in particular (Ericsson, 1998; see also
Ericsson & Lehmann, 1996). Granted, for tasks such as catching a ball, visual information
will be useful. For example, it would be difficult for one to catch a ball without any
perceptual cues as to the direction from which it is traveling, speed at which it is traveling,
and the size of the ball. However, the basic argument from a mental representations
perspective is that this perceptual information derived from such a task is processed via a
mental representation. Thus, with regard to the studies of long-jumpers and their
variability in long jump approach (Berg et al., 1994; Lee et al. 1982) mentioned above, we
argue performance is monitored via a mental representation of actual performance rather
than being a direct link from perception to motor response (action).
Many of the studies described in the previous section, for which the researchers
advocated an ecological/dynamical systems approach (i.e., unmediated perception-action
coupling), used tasks for which some degree of vision may be necessary, such as returning
table tennis balls (e.g., Bootsma et al., 1990). Physical tasks with self-paced requirements,
such as dancing, golf-putting, maintaining form in martial-arts, or ten-pin bowling, offer a
potentially more fruitful opportunity to investigate internalized mental representations.
Such tasks allow investigations of tasks with little or no external input (i.e., dancing) and
tasks with relatively high external demands, such as golf-putting (e.g., weather, terrain,
etc.). Moreover, the self-paced characteristics of such tasks allow one to investigate the
consistency of performance, such as success rate or degree of execution variability.
One means by which to test a mental representations approach, given the proposed
reliance upon internalized information, is to make external environmental information
unavailable. Consequently, this manipulation serves the dual purpose of simultaneously
providing evidence arguing for a mental representations approach and arguing against an
15
ecological/dynamical systems approach, if high levels of performance are maintained in
the absence of environmental information.
Example Visual Occlusion Studies of Postural and Balance Tasks
With the removal of all environmental information (e.g., auditory and visual),
kinesthetic information is likely the only remaining potential source of feedback and it can
be assumed to be part of the representations of production and actual performance
monitoring. In other words, a mental representation of production would include
information such as the desired position of the limbs which can be compared to the mental
representation monitoring actual performance (and actual limb positions). The role of
kinesthetic and similar types of feedback for maintaining postural control in the absence of
visual information has already been investigated in areas of performance such as judo
(Paillard, Costes-Salon, Lafont, & Dupui, 2002), triathlons (Nagy et al., 2004), dancing
(Golomer, Cremieux, Dupui, Isableu, & Ohlmann, 1999), gymnastics (Vuillerme et al.,
2001), and soccer (Paillard et al., 2006), with mixed results. For example, Paillard et al.
(2002) compared the ability of regional and national/international-level judo athletes to
maintain static balance (flat surface and arms to side) under conditions of vision and no
vision. The findings indicated a significant vision by skill interaction on some variables
related to balance (e.g., average position in y plane). More specifically, the
national/international judiosts were more reliant upon vision than regional level judo
athletes for variables for which there was a significant interaction. Paillard et al. (2002)
concluded that rising competition levels increase reliance upon visual information for
posturokinetic activity.
Nagy et al., (2004) compared the ability of ironmen tri-athletes and a control group of
firefighters (the firefighters exercised a minimum of three times a week) to maintain static
postural control under visual and blind (no vision) conditions. Contrary to the findings of
Paillard et al. (2002), while no difference between the groups was found for the visual
conditions, the ironmen tri-athletes exhibited greater postural control in the blind
conditions. Similarly, skilled dancers (Golomer et al., 1999), skilled gymnasts (Vuillerme
et al., 2001), and skilled soccer players (Paillard et al., 2006) were less reliant upon vision
than novices for postural control in non-static balancing tasks. Thus, it appears that with
16
regard to postural control, skilled performers have developed enhanced abilities to make
use of sensory information other than vision.
An Expert Performance Approach View of Visual Occlusion Studies of Postural and
Balance Tasks
The studies mentioned above investigating the role of visual information in postural
and balancing tasks (e.g., Paillard et al., 2006) implemented primarily artificial tasks (e.g.,
balancing tasks). Therefore, the applicability of the findings is limited and a likely cause of
the discrepancies regarding skilled reliance on vision. For instance, Paillard et al.’s finding
of higher level judo athletes being more reliant upon vision to maintain static posture is not
informative with regard to actual judo performance or a representative task (e.g.,
performing judo positions). Moreover, an investigation of the effect of vision on postural
control of ballet dancers (Hugel, Cadopi, Kohler, & Perrin, 1999) indicates that the degree
to which the task is representative of the domain strongly influences the findings.
Specifically, Hugel et al. (1999) found that the removal of vision was equally disruptive of
expert and novice dancers during a static-balance task but experts performed equally well
under sighted and no-vision conditions during a releve on pointe task.
Example Visual Occlusion Studies of Simulated Task Performance
Visual occlusion paradigms of more representative (i.e., ecologically valid) self-paced
tasks have already been implemented by researchers investigating the specificity of
learning hypothesis (e.g., Bennett & Davids, 1995) and by researchers specifically
investigating the role of vision in skilled performance (Wannebo & Reeve, 1984). The
studies described in the current section vary in the adherence to the expert performance
approach (Ericsson & Smith, 1991). Examples of these studies are nonetheless useful for
addressing mental representations and non-adherence to the expert performance approach
will subsequently be addressed.
Power-lift squat. Bennet and Davids (1995) investigated the power-lift squat
performance of intermediate power-lifters under full-, ambient-, and no-vision conditions
and later compared skilled versus less-skilled power-lifters under the same three
conditions. Competitively, in order for a power-lift squat to be “good” (i.e., points
awarded), the top surface of the thighs at the hip must be just lower than the top surface of
the knee. Thus, the self-pacing nature and technical requirements of power-lift squats
17
allowed the role of vision and kinesthetic sensations to be experimentally separated. A
scoring system, based on competitive power-lifting scoring, was devised for the two
experiments. The findings of the first experiment indicated progressively worse
performance scores of intermediate power-lifters corresponding with the amount of visual
information that had been removed. More specifically, performance was greatest in the
full-vision condition, dropped in the ambient vision condition, and was lowest in the no
vision condition. Thus, vision was necessary for intermediate power-lifters to maintain
performance. However, when skilled power-lifters were included in the second
experiment, high levels of performance were maintained for all vision conditions.
The goal of Bennet and Davids (1995) was to test the specificity of learning hypothesis
but the findings are also relevant to the present studies. Ten-pin bowling and the powerlifting squat are similar in that both tasks have self-paced performance requirements.
Furthermore, the findings that skilled power-lifters maintain high levels of performance
despite the removal of visual information seems to indicate that skilled power-lifters rely
upon some form of internalized feedback, likely kinesthetic sensations. Moreover, we
propose that a mental representation of the desired endpoint (i.e., goal state) serves to
monitor performance and ultimately allows skilled power-lifters to maintain superior levels
of performance via comparisons of mental representations of production and mental
representations of actual performance.
The investigation by Bennet and Davids (1995) closely adhered to the expert
performance approach. The expert performers were identified from national competitions
as top-five competitors, and the intermediates were from a local weight-lifting club. More
importantly, the participants were asked to perform and were measured on tasks one would
be asked to perform during actual competitions. The participants were measured based
upon specific performance criteria on a numbered scoring system intended to reflect the
scoring system of an actual competition, the only potential limitation from the perspective
of an advocate of the expert performance approach. However, performance differences
were observed between the two groups and performance levels were maintained by the
expert performers when vision was removed.
Balance-beam gymnastics. Robertson and Elliot (1996) investigated the role of vision
during the task of walking across a balance beam in skilled and novice gymnasts. The
18
authors measured the mean movement time across the beam, the number of steps across
the beam, and the total number of form errors during full-, no-, or displaced (to left or
right) visual conditions. The overall findings indicated that skilled gymnasts performed
equally well in full- and no-vision conditions with an increase in form errors during the novision condition (experts still exhibited significantly fewer form errors than novices).
Robertson and Elliot suggested that the experts were able to make necessary adjustments
based upon kinesthetic information, vestibular information, or a combination of the two.
Moreover, the authors propose that the ability to rapidly detect and correct movements is a
component of expertise. Implicitly, this movement-correction ability stems from
monitoring via internalized resources.
Although the findings of Robertson and Elliot (1996) offer tentative support for an
internalized source of performance monitoring and correction, the task implemented for the
study only weakly adheres to the expert performance approach. Walking across a balance
beam is not likely a representative task (a task capturing the essence of a domain) for
balance-beam gymnasts. A study implementing a more representative task is needed to
buttress the findings of Robertson and Elliot regarding the role of vision in skilled balancebeam performance.
Somersaulting gymnastics. Bardy and Laurent (1998) investigated the role of vision in
maintaining body orientation during a backward somersault by three “expert” and two
novice gymnasts (Experiment 2). The researchers measured overall body orientation, angle
of the thigh-leg combination, and angle of the trunk-thigh combination as the participant
approached landing. The findings indicated that novices exhibited more consistent mean
standard deviations of body orientation in space than “experts.” In other words the novices
were more consistent across vision and no vision conditions with regard to body
orientation.
Once again, with regard to the expert performance approach Bardy and Laurent’s
(1998) study has several limitations. Although the experts from the study were reported to
have competed internationally, no other criteria for designation as an expert was given.
The novices were described has having “recently learned” the somersault and to compete
in non-sanctioned competitions on occasion. Furthermore, only a portion of the successful
trials were analyzed and the number of successful and failed somersaults was reported.
19
Three experts and two novices participated, and on average the experts achieved 22
successful somersaults and 16.7 failed somersaults compared to the novices’ 19.5
successful and 16.5 failed somersaults. Thus, performance differences were not established
based upon these performance measures and the results are not very useful for making
expert-novice comparisons,
Golf putting. Several studies have investigated the role of vision for the task of golfputting (e.g., Aksamit & Husak, 1983; Meacci & Pastore, 1995; Wannebo & Reeve, 1984)
with mixed results. For instance, Aksamit and Husak (1983) found no difference in putting
performance for novice golfers between full- and no-vision conditions. However, Wannebo
and Reeve (1984) argued that because the participants in Aksamit and Husak’s study were
novice non-golfers, that removal of visual information may be an effective teaching tool (a
hypothesis supported by the findings of Meacci & Pastore, 1995) but that relevant visual
cues are a requirement of skilled golfing performance. To test their hypothesis, Wannebo
and Reeve compared golfers with more than three years of experience to golfers with less
than six months of experience under conditions of no visual cues, relevant visual
information, and irrelevant visual information (focusing on tape in the form of an “x”)
under distances of 5 ft and 10 ft. The findings indicated that golfers with more experience
were more accurate overall than the less experienced golfers and accuracy decreased as
distance increased. Moreover, accuracy was greatest for each experience level in relevant
visual information (i.e., full-vision) conditions than in irrelevant visual information and novision conditions, which were not significantly different.
Because Wannebo and Reeve (1984) relied only upon years of experience as a basis for
expert designation, the findings may not be indicative of the visual requirements of skilled
putting performance. Hill (1999) found that expert golfers exhibited greater performance
than novice golfers on complex putting situations. Moreover, expert golfers gave more
consideration to, and recalled more factors affecting performance during the representative
task. Thus, it is feasible that expert performers identified by objective performance
measures may be less susceptible to the removal of vision than the golfers studied by
Wannebo and Reeve.
Basketball shooting. Vickers (1996) investigated the role of vision in free-throw
shooting by elite female basketball team members (Canadian gold-and silver-medal
20
winners). Based upon free throw shooting percentages achieved during a recently ended
season (a 68-game winning streak), the players were classified as experts (≥75%) and nearexperts (<65%). These accuracy scores were later combined with accuracy scores achieved
in the experimental setting (mean 82% for experts, 71% near-experts) resulting in a mean
combined accuracy score of 78% for the experts and 56% for the near experts. A mobile
eye tracker measured the location and duration of each players gaze during free-throw
shots. The findings indicated that experts took longer than near experts during the
preparation phase (500 ms) and were steadier during pre-shot. Perhaps most importantly,
the experts appeared to suppress visual information when their hands entered the visual
field by blinking or moving gaze freely. Conversely, the near-experts either initially fixated
on the hoop very briefly or attempted to maintain fixation for the duration of the shot.
Although vision was not occluded in the investigation by Vickers (1996), the findings
indicate that given sufficient preparatory time, vision may not be needed to achieve the
task goal (i.e., a successful basket). Oudejans, van de Langenberg, and Hutter (2002)
manipulated visual information during a basketball jump shot by ten experienced
basketball players (nine were Dutch professional players). The researchers manipulated
occlusion such that the conditions were full-, no-, early- (vision occluded during final 350
ms before ball release), and late-vision (vision occluded until final 350 ms before ball
release). The findings indicated that successful baskets were achieved equally well under
full-vision (mean 61.5%) and late-vision (60.5%) conditions. Moreover, successful baskets
were achieved significantly less during the early-vision (30%) and no-vision conditions
(17.5%). However, it can be argued that this reflects the need for the preparatory period
(albeit brief) identified by Vickers. More specifically, in the no-vision condition there is no
opportunity for preparation and the preparatory period is disrupted by the early-vision
condition. Conversely, the preparatory period remains intact during the full-vision
condition and late-vision conditions (albeit brief).
An alternate explanation of the equally high performance exhibited during the full- and
late-vision conditions concerns the role of vision in the control of the jump during the
jump-shot. If controlling the jump is a significant factor in the success of the jump-shot,
though not typically controlled, the full- and late-vision conditions appear to allow the
jump component of the task to proceed unimpeded. Conversely, during the early- and no
21
vision conditions, the opportunity to make a visual appraisal of the task space is removed
either in situ (early-vision) or is never available (no-vision). This explanation may appear
to argue against a mental representational account of task performance, given there is some
reliance on external cues. However, as an aiming task requiring some level of visual
appraisal, target-assessment may not occur until the performer reaches or nears the apex of
the jump. Thus, the mental representation would reflect this coupling of the timing of the
jump with visual appraisal (an in-situ pre-shot appraisal). Moreover, the high success rate
of performers during the late-vision conditions (60.5%) argues for this hypothesis. In
essence, the mental representation of jump shot performance allows for the initiation of
task performance and the critical window of visual appraisal remains intact. Conversely,
this critical window is lacking in the early-vision condition and is reflected in the
participants’ performance (success on 30% of the attempts).
The studies by Vickers (1996) strongly adhered to the requirements of the expert
performance approach. Experts were designated and distinguished by near experts based
upon in-game free-throw shooting percentages. The experimental performance measures
confirmed this classification and experts maintained a performance advantage over near
experts. Furthermore, the task of free-throw shooting at a standard basketball goal must be
considered a representative task of basketball free-throw shooting. Thus, despite the study
not being a visual occlusion task, the role of vision in skilled task performance was
nonetheless identified because of adherence to expert performance approach guidelines.
Oudejans et al. (2002) adhered to the expert performance approach to a lesser degree
than Vickers (1996). However, given the professional level of the players, one might
assume that performance measures would have confirmed superior performance relative to
a comparison group. There is also a possibility that the disruptive nature of occluding
vision “mid-shot” can explain the low percentage of made baskets by participants in the
early vision condition (as argued above). If this were the case, the degree to which the
condition reflected a representative task is questionable. Moreover, the essence of skilled
performance of basketball jump-shots may be the way in which the performer makes use of
the peak of the jump (similar to the preparatory periods in Vickers, 1996). Such an account
would explain the high percentage of made baskets by participants during the late-vision
condition (3 of the included 8 participants performed the best under the late-vision
22
conditions). A comparison of skill groups based upon performance measures could
investigate this hypothesis.
Section Summary
The mental representations approach states that internalized and refined mental
representations mediate skilled performance (e.g., Ericsson & Lehmann, 1996). These
representations allowing skilled performance are comprised of representations of: a) the
desired performance goal, b) production aspects of the task, and c) representations of
actual task performance (e.g., Lehmann, 1997). Studies in which visual information was
removed are useful for investigating the potential mediating effects of these representations
given their proposed internalized nature. Studies investigating the effect of visual
occlusion for static balance tasks (Paillard et al., 2006) and simulated tasks (Bennet &
Davids, 1995) were introduced and the findings indicated mixed results. However, because
many of the studies for which expert-novice comparisons did not adhere to the framework
of the expert performance approach, the applicability of many of the studies for addressing
the issues outlined above are limited. However, when adherence to the expert performance
approach is high (e.g., Bennet & Davids, 1995) the findings based upon objective
performance measures support a mental representations approach.
23
CHAPTER TWO
THE PRESENT STUDY
The task of ten-pin bowling is introduced in the following section. As a self-paced task,
ten-pin bowling is especially suited to investigate the diverging claims made by the
ecological/dynamical and mental representations approaches outlined above. The task of
ten-pin bowling allows adherence to the expert performance approach (Ericsson & Smith,
1991) with regard to expert performer identification (bowling average), representative
tasks (bowling frames), and performance measures (number of pins knocked down) which
allows objective comparisons of skilled and less-skilled bowlers.
The Task of Ten-Pin Bowling
Ten-pin bowling is a self-paced task in which a performer is required to propel a
spherical object of varying weight (i.e., bowling ball) down a lane measuring 60 feet
(18.28 m), from foul line to head pin, in an attempt to knock over ten pins placed 12’’ apart
in an equilateral triangle formation. The lane is 3.5 feet (4.57 m) wide and is usually
comprised of 39 wooden boards. On each side of the lane are gutters which serve to collect
balls that travel beyond the width of the lane. Two sets of approach dots are placed 12 feet
(3.65 m) and 15 feet (4.57 m) back from the foul line in the starting area of the lane.
Additionally, guide arrows can be found around 15 feet (4.57 m) from the foul line down
the lane (American Bowling Congress and Women’s International Bowling Congress
[ABC/WIBC], 2004).
A game of ten-pin bowling is divided into ten frames. During each frame, a player is
given two opportunities to knock down all the pins. If a player knocks down all the pins
during the first opportunity of the frame (known as a “strike”) then the frame ends and
bonus points are awarded based upon both opportunities of the subsequent frame. If all the
pins are again knocked down during the subsequent frame, the bonus points are
cumulatively added during subsequent frames. A perfect game is achieved when all pins
are knocked down during every available opportunity resulting in a perfect score of 300.
However, if pins remain following the first opportunity, the player rolls again in an attempt
to knock down the remaining pins. If the player is successful in knocking down all of the
24
remaining pins (known as a “spare”) then it results in a bonus based upon the first
opportunity of the subsequent frame. Conversely, if the player is unsuccessful in knocking
down all of the remaining pins (known as an “open frame”) then bonus points are not
awarded based upon subsequent frames (Day, 1948).
A bowler chooses a starting point (which may or may not be based on the approach
dots) and typically “sets up” for the shot. During the set up period, the bowler engages in
any pre-shot routines, determines where to strike the pins, and decides how best to place
the ball to achieve the desired outcome. Following this “set up” period, the bowler begins
an approach that is usually four or five steps and releases the bowling ball. Many bowlers
use the guide arrows, described above, rather than the pins to place the ball on a path down
the lane (Harris & Ericsson, 2006). An ideal shot for a right-handed bowler would be
having the ball contact the 1, 3, 5, and 9 pins or the 1, 2, 5, and 8 pins for a left handed
bowler (Weiskopf, 1978).
The origins of bowling have been traced back to at least 300 AD with some attributing
artifacts dating back to 3200 BC as related to an early version of the game. Currently tenpin bowling is played by over 95 million people worldwide
(http://www.bowlingmuseum.com/history.asp). Bowling is an activity in which a wide
range of people can participate, regardless of age and other physical characteristics.
Moreover, facilities are generally available to most of the population in locations ranging
from large cities to small towns. Given this accessibility to the sport, there are many casual
bowlers for whom bowling is only a means for passive recreation. Conversely, there are
bowlers who seek to continually improve their bowling performance and many of these
bowlers (which includes professional bowlers) likely engage in deliberate practice
(Ericsson, et al. 1993). Furthermore, preliminary interviews with collegiate bowlers
confirm engagement in deliberate practice activities (Harris & Ericsson, 2006). As stated
above, engagement in these types of activities leads to refined representations of task
performance (Ericsson & Lehmann, 1996). As consistency of performance (particularly
given knowledge of what comprises an ideal shot) is one expected result of these refined
representations (e.g., Hill, 2003), one would expect those having engaged in deliberate
practice (i.e., skilled bowlers) to exhibit more consistent performance than those not
engaging in deliberate practice. Moreover, this increased consistency will be evidenced in
25
success rate (i.e., number of pins knocked down) and by the reduced variability in both the
physical movements of the bowler during the act of bowling (i.e., execution variability).
Moreover, enhanced performance consistency will be reflected in the subsequent path
taken by the ball and ultimate striking location (i.e., result variability).
The Proposed Relationship between Execution Variability and Success Rate
A skilled performer would by definition have to display a high success rate during
regular competition and for the experimental task with full vision--consistent with both of
the aforementioned approaches. Based on preliminary interviews with collegiate bowlers
(Harris & Ericsson, 2006) it was found that shot consistency is desired and practiced.
Moreover, pre-shot routines are common and often include mentally visualizing the path of
the bowling ball. As Hill (2003) proposed for the task domain of golf, these pre-shot
routines enhance or possibly access the performer’s mental representations, which guide
and ultimately enhance performance. Consequently, the expert performance approach
(Ericsson, 2002) would predict that skilled bowlers have acquired some mediating
mechanism that allows them to increase their bowling accuracy over successive trials than
novices. It is predicted that this increase in performance is attained by decreasing the
execution variability and that this movement consistency have been developed via refined
representations of task requirements resulting from significant amounts of time engaged in
deliberate practice activities.
For advocates of ecological/dynamical systems, the predictions regarding execution
variability are not as straightforward. For instance, some advocates of the approach
(Williams, David, Williams, 1999) posit that highly reproducible performance (when
consistency is desirable) in the form of low execution variability, can be a component of
skilled performance. However, other advocates (e.g., Scholz, Schöner, & Latash, 2000; see
also Davids, Button, & Bennett, 2008) maintain that, even for static tasks with consistent
task requirements (e.g., pistol shooting), execution variability of some component parts
(e.g. shoulder and elbow) was high, allowing skilled participants to stabilize the pistol via
hand stability.
From the perspective of the expert performance approach, it is predicted that execution
variability over successive trials will be low for skilled performers and significantly lower
than that of novice performers during conditions for which external environmental
26
information is available. Moreover, this low level of execution variability will be
negatively correlated with success rate and positively correlated with skill level. However,
the implemented task would be considered well-rehearsed for skilled performers and some
ecological/dynamical systems advocates would argue that execution variability would be
low in this well organized system (e.g., Williams, David, Williams, 1999). Thus, both
approaches could also account for low levels of execution variability during task
performance. However, skilled performers exhibiting somewhat high levels of execution
variability over successive trials combined with a high success rate will lend support to the
ecological/dynamical systems approach.
The Proposed Relationship between Environmental Information, Execution Variability and
Success Rate
The most direct divergence of the two approaches, described above, is the role of
environmental information during task performance. The most straightforward way to
determine if (skilled) performance levels can be maintained in the absence of
environmental information is to remove environmental information during task
performance. From the perspective of the mental representations approach, following the
removal of environmental information, skilled performers will maintain low levels of
execution variability and a success rate advantage over novice participants. This prediction
is derived from the proposed reliance of skilled performers on internalized representations
of task requirements. In contrast, from the perspective of the ecological/dynamical systems
approach predictions, execution variability would increase, and because of a lack of
environmental guidance, the expert advantage for success rates will not exist.
More specifically, if skilled bowlers can continue to bowl well despite the removal of
environmental information (visual occlusion and removal of auditory feedback), task
performance is likely reliant upon internalized mental representations of task performance.
Moreover, if during the environmentally deprived conditions, performance levels (high
success rates) are maintained and execution variability remains minimal, the findings
would again be indicative of internal performance monitoring. However, if execution
variability is significantly greater during environmentally-deprived conditions than nondeprived conditions but the success rate remains high, the findings would be indicative of
internal performance monitoring with appropriate adjustments based upon internal
27
feedback sources. This outcome would arguably support a mental representations approach
but dynamical/ecological systems advocates would likely argue that the information source
for system organization had shifted to these internalized sources. Similarly, if execution
variability is significantly greater during environmentally-deprived conditions than nondeprived conditions and the success rate significantly falls, the findings would likely be
inconclusive and represent a floor effect. However, the ability of skilled performers to
modify task goals and achieve a high success rate compared to novice performers
(regardless of execution variability) during environmentally-deprived conditions would
lend support to a representations approach. Finally, the ability of skilled performers to
provide accurate and content-rich verbal reports of task performance during
environmentally occluded conditions would arguably support a mental representations
approach.
Retrospective Verbal Reports of Task Performance
Given the differing accounts regarding the importance of environmental information,
as proposed by advocates of the mental representations approach and the
ecological/dynamical systems approach, the process tracing method of collecting verbal
reports (Ericsson & Simon, 1984/1993) will be implemented. Ericsson & Simon validated
the use of verbal reports as data which accurately reflect (when properly attained)
cognitions having been held in short-term/working memory. Thus, can skilled performers
provide accurate verbal reports of task performance, even when environmental sources of
information are removed? Furthermore, how would an approach that disavows1 any form
of cognitive mechanism (including mental representations) as playing a role in task
performance account for the accurate content contained in these reports?
Hypotheses of the Present Study
Consistency during task performance is a characteristic of many types of expert
performance (Ericsson, 1998). Furthermore, as stated above, preliminary interviews
confirm that consistency is one goal of collegiate bowlers while engaging in practice
activities (Harris & Ericsson, 2006). Consistency of task performance with respect to
1
In a recently published book on dynamical systems, Davids, Button, and Bennett, (2008) claim that the
approach and Gibson’s (1979) work had been misinterpreted that it “eliminates the role of cognitive
processes in action” (p.65). The authors argue that cognitive processes were just minor considerations given
the importance of perception for action.
28
execution variability and success rate will be investigated for the task of ten-pin bowling. It
is predicted that compared to novice bowlers, skilled bowlers will exhibit less execution
variability and have much higher success rate (e.g., a higher percentage of the appropriate
pins knocked down) over successive trials. Moreover, this low level of execution
variability is predicted to be an essential component of skilled performance in bowling.
Furthermore, adherents of the ecological/dynamical systems approach posit that
internalization of a representation is not necessary for high levels performance. More
specifically, performance is the result of the direct link between perception of
environmental information and motor response (e.g., Bootsma & van Wieringen, 1990).
Thus, an assumption of the ecological/dynamical systems approach is that cognitive
processing and internal representations do not mediate skilled performance.
To explicitly test the necessity of environmental information in the skilled performance
of ten-pin bowling, we propose to reduce the availability of visual information and reduce
auditory information. Consistent with a mental representations approach, we hypothesize
that during occluded conditions, skilled performers will maintain low execution variability
and maintain a performance advantage over successive trials when compared to novice
performers. Novice performers will experience substantial performance decreases in the
form of performance outcome (pins knocked down) and increased execution variability.
Moreover, it is predicted that skilled performers will not experience this magnitude of
performance decreases.
Finally, because extended deliberate practice is the proposed mechanism by which
refined representations of task requirements are developed, it is proposed that hours
engaged in deliberate practice activities will be correlated with success rate and negatively
correlated with execution variability. Moreover, the number of hours engaged in deliberate
practice activities will be correlated with bowling average and skill level. These
relationships are also expected to hold during occluded conditions.
In sum, the hypotheses of the present study can be organized in the following groups:
First, it is predicted that skilled performers will have a significantly higher success rate
(e.g., a higher percentage of the appropriate pins knocked down) than novice participants
on the two standardized bowling tasks. Furthermore, we predict that the skilled performers
29
will exhibit less execution variability over successive trials than novices and that lower
execution variability will be correlated with higher success rates.
Second, based on these predicted findings in the normal bowling tasks, it predicted that
skilled performers will maintain their low levels of execution variability even following
removal of environmental information, and thus attain relatively high success rates in the
absence of relevant environmental stimuli.
Finally, it is predicted that the superior performance in bowling and in particular the
low execution variability is correlated with relevant practice activities and in particular
solitary and team deliberate practice.
In Chapter 4, the measures of performance associated with success rate and execution
variability will first be explored. In the same chapter, the relation between execution
variability and success rate and each measure’s relationship to skill level is investigated.
Once these relations have been examined, the role of environmental information during
task performance is explored by having participants bowl under visual and auditory
occlusion will be examined in Chapter 5. Unexpected findings for the occluded conditions
lead to additional methods of analysis for execution variability reported in Chapter 6. The
final chapter reporting results (Chapter 7) examines practice activities and their relations to
success rate and refined representations of task performance and the associated execution
variability.
30
CHAPTER THREE
METHOD
Participants
Sixteen skilled and sixteen novice bowlers were initially selected for participation. The
skilled participants are Florida State University bowling team members and have a “house”
bowling average of 170 or higher. The averages for the skilled bowlers ranged from 170 to
240 (mean score = 200.67). The novice participants were recruited from several Florida
State University bowling classes and have a class bowling average of 120 or less. The
averages for the novice bowlers ranged from 50 to 120 (mean score = 103.75).
Malfunctions of the data-collection apparatus resulted in the loss of data for one skilled
participant (with the exception of outcome measures) and four novice participants. Four
new novice participants were subsequently recruited and tested. During later analysis, it
was discovered that a system malfunction required the exclusion of another novice
participant. Thus, the motion analysis data presented below is based upon a comparison of
fifteen skilled- and fifteen novice-participants.
Motion Analysis Measures
Each participant’s performance was recorded using high-speed motion analysis
cameras (A600 series, Bassler Vision Technologies) and analyzed using motion analysis
software (MaxTRAQ, Innovision Systems Incorporated). In order to capture the execution
variability of the participants, a black sleeve and white markers (38 mm Sportcraft 1 Star
Grade Recreational Table Tennis Balls) were placed at designated locations on the bowling
arm of each participant. More specifically, the markers were placed on the participant’s
wrist (WRT), forearm (ARM), elbow (ELB), upper arm (UPA), and shoulder (SHO).
Measures were also taken of the location of the participant’s front foot (FF) and back foot
(BF).
31
Motion Analysis
SHO
UPA
ELB
WRT
ARM
Figure 1: Position of motion analysis markers on participants.
Procedure
Participants from both the skilled and novice group were instructed to make 10 strike
and 10 spare attempts, which were sub-divided into two groups of 10 bowls (5 each of
strike and spare attempts) with the order of strikes and spares determined randomly. All
participants were instructed to bowl the same randomized sequence of 20 bowls. Half the
participants from each group were randomly assigned to bowl strikes and spares with full
vision for the first ten bowls (normal to occluded group) or with visual and auditory
occlusion for the first ten bowls (occluded to normal group). Participants bowled under the
remaining condition for the second ten bowls. During each trial all ten pins were presented
in their regular positions to the participants in all conditions and the task was either to
knock down as many pins as possible (strike attempt) or only the ten-pin for right-handed
bowlers (seven-pin for left-handed bowlers)—the spare attempt. Retrospective verbal
reports (Ericsson & Simon, 1984/1993) were elicited following six of the 20 trials.
Following a warm-up period, participants in the normal condition were instructed to
“Bowl to the best of your ability and try to achieve a strike (spare by knocking down the
ten-pin only).” The participant was then allowed to set-up and to prepare the shot. The pins
were reset following this first bowl to clear any remaining pins and the participant was
again asked to “Try to achieve a strike (spare by knocking down the ten-pin only).” This
32
process was repeated until the participant had bowled ten times under normal conditions. A
retrospective verbal report was elicited immediately following three, pre-determined trials
of the ten bowls in the condition with full vision.
Participants in the occluded condition followed the same procedure with the following
exceptions: participants were wearing earplugs (AO Safety, Noise Reduction Rating 32
dB) and following the set-up prior to bowling, visual occlusion glasses2 were placed upon
the participants. A retrospective verbal report was elicited immediately following three,
pre-determined, trials of the ten bowls in the occluded condition.
2
The visual occlusion glasses have a small gap between the bottom of the lenses and the participants’ cheeks,
which allowed minimal visual appraisal of the floor if the participant directed their gaze straight down.
33
CHAPTER FOUR
RESULTS
This chapter will first report on the predicted group differences between expert and
amateur bowlers on the two standardized tasks designed to capture expert bowling
performance. Differences between the two groups’ performance would validate the ability
of the selected tasks to capture relevant aspects of the competitive bowling performance.
After examining overall performance in bowling, we will examine whether the groups
differed in their movements generating the release of the bowling ball, in particular how
similar the movements were from one trial to the next.
Success Rate
It was hypothesized that skilled participants would exhibit a performance advantage
over novice participants regarding the success of each bowling trial (e.g., total number of
the appropriate pins knocked down).
Spare trials. For spare trials, participants received a score of 1 if the ten-pin (or sevenpin for left-handed participants) was knocked down and a 0 if the ten-pin was left standing.
Moreover, credit for a spare was given only if the path of the ball would have knocked the
pin down in the absence of other pins. For example, a score of 0 was awarded if the ten-pin
was knocked down by pins set in motion from an initial impact on the two-pin. The
maximum possible score per visual condition is 5. The data was then converted into
percent of the maximum score before submitted to the statistical analyses.
The success rate for spare trials was analyzed via a one-way ANOVA with skill level
as a between subjects factor. There was a significant effect of skill level on success rate, F
(1, 28) = 65.68, p < .01. The skilled participants (M = 93.3%, SD = 12.3%) statistically
outperformed novice participants (M = 37.3%, SD = 25.3%). Thus, as predicted the skilled
participants exhibited a significant performance advantage over novice performers during
spare trials, thereby objectively confirming their superior performance levels.
34
100.00
Mean Spare
80.00
60.00
40.00
20.00
0.00
Novice
Skilled
Skill
Figure 2: Mean success rate on spare trials as a function of skill-level.
Strike trials. For strike trials, participants received a point for every pin knocked down
which created a possible score between 0 and 10. The maximum possible score per visual
condition is 50. The data were transformed into percent of the maximum score and
submitted to statistical analysis. The success rate for strike trials was analyzed via a oneway ANOVA with skill level as a between subjects factor. There was a significant effect
of skill level on success rate, F (1, 28) = 28.64, p < .01. The skilled participants (M =
85.9%, SD = 10.3%) statistically outperformed novice participants (M = 54.1%, SD =
20.5%). Once again, the skilled participants exhibited a significant performance advantage
over novice performers during strike trials, objectively confirming superior performance
levels.
35
100.00
Mean Strike
80.00
60.00
40.00
20.00
0.00
Novice
Skilled
Skill
Figure 3: Mean success rate on strike trials as a function of skill-level.
Consistency Measures
The findings regarding success rate indicated that, as hypothesized, skilled performers
achieved higher success rates than novice participants. For bowlers, if a particular way of
executing a shot led to a successful outcome, then executing the shot in that same manner
each time would presumably be the most efficient way to consistently achieve a high
success rate. Thus, is a difference in execution variability a distinguishing factor between
participants consistently achieving a high success rate and those who are not? Based upon
initial interviews with skilled bowlers indicating that consistency of execution was both
desirable and practiced (Harris & Ericsson, 2006), it was hypothesized that skilled
performers would exhibit less within-participant execution variability than novice
performers over successive trials. To test these hypotheses, frame-by-frame analysis was
36
conducted by starting from the point at which the bowling ball had completely cleared the
foul-line (refer to Figure 4 for a demonstration of the task parameters) and working
backwards from this point toward the beginning of the bowl (Figure 5; see appendices for a
detailed description). A within-participant consistency average3 was generated for each of
the marker positions (WRT, ARM, ELB, UPA, SHO, FF, and BF) over specified
combinations of trials. More specifically, these consistency measures resulted in
comparisons of each trial to the subsequent trial (e.g., the first trial to the second trial, the
second trial to the third trial, etc.) for both spares and strikes.
Figure 4: Demonstration of marker tracking and parameters of task area.
3
This average was derived by taking the minimum number of frames available for a participant when the
normal or occluded conditions were collapsed across spares or strikes. For example, if the smallest number of
frames that participant 17 had across all spare-trials was 83, then the average for all spare trials was derived
by working backward from the endpoint of the ball clearing the foul-line for 80 frames in both normal and
occluded trials.
37
For example, one consistency measure was derived by calculating the average
difference in cm between the position of the wrist (WRT) marker of a single participant
across the frames of the first spare trial to the position of the WRT marker across the
frames of the subsequent trial (second spare trial) of that same participant, then the second
spare trial to the third spare trial, and so on. Thus, each marker was associated with two
consistency scores: a) subsequent spares and b) subsequent strikes.
Frame X
Time
Frame 1
Spare: Trial 1
Spare: Trial 2
Spare: Trial 2
Spare: Trial 3
-compared to-
Spare: Trial 3
Spare: Trial 4
Spare: Trial 4
Spare: Trial 5
Figure 5: Example calculation of within-participant consistency average for each of the
marker positions on spare trials.
Execution variability: spares. All consistency measures for each of the markers (WRT,
ARM, ELB, UPA, SHO, FF, and BF) were analyzed individually with a one-way ANOVA
with skill level as a between subjects factor. Because of the overall trend of similar
38
findings from the analyses for each marker (Table 1), the data were analyzed by means of a
principal component analysis. Based on an examination of the Scree Plot, seen in Figure 6,
only the first component was included. The first component comprised of all seven of the
markers with an Eigenvalue of 5.86 was found with 83.7% of the variance explained. The
marker loadings on the first component during spare trials can be found in Table 1.
Table 1
Mean Variability (cm) for Each Marker by Skill Level and Marker Item Loadings on the
First Component during Spare Trials from Principal Component Analysis
Marker
Skilled
Novice
Factor
Loading
WRT
4.37 (1.37)
16.22 (9.86) **
.991
ARM
3.89 (1.39)
15.21 (10.14) **
.991
ELB
3.72 (1.32)
14.68 (10.46) **
.989
UPA
3.69 (1.28)
14.77 (11.14) **
.984
SHO
3.57 (1.29)
14.77 (12.24) **
.975
FF
3.08 (1.61)
10.34 (5.85) **
.802
BF
6.13 (3.00)
12.86 (8.79) **
.596
** p <.01, * p < .05
Because of the component identified in the principal component analysis, component
scores for this combined measure representing execution variability during spare trials was
calculated. This single combined marker (component score) was analyzed via a one-way
ANOVA with skill level as a between subjects factor. There was a significant effect of
skill level on execution variability, F (1, 28) = 19.39, p < .01. The skilled participants were
statistically less variable than the novice participants. Thus, as predicted skilled
39
participants exhibited lower degrees of execution variability than novices during spare
trials. A sample direct comparison of consistency for skilled and novice participants over
five spare trials is presented in Figure 7.
Figure 6: Scree Plot from the Principal Component Analysis of markers during spare
conditions.
Execution variability: strikes. All consistency measures for each of the markers (WRT,
ARM, ELB, UPA, SHO, FF, and BF) were analyzed individually with a one-way ANOVA
with skill level as a between subjects factor. Once again, because of the overall trend of
similar findings from the analyses for each marker (Table 2), the data were analyzed by
means of a principal component analysis. Based on an examination of the Scree Plot seen
in Figure 8, only the first component was extracted for analysis. The first component
40
contained high factor loadings for all seven of the markers with an Eigenvalue of 5.61,
which is associated with explaining 80.08% of the variance. The factor loadings on the
single factor during strike trials for each marker can be found in Table 2.
Table 2
Mean Variability (cm) for Each Marker by Skill Level and Marker Item Loadings on the
First Component during Strike Trials from Principal Component Analysis
Marker
Skilled
Novice
Factor
Loading
WRT
4.78 (2.50)
14.42 (7.79) **
.975
ARM
4.39 (2.15)
13.36 (7.64) **
.983
ELB
4.22 (2.09)
12.84 (7.49) **
.985
UPA
4.10 (1.94)
12.59 (7.58) **
.985
SHO
3.91 (1.78)
12.42 (7.58) **
.978
FF
4.04 (2.46)
9.37 (7.55) **
.631
BF
7.24 (3.64)
12.51 (7.38) **
.628
** p <.01, * p < .05
41
Figure 7: Marker traces representing five spare trials for a skilled (top) and a novice
(bottom) participant.
42
Figure 8: Scree Plot from the Principal Component Analysis of markers during strike
conditions.
Because of components identified in the principal component analysis, a single
combined measure representing execution variability during strike trials was calculated.
This single combined marker (component score) was analyzed via a one-way ANOVA
with skill level as a between subjects factor. There was a significant effect of skill level on
execution variability, F (1, 28) = 19.71, p < .01. The skilled participants were statistically
less variable than the novice participants. Thus, as predicted skilled participants exhibited
lower degrees of execution variability than novices during strike trials. A sample direct
comparison of consistency for skilled and novice participants over five strike trials is
presented in Figure 9.
43
Figure 9: Marker traces representing five strike trials for a skilled (top) and a novice
(bottom) participant.
44
Correlations between Participants’ Success Rate and Execution Variability
It was hypothesized that consistency of task performance in the form of low execution
variability and high success rates is a characteristic of skilled performance. In this section,
the degree to which success rate and execution variability are correlated with one another,
and with skill, is explored.
Table 3
Correlations between Execution Variability, Success Rate, and Skill-Level during Spare
Trials
Success
Rate
Execution
Variability
Success Rate
1.000
Execution Variability
-.759**
1.000
Skill Level
.848**
-.859**
Skill
Level
1.000
** p <.01, * p < .05
Spares. A Spearman rho correlation coefficient was calculated for the relationship
between success rate and execution variability (Table 3). Success rate was negatively
correlated with execution variability, i.e., the greater the amount of execution variability
exhibited by the participant, the lower the participant’s score. More specifically, there was
a significant negative correlation between combined execution variability and success rate
in the spare conditions (rs= -.759, N = 30, p < .01, two tailed). Moreover, there was a
significant correlation between skill level and spare success rate (rs= .848, N = 30, p < .01,
two tailed); as reported above, the skilled participants are associated with higher success
rates than novice participants. There was also a significant negative correlation between
execution variability and skill level (rs= -.859, N = 30, p < .01, two tailed). Skilled
participants were associated with less execution variability than novice participants.
Moreover, to determine if execution variability predicted success on spare trials above and
45
beyond skill-level, the variables were entered into a regression equation in a stepwise
manner. Execution variability successfully predicted success rate during spare trials,
accounting for 18.6% of the variance, F (1, 28) = 7.63, p < .01. Moreover, participants’
house average (an indicator of skill) accounted for an additional 47.2% of the variance,
Fchange (1, 27) = 40.50, p < .01. Thus, execution variability based on five trials can be used
to predict success rate during spare trials but skill level, which is based on hundreds or
thousands of trials, is the best indicator of success rate on spare trials.
Strikes. For strike trials, there was again a significant negative correlation between
combined execution variability and success rate (rs= -.718, N = 30, p < .01, two tailed).
The significant correlation between skill level and strike success rate (rs= .780, N = 30, p <
.01, two tailed), and the significant negative correlation between execution variability and
skill level (rs= -.774, N = 30, p < .01, two tailed) found for spare trials was also found for
strike trials (Table 4). Execution variability successfully predicted success rate during
spare trials, accounting for 40.7% of the variance, F (1, 28) = 20.89, p < .01. Moreover,
participants’ house average accounted for an additional 21.9% of the variance, Fchange (1,
27) = 16.70, p < .01. Thus, execution variability based on five trials is a good predictor of
success rate during strike trials and the addition of skill level improves the predictability.
Table 4
Correlations between Execution Variability, Success Rate, and Skill-Level during Strike
Trials
Success
Rate
Execution
Variability
Success Rate
1.000
Execution Variability
-.718**
1.000
Skill Level
.780**
-.774**
** p <.01, * p < .05
46
Skill
Level
1.000
Section Summary
Consistency of task performance in the form of a high success rate and low execution
variability was hypothesized to be a characteristic of skilled performance in ten-pin
bowling. As hypothesized, skilled participants exhibited significantly higher rates of
success than novice participants during both spare (93.3% versus 37.3% successful) and
strike (85.9% versus 54.1% successful) trials. Moreover, these measures of success rate
ensured the successful objective identification of expert performers.
With regard to execution variability, it was hypothesized that skilled performers would
exhibit less within-participant execution variability than novice performers over successive
trials. The initial measures of execution variability during spare and strike trials was
calculated via a frame-by-frame analysis of markers placed on participants’ wrist (WRT),
arm (ARM), elbow (ELB), upper-arm (UPA), and shoulder (SHO) over subsequent trials,
in addition to the final position of the participants’ front foot (FF) and back foot (BF).
Following principal component analyses, these variables were subsequently reduced into a
single component score for both spare and strike trials. As predicted, skilled participants
exhibited significantly less execution variability than novice participants during both spare
and strike trials.
Spearman rho correlation coefficients were calculated to examine the relationship
between skill level, success rate and execution variability for spare and strike combined
execution variability measures. Success rate was negatively correlated with execution
variability, i.e., the greater the amount of execution variability exhibited by the participant,
the lower the participant’s score, on both spare (rs= -.759, N = 30, p < .01, two tailed) and
strike (rs= -.718, N = 30, p < .01, two tailed) trials. There was also a significant negative
correlation between execution variability and skill level on both spare and (rs= -.859, N =
30, p < .01, two tailed) strike (rs= -.774, N = 30, p < .01, two tailed) trials. Moreover, skill
level was significantly correlated with success rate on both spare (rs= .848, N = 30, p <
.01, two tailed) and strike (rs= .780, N = 30, p < .01, two tailed) trials. Thus, low levels of
execution variability were associated with higher success rates and skill level. The role of
deliberate practice, as the proposed mechanism by which this relationship was established,
is discussed in Chapter 7. In the next chapter, Chapter 5, the theoretically contentious role
of available environmental information during task performance is explored.
47
CHAPTER FIVE
THE ROLE OF ENVIRONMENTAL INFORMATION DURING TASK
PERFORMANCE
It was correctly hypothesized that skilled performers would exhibit less withinparticipant execution variability and achieve a higher success rate than novice performers
during normal conditions. The results presented in the preceding chapter provide
confirmatory evidence that consistency of performance in the form of low execution
variability and high success rate is a component of skilled performance. Moreover, there
was a negative correlation between execution variability and success rate indicating that
low levels of (appropriate) execution variability will lead to high success rates.
As stated in the introduction, the most direct disagreement between the two discussed
theoretical frameworks concerns the role of environmental information during task
performance. If representations of task performance are cognitively mediated mental
representations, as proposed by the expert performance approach, low execution variability
should be possible to maintain in the absence of environmental information. However, if
performance is reliant only upon responses to environmental information, there should be
an observed change leading to increased execution variability and leading to decreases in
success rates. In this section, the results associated with the effect of removing
environmental information on success rate and execution variability during skilled
performance will be presented.
Success Rate
Because of the proposed reliance on internal representations for task performance by
skilled performers, it was hypothesized that skilled performers would maintain a
performance advantage over novice performers with regard to success rate during occluded
conditions. Moreover, this hypothesis is contradictory to the ecological/dynamical systems
approach because of the proposed reliance upon environmental information for guidance
during performance.
Spares. To investigate the effect of the removal of environmental information on
success rate, the success rate for occluded spare trials was analyzed with a mixed model
48
ANOVA with skill level as a between subjects factor and visual condition as a within
subjects factor. There was a significant interaction between skill level and visual condition,
F (1, 28) = 32.61, p < .01. As reported in Chapter 4, the skilled participants (M = 93.3%,
SD = 12.3%) statistically outperformed novice participants (M = 37.3%, SD = 25.3%) in
the normal condition. However, there were no significant differences in performance
between the skilled participants (M = 30.7%, SD = 14.9%) and novice participants (M =
25.3%, SD = 24.5%) in the occluded vision condition. Thus contrary to predictions, the
skilled participants did not maintain their performance advantage over novice participants
with regard to success rate during environmentally occluded spare conditions (Figure 10).
Strikes. The success rate for strike trials was analyzed with a mixed model ANOVA
with skill level as a between subjects factor and visual condition as a within subjects
factor. For the success rate of strike trials, there was a main effect for skill level, F (1, 28)
= 35.69, p < .01, and visual condition, F (1, 28) = 62.20, p < .01. As reported in Chapter 4,
the skilled participants (M = 85.9%, SD = 10.3%) statistically outperformed novice
participants (M = 54.1%, SD = 20.5%) in the normal conditions. However, for strike trials,
the skilled participants (M = 48.3%, SD = 18.4%) also statistically outperformed novice
participants (M = 28.7%, SD = 12.9%) in the occluded vision conditions, F (1, 28) = 11.39,
p < .01.
Thus, for strike trials, the performance advantage of skilled performers was exhibited
in both the normal and occluded conditions. Although skilled performance during occluded
conditions as compared to normal conditions was significantly degraded, the performance
of skilled participants in the occluded condition (M = 48.26%) was statistically equivalent
to the performance of novice participants in the normal conditions (M = 54.10%; F < 1). In
sum, the advantage of skilled performers was maintained during occluded conditions and
skilled performance during occluded conditions was equivalent of novice performance
during normal conditions.
49
Spare
Spare_O
100.00
80.00
Mean
60.00
40.00
20.00
0.00
Novice
Skilled
Skill
Figure 10: Mean success rate on spare trials as a function of skill-level and visual
condition.
50
Strike
Strike_O
100.00
80.00
Mean
60.00
40.00
20.00
0.00
Novice
Skilled
Skill
Figure 11: Mean success rate on strike trials as a function of skill-level and visual
condition.
Execution Variability
The results of the preceding section indicated that skilled participants maintained a
performance advantage with regard to success rate during occluded conditions for strike
trials but, unexpectedly, not for spare trials. An additional hypothesis was, that compared
to novice participants, skilled performers would exhibit lower degrees of execution
variability during both normal and occluded conditions because of the internalized nature
of the representation of task performance. The results presented in Chapter 4 confirmed
that skilled participants exhibit significantly less execution variability than novice
participants during normal conditions and less execution variability was negatively
51
correlated with success rate. Thus, will increases in execution variability during the
occluded conditions explain the decreases in success rate? In this section, the execution
variability of participants during occluded conditions is analyzed to determine if the
decreases in success rate, particularly during spare trials, can be explained by increase in
execution variability.
Execution variability: subsequent spares. All consistency measures for each of the
markers (WRT, ARM, ELB, UPA, SHO, FF, and BF) were analyzed individually with a
mixed model ANOVA with skill level as a between subjects factor and visual condition as
a within subjects factor. Because of the overall trend of similar findings from the analyses
for each marker (Table 5), the data were analyzed by means of a principal component
analysis. Based on an examination of the Scree Plot seen in Figure 12, only the first two
components were extracted for analysis. The first component contained high factor
loadings for five of the markers (moderate loadings for the other two markers) with an
Eigenvalue of 5.28, which is associated with explaining 75.36% of the variance. The
second component contained high factor loadings for two of the markers with an
Eigenvalue of 1.68, which is associated with 23.97% of the variance. The first component
was heavily loaded with the markers of the upper body (WRT, ARM, ELB, UPA, SHO)
and the second component was heavily loaded with markers of the lower body (FF, BF).
The factor loadings on the two factors during occluded spare trials can be found in Table 5.
Each of these combined markers (component score) was analyzed via a one-way
ANOVA with skill level as a between subjects factor. There was a significant effect of
skill level on upper body execution variability, F (1, 28) = 9.22, p < .01. The skilled
participants were statistically less variable than the novice participants on measures of
upper-body variability. However, there was not a significant effect of skill level on lower
body execution variability, F (1, 28) = .463, p > .05. Thus, as predicted skilled participants
exhibited lower degrees of upper-body execution variability than novices during occluded
spare trials. However, there were no significant differences with regard to lower-body
execution variability. Sample direct comparisons between normal and occluded spare
conditions are presented in Figures 13 (skilled participant) and 14 (novice participant).
However, this consistency of upper-body execution variability for skilled performers did
52
not necessarily translate into success rate during the occluded conditions. Possible
explanations for this unexpected finding will be discussed below.
Table 5
Mean Variability (cm) for Each Marker by Skill Level and Marker Item Loadings on the
First and Second Components during Occluded Spare Trials from Principal Component
Analysis
Marker
Skilled
Novice
Factor Loading
Component 1 Component 2
WRT
6.83 (2.53)
18.22 (13.58) **
.988
-.135
ARM
6.22 (2.48)
16.83 (13.56) **
.988
-.153
ELB
5.99 (2.47)
16.24 (13.51) **
.987
-.161
UPA
5.93 (2.59)
16.06 (13.77) **
.985
-.172
SHO
5.77 (2.81)
15.81 (14.12) **
.978
-.193
FF
8.96 (4.21)
32.97 (47.62) **
.408
.905
BF
6.13 (3.00)
30.28 (36.15) **
.507
.852
** p <.01, * p < .05
53
Figure 12: Scree Plot from the Principal Component Analysis of markers during occluded
spare conditions.
54
Figure 13: Marker traces for a skilled participant during a spare trial during normal (top)
and occluded (bottom) conditions
55
Figure 14: Marker traces for a novice participant during a spare trial during normal (top)
and occluded (bottom) conditions
56
Execution variability: subsequent strikes. As with the spare trials, all consistency
measures for each of the markers (WRT, ARM, ELB, UPA, SHO, FF, and BF) were
analyzed individually with a mixed model ANOVA with skill level as a between subjects
factor and visual condition as a within subjects factor. Again, because of the overall trend
of similar findings from the analyses for each marker (Table 6), the data were analyzed by
means of a principal component analysis. Based on an examination of the pattern of
Eigenvalues, shown in the Scree Plot in Figure 15, only the first component was extracted.
It had high factor loadings on all seven of the markers and an Eigenvalue of 6.28 and
explained 89.76% of the variance. The factor loadings on the first component for strike
trials can be found in Table 6.
Table 6
Mean Variability (cm) for Each Marker by Skill Level and Marker Item Loadings on the
First Component during Occluded Strike Trials from Principal Component Analysis
Marker
Skilled
Novice
Factor
Loading
WRT
6.96 (3.17)
20.75 (10.97) **
.985
ARM
6.17 (3.01)
20.01 (11.39) **
.989
ELB
5.99 (3.18)
19.51 (11.56) **
.986
UPA
5.95 (3.24)
19.21 (11.72) **
.986
SHO
9.37 (6.38)
19.91 (11.56) **
.978
FF
11.00 (5.59)
22.29 (19.23) **
.851
BF
7.24 (3.64)
12.51 (7.38) **
.842
** p <.01, * p < .05
57
Because of components identified in the principal component analysis, a single
combined measure representing execution variability during occluded strike trials was
calculated. This single combined marker (component score) was analyzed via a one-way
ANOVA with skill level as a between subjects factor. There was a significant effect of
skill level on execution variability, F (1, 28) = 16.27, p < .01. The skilled participants were
statistically less variable than the novice participants. Thus, as predicted skilled
participants exhibited lower degrees of execution variability than novices during occluded
strike trials. Moreover, the results of an analysis of the execution variability of skilled and
novice participants during occluded conditions revealed that, like success rate, skilled
participants maintained their performance advantage over novice participants following
occlusion. Sample direct comparisons between normal and occluded strike conditions are
presented in Figures 16 (skilled participant) and 17 (novice participant).
Correlations between Participants’ Success Rate and Execution Variability
As hypothesized, execution variability and success rate were found in Chapter 4 to be
negatively correlated during spare and strike trials under normal conditions. Because of the
proposed internalized nature of mental representations, it was proposed that both low
levels of execution variability and high success rates would be maintained following
environmental occlusion. As predicted, execution variability remained low for both spare
and strike trials during occluded conditions, but surprisingly the advantage of the skilled
participants with regard to success rate was not maintained during occluded spare trials. In
this section, the degree to which success rate and execution variability during occluded
conditions are correlated with one another and with skill, is explored.
Spares. A Spearman rho correlation coefficient was calculated for the relationship
between success rate and upper- and lower-body execution variability (Table 7). Success
rate was negatively correlated with upper body execution variability in the occluded spare
conditions (rs= -.396, N = 30, p < .05, two tailed). Moreover, there was a significant
correlation between skill level and upper body execution variability (rs=.-666, N = 30, p <
.01, two tailed). There were no significant correlations with regard to lower-body
execution variability.
Strikes. For occluded strike trials, there was again a significant correlation between
combined execution variability and skill level (rs= .513, N = 30, p < .01, two tailed). The
58
significant negative correlation between execution variability and skill level (rs= -.697, N =
30, p < .01, two tailed) was also found for occluded strike trials (Table 8). There were no
other significant correlations for occluded strike trials.
Table 7
Correlations between Execution Variability, Success Rate, and Skill-Level during
Occluded Spare Trials
Execution Variability
Success Rate
Skill Level
Upper Body
Success Rate
1.000
Skill Level
.228
1.000
Upper Body
-.396*
-.666**
1.000
Lower Body
.217
.312
-.353
Lower Body
1.000
** p <.01, * p < .05
Section Summary
In the present study, environmental information was removed (or reduced) prior to task
performance during occluded conditions. It was hypothesized that, as compared to novice
participants, skilled performers would experience less disruption of performance, as
measured by success rate and execution variability, because of internalized mental
representations of task performance. With regard to success rate on occluded spare trials,
contrary to predictions, skilled performers did not maintain a significant performance
advantage over novice participants (30.7% versus 25.3% successful). However, as
predicted, skilled performers maintained a performance advantage over novice participants
during occluded strike trials (48.3% versus 28.7% successful).
Following principal component analyses, the initial marker variables (WRT, ARM,
ELB, UPA, SHO, FF, and BF) were subsequently reduced into two combined variability
scores representing the upper body (WRT, ARM, ELB, UPA, SHO) and lower body (FF,
59
BF) for occluded spare trials. As predicted, skilled performers were significantly less
variable than novice participants during occluded spare trials with regard to upper-body
execution variability. However, unexpectedly there was not a difference between skill
groups with regard to lower-body execution variability. Moreover, following principal
component analyses, the initial marker variables were reduced into a single component
score for occluded strike trials. As predicted, skilled performers were significantly less
variable than novice participants with regard to execution variability during occluded strike
trials.
Table 8
Correlations between Execution Variability, Success Rate, and Skill-Level during
Occluded Strike Trials
Success
Rate
Execution
Variability
Success Rate
1.000
Execution Variability
-.240
1.000
Skill Level
.513**
-.697**
Skill
Level
1.000
** p <.01, * p < .05
Spearman rho correlation coefficients were calculated to examine the relationship
between skill level, success rate and execution variability for spare and strike execution
variability measures during occluded conditions. Success rate was negatively correlated
with upper body execution variability during occluded spare trials (rs= -.666, N = 30, p <
.01, two tailed) but not lower body execution variability. In addition, despite skill level
being significantly correlated with success rate (rs= .513, N = 30, p < .01, two tailed) and
execution variability (rs= -.697, N = 30, p < .01, two tailed) during occluded strike trials,
there was not a significant relationship between execution variability and success rate.
Thus, despite maintaining low levels of execution variability during both occluded spare
and strike trials, the performance advantage of skilled participants associated with success
60
rate was diminished during strike trials and was completely lost during spare trials. In the
following chapter, a new method of analysis is introduced in an attempt to determine why
despite execution variability remaining low, a significant relationship between execution
variability and success rate was not found for occluded conditions.
Figure 15: Scree Plot from the Principal Component Analysis of markers during occluded
strike conditions.
61
Figure 16: Marker traces for a skilled participant during a strike trial during normal (top)
and occluded (bottom) conditions
62
Figure 17: Marker traces for a novice participant during a strike trial during normal (top)
and occluded (bottom) conditions
63
CHAPTER SIX
A MORE REFINED ANALYSIS
The estimates of variability described and analyzed in Chapters 4 and 5 (differences
between movement trajectories on subsequent trials) estimated the execution variability of
spare or strike trials separately for both the normal and occluded conditions. These
variability measures represented an aggregated measure of differences for consecutive
trials for the very large number of video frames. However, these difference measures are
by definition relative and cannot be used to assess whether the variability was larger in the
beginning of the approach, during the middle of the bowling movement or toward the
release of the bowling ball. Moreover, the subsequent scores provided a measure of
variability from one trial to the next, rather than a measure of variability of a single trial
from the average position of the combined trial. Furthermore, the subsequent scores were
calculated by comparing the location of a marker for a given frame (e.g., 3 frames from
crossing the foul line) from one trial to the next. This measure does not focus on the
critical events, such as the release of the bowling ball. If one were to measure the objective
location of the limbs at discrete meaningful points during the bowling act it would be
possible to make direct comparisons of the location of limbs in normal and occluded
conditions. It would also be possible to study differences in the movement speed and step
length by identifying the time and location of the placement of the foot and the associated
limb positions at that time
Variability at Three Distinct Events during the Bowling Action
In this new approach the final three steps for each participant was identified and used
to break down the approach into three events (Figure 18). The first two events were
defined by marking the position of the participants’ heel at contact with the floor, prior to
the first downward motion of the toe for the first two steps (STEP 1, STEP 2) and the
position of the heel during hand release (HR) during the final step (STEP 3). At each of the
three events, the location of the wrist, arm, elbow, upper-arm, and shoulder markers was
recorded. The variability score was then calculated by the difference in the position of the
marker on an individual frame associated with the event and the mean position of that
64
marker for all five trials of the same type (Figure 19), namely for spare and strike trials for
both normal and occluded conditions. For example, the variability score for the wrist
marker for a given participant during the occluded spare condition was calculated by
averaging the distance of the wrist marker for each of the five individual occluded spare
trials from the mean position of the wrist marker averaged over the five occluded spare
trials.
Figure 18: Representation of the events created by the final three steps of the participant.
Variability score: spares in Normal Condition. All Variability Score measures were
analyzed individually with a mixed model ANOVA with skill level as a between subjects
factor and visual condition as a within subjects factor. Again, because of the overall trend
of similar findings from the analyses for each marker (Table 9), the data were analyzed by
means of a principal component analysis.
65
For variability scores during normal spare conditions, two components with
Eigenvalues greater than 1.0 and differing from the values of the remaining components
were found as can be seen in the Scree Plot in Figure 20. The first component was
comprised of all eighteen of the measures with an Eigenvalue of 15.15 and explained
84.17% of the variance. The second component had an Eigenvalue of 1.96 and explained
10.88% of the variance with moderate loadings on several variables. The factor loadings
on the two components from normal spare trials can be found in Table 9.
There was a significant effect of skill level on the first component score of combined
execution variability during normal spare conditions, which comprised all the markers at
all the events, F (1, 26) = 30.32, p < .01. Consistent with the measures reported above, the
skilled participants were statistically less variable than the novice participants. For the
second component of combined execution variability, which was moderately loaded with
all the markers for the third event, there was not a significant effect of skill level, F (1, 26)
= .344, p > .05.
Execution variability, as measured by the first component score, successfully predicted
success rate during spare trials, accounting for 51.0% of the variance, F (1, 26) = 27.05, p
< .01. Moreover, participants’ house average (an indicator of skill) accounted for an
additional 18.3% of the variance, Fchange (1, 25) = 14.95, p < .01. Thus, execution
variability based on the three events is a more refined analysis accounting for more
variance associated with success rate than the subsequent trial analysis described above,
which accounted for 18.6% of the variance.
Variability score: spares in Occluded Condition. The analyses of the individual
measures showed a very similar pattern (Table 10). The results of the principal component
analysis were similar for variability scores during occluded spare conditions, as two
components with Eigenvalues greater than 1.0 and differing from the values of the
remaining components were found as can be seen from the Scree Plot in Figure 21. The
first component was comprised of all eighteen of the measures with an eigenvalue of 14.85
and explained 82.49% of the variance. The second component had an Eigenvalue of 1.84
and explained 10.20% of the variance and loaded on several of the variables. The marker
loadings on the two factors related to cluster scores during normal spare trials can be found
in Table 10.
66
There was a significant effect of skill level on the first component score of combined
execution variability during occluded spare conditions, which comprised all the markers at
all the events, F (1, 26) = 8.25, p < .01. Consistent with the measures reported above, the
skilled participants were statistically less variable than the novice participants. For the
second component of combined execution variability, which was moderately loaded with
all the markers for the third event, there was not a significant effect of skill level, F (1, 26)
= .119, p > .05.
1
2 Average 3
Position of
Marker 5
4
Figure 19: Representation of the components used in calculating the variability scores.
Variability score: strikes in Normal Condition. All Variability Score measures were
analyzed individually with a mixed model ANOVA with skill level as a between subjects
factor and visual condition as a within subjects factor. Again, because of the overall trend
of similar findings from the analyses for each marker (Table 11), the data were analyzed by
means of a principal component analysis.
Based on an examination of the pattern of Eigenvalues, shown in the Scree Plot in
Figure 22, only the first two components were extracted. The first component had high
factor loadings on all eighteen of the measures, an Eigenvalue of 14.12 and explained
78.46% of the variance. The second component had moderate factor loadings on several
measures, an Eigenvalue of 2.12 and explained 11.78% of the variance. The factor
loadings on the first and second component for cluster scores during normal strike trials
can be found in Table 11.
67
Table 9
Mean Variability (cm) of Each Marker at the Three Events and Marker Item Loadings
Related to Variability Scores on a Single Factor during Normal Spare Trials from
Principal Component Analysis
Novice
Factor Loading
Component 1 Component 2
Marker
Event
Skilled
WRT
1
2
3
2.67 (1.01)
3.57 (1.22)
4.72 (2.08)
10.25 (3.99) **
10.44 (5.45) **
9.57 (5.69) **
.954
.929
.819
-.168
-.281
.529
ARM
1
2
3
2.37 (0.78)
3.39 (1.06)
3.69 (1.76)
9.32 (4.03) **
8.76 (4.27) **
9.02 (5.26) **
.965
.922
.849
-.173
-.331
.508
ELB
1
2
3
2.35 (0.67)
3.26 (1.13)
3.47 (1.63)
9.06 (4.08) **
8.23 (3.79) **
8.92 (5.21) **
.970
.925
.857
-.160
-.318
.506
UPA
1
2
3
2.39 (0.70)
3.30 (1.09)
3.11 (1.43)
8.99 (4.23) **
7.95 (3.62) **
9.07 (5.68) **
.975
.948
.875
-.143
-.241
.475
SHO
1
2
3
2.24 (0.56)
3.11 (0.82)
2.85 (1.39)
9.03 (4.66) **
7.76 (3.81) **
8.89 (5.70) **
.956
.963
.908
-.160
-.146
.388
STEP
1
2
3
2.19 (0.92)
2.51 (0.91)
2.62 (1.5)
7.88 (3.37) **
8.55 (4.18) **
7.63 (6.05) **
.948
.889
.839
-.226
-.231
.429
** p <.01, * p < .05
There was a significant effect of skill level on the first component score of combined
execution variability during normal strike conditions, which comprised all the markers at
all the events, F (1, 26) = 20.29, p < .01. Consistent with the measures reported above, the
skilled participants were statistically less variable than the novice participants. For the
second component of combined execution variability, which was moderately loaded with
all the markers for the third event and several markers during the first event, there was not
a significant effect of skill level, F (1, 26) = .978, p > .05.
68
Figure 20: Scree Plot from the Principal Component Analysis of variability scores during
normal spare conditions.
Execution variability, as measured by the first component score, also successfully
predicted success rate during strike trials, accounting for 41.6% of the variance, F (1, 26) =
20.26, p < .01. Moreover, participants’ house average (an indicator of skill) accounted for
an additional 19.0% of the variance, Fchange (1, 25) = 12.74, p < .01. Thus, the amount of
variance accounted for by an analysis of execution variability based the three events as
associated with success rate was similar to the subsequent trial analysis described above,
which accounted for 40.7% of the variance.
69
Table 10
Mean Variability (cm) of Each Marker at the Three Events and Marker Item Loadings
Related to Variability Scores on a Single Factor during Occluded Spare Trials from
Principal Component Analysis
Novice
Factor Loading
Component 1 Component 2
Marker
Event
Skilled
WRT
1
2
3
3.98 (1.59)
5.85 (2.94)
9.09 (3.79)
12.43 (9.74) **
12.62 (7.52) **
15.83 (16.93)
.926
.910
.846
-.227
-.053
.465
ARM
1
2
3
3.82 (1.56)
5.59 (2.72)
8.51 (3.53)
10.92 (8.91) **
11.36 (6.93) **
15.88 (17.27)
.915
.948
.862
-.173
-.064
.469
ELB
1
2
3
3.89 (1.52)
5.42 (2.66)
8.25 (3.45)
10.38 (8.49) **
10.54 (6.45) **
15.93 (17.20)
.912
.973
.874
-.342
-.091
.461
UPA
1
2
3
3.99 (1.53)
5.36 (2.57)
8.20 (3.63)
10.17 (8.14) **
10.23 (6.40) **
16.20 (17.38)
.916
.977
.878
-.351
-.112
.449
SHO
1
2
3
4.13 (1.85)
5.14 (2.66)
7.62 (3.67)
9.21 (7.84) **
10.03 (6.51) **
16.68 (17.19)
.893
.975
.864
-.373
-.086
.426
STEP
1
2
3
3.87 (1.83)
5.40 (2.62)
7.53 (3.78)
8.81 (8.09) *
10.58 (6.98) *
19.16 (18.71) *
.882
.954
.822
-.411
.027
.273
** p <.01, * p < .05
70
Figure 21: Scree Plot from the Principal Component Analysis of variability scores during
occluded spare conditions.
Variability score: strikes in Occluded Condition. The analyses of the individual
measures showed a very similar pattern (Table 12). Based on an examination of the pattern
of Eigenvalues, shown in the Scree Plot in Figure 23, only the first two components were
extracted. The first component had high factor loadings on all eighteen of the measures, an
Eigenvalue of 12.53 and explained 69.59% of the variance. The second component had
moderate factor loadings on several measures, an Eigenvalue of 2.59 and explained
14.40% of the variance. The marker loadings on the two factors related to cluster scores
during occluded strike trials can be found in Table 12.
There was a significant effect of skill level on the first component score of combined
execution variability during normal strike conditions, which comprised all the markers at
71
all the events, F (1, 26) = 19.46, p < .01. Consistent with the measures reported above, the
skilled participants were statistically less variable than the novice participants. For the
second component of combined execution variability, which was moderately loaded with
all the markers for the first and third event, there was not a significant effect of skill level,
F (1, 26) = .978, p > .05.
Table 11
Mean Variability (cm) of Each Marker at the Three Events and Marker Item Loadings
Related to Variability Scores on a Single Factor during Normal Strike Trials from
Principal Component Analysis
Novice
Factor Loading
Component 1 Component 2
Marker
Event
Skilled
WRT
1
2
3
2.90 (2.44)
3.71 (1.83)
4.54 (1.52)
10.09 (7.39) **
11.26 (7.28) **
9.74 (5.14) **
.930
.907
.853
-.220
-.229
.443
ARM
1
2
3
2.69 (2.22)
3.61 (1.62)
3.83 (1.27)
9.15 (6.76) **
10.08 (6.34) **
9.32 (4.82) **
.941
.937
.873
-.274
-.227
.456
ELB
1
2
3
2.74 (2.13)
3.44 (1.56)
3.49 (1.18)
8.70 (6.43) **
9.64 (6.12) **
9.33 (4.99) **
.932
.953
.869
-.304
-.195
.474
UPA
1
2
3
2.70 (1.93)
3.32 (1.61)
3.43 (1.18)
8.43 (6.33) **
9.04 (5.89) **
9.20 (4.77) **
.927
.962
.865
-.308
-.200
.489
SHO
1
2
3
2.81 (1.76)
2.92 (1.57)
2.89 (1.11)
8.26 (6.74) **
8.39 (5.22) **
9.33 (4.89) **
.897
.968
.852
-.312
-.121
.487
STEP
1
2
3
3.40 (4.17)
2.62 (1.69)
3.58 (2.65)
10.89 (8.71) **
8.37 (4.34) **
9.64 (8.71) **
.655
.210
.609
-.382
.171
.483
** p <.01, * p < .05
72
Figure 22: Scree Plot from the Principal Component Analysis of variability scores during
normal strike conditions.
73
Table 12
Mean Variability (cm) of Each Marker at the Three Events and Marker Item Loadings
Related to Variability Scores on a Single Factor during Occluded Strike Trials from
Principal Component Analysis
Novice
Factor Loading
Component 1 Component 2
Marker
Event
Skilled
WRT
1
2
3
4.44 (2.04)
6.40 (2.89)
8.26 (3.72)
11.35 (6.47) **
16.55 (9.33) **
18.24 (13.62) **
.851
.820
.818
.386
.249
-.485
ARM
1
2
3
4.26 (2.22)
7.86 (3.26)
7.86 (3.26)
9.60 (5.28) **
13.96 (6.63) **
18.09 (13.64) **
.848
.907
.833
.423
.207
-.491
ELB
1
2
3
4.18 (2.21)
5.97 (2.89)
7.88 (3.41)
9.12 (4.98) **
12.18 (5.45) **
17.99 (13.64) **
.814
.945
.833
.452
.132
-.512
UPA
1
2
3
4.30 (2.29)
6.03 (2.92)
7.69 (3.54)
8.80 (4.64) **
10.89 (4.49) **
18.13 (13.93) **
.786
.943
.835
.507
.056
-.525
SHO
1
2
3
4.31 (2.44)
5.90 (2.97)
7.38 (3.82)
10.94 (11.11) *
10.47 (3.79) **
18.09 (13.45) **
.698
.899
.845
.415
-.056
-.482
STEP
1
2
3
5.73 (4.26)
6.03 (3.14)
8.44 (4.20)
8.90 (4.01)
10.55 (4.44) **
15.64 (12.05) *
.633
.837
.813
.338
-.135
-.351
** p <.01, * p < .05
74
Figure 23: Scree Plot from the Principal Component Analysis of variability scores during
occluded strike conditions.
Correlations between Participants’ Success Rate and Execution Variability at the Three
Events
In this section the correlations between success rate and execution variability at each of
the three events (based on the component scores) will be presented. Moreover, as with the
previous measures of execution variability, the relationship between skill level and each of
these measures will also be presented.
Spares during Normal Condition. For normal spare trials, there was a significant
negative correlation between the first combined execution variability component and
success rate (rs= -.791, N = 28, p < .01, two tailed). A significant negative correlation
75
between the first combined execution variability component and skill level (rs= -.858, N =
28, p < .01, two tailed) was also found (Table 13). There were no significant correlations
associated with the second combined execution variability component for normal spare
trials.
Table 13
Correlations between Execution Variability at the Three Events, Success Rate, and SkillLevel during Normal Spare Trials
Execution Variability Component
Success Rate
Skill Level
1
Success Rate
1.000
Skill Level
.848**
1.000
Component 1
-.791**
-.858**
1.000
Component 2
.203
.106
-.053
2
1.000
** p <.01, * p < .05
Spares during Occluded Condition. For occluded spare trials, there was a significant
negative correlation between the first combined execution variability component and skill
level (rs= -.566, N = 28, p < .01, two tailed). There were no other significant correlations
for occluded spare trials (Table 14).
Strikes during Normal Condition. For normal strike trials, there was a significant
negative correlation between the first combined execution variability component and
success rate (rs= -.721, N = 28, p < .01, two tailed). A significant negative correlation
between the first combined execution variability component and skill level (rs= -.778, N =
28, p < .01, two tailed) was also found (Table 15). There were no significant correlations
associated with the second combined execution variability component for normal strike
trials.
76
Strikes during Occluded Condition. For occluded strike trials, there was a significant
negative correlation between the first combined execution variability component and skill
level (rs= -.663, N = 28, p < .01, two tailed). There were no other significant correlations
for occluded strike trials (Table 16).
Table 14
Correlations between Execution Variability at the Three Events, Success Rate, and SkillLevel during Occluded Spare Trials
Execution Variability Component
Success Rate
Skill Level
1
Success Rate
1.000
Skill Level
.228
1.000
Component 1
-.171
-.566**
1.000
Component 2
-.265
.071
.251
2
1.000
** p <.01, * p < .05
Table 15
Correlations between Execution Variability at the Three Events, Success Rate, and SkillLevel during Normal Strike Trials
Execution Variability Component
Success Rate
Skill Level
1
Success Rate
1.000
Skill Level
.780**
1.000
Component 1
-.721**
-.778**
1.000
Component 2
-.140
-.274
.190
** p <.01, * p < .05
77
2
1.000
Table 16
Correlations between Execution Variability at the Three Events, Success Rate, and SkillLevel during Occluded Strike Trials
Execution Variability Component
Success Rate
Skill Level
1
Success Rate
1.000
Skill Level
.513**
1.000
Component 1
-.186
-.663**
1.000
Component 2
-.075
-.009
-.106
2
1.000
** p <.01, * p < .05
Variability at the Three Distinct Events Combined Across Normal and Occluded
Conditions
The analyses reported above indicate that the skilled participants were more consistent
than novices on a single component loaded with all markers. This single component was
negatively correlated with skill level for normal and occluded conditions during both spare
and strike trials. However, this single component was negatively correlated with success
rate during normal conditions only. In this section, execution variability at each of the
three stages for both normal and occluded conditions will be examined separately for both
spare trials and strike trials.
Spares. Based on an examination of the pattern of Eigenvalues, shown in the Scree Plot
in Figure 24, only the first three components were extracted. The first component had high
factor loadings on all thirty-six of the measures, an Eigenvalue of 23.24 and explained
64.54% of the variance. The second component had moderate factor loadings on several
measures, an Eigenvalue of 7.16 and explained 19.88% of the variance. The third
component had moderate factor loadings on the third event, an Eigenvalue of 2.19 and
explained 6.10% of the variance. The marker loadings on these three factors related to
variability scores during spare trials can be found in Table 17.
78
There was a significant effect of skill level on the first component score of combined
execution variability during normal strike conditions, which comprised all the markers at
all the events, F (1, 26) = 24.56, p < .01. Consistent with the measures reported above, the
skilled participants were statistically more consistent than the novice participants. There
were no significant differences between skilled and novice participants for the second, F
(1, 26) = 1.30, p > .05, or third, F (1, 26) = .834, p > .05, components of combined
execution variability.
The component score of execution variability of spare trials combined across normal
and occluded conditions exhibited relationships to success rate similar to the correlations,
based on separate analyses of normal and occluded conditions, presented above. There was
a significant negative correlation between the first combined execution variability
component and skill level (rs= -.787, N = 28, p < .01, two tailed) and success rate during
normal spare conditions (rs= -.642, N = 28, p < .01, two tailed). There were no other
significant correlations associated with the component scores derived from execution
variability combined across normal and occluded conditions.
79
Figure 24: Scree Plot from the Principal Component Analysis of variability scores during
combined occluded and normal spare trials.
80
Table 17
Marker Item Loadings Related to Variability Scores on a Single Factor during Spare
Trials from Principal Component Analysis
Marker
WRT
Event
Component 1
Component 2
Component 3
1
2
3
1
2
3
.855
.876
.706
.768
.811
.768
-.420
-.325
-.438
.536
.423
.314
-.172
-.267
.481
-.040
-.063
.377
ARM
1
2
3
.856
.874
.717
-.435
-.304
-.473
-.191
-.324
.434
Occluded
1
2
3
.728
.830
.802
.590
.467
.288
-.036
-.068
.345
ELB
1
2
3
1
2
3
.850
.869
.721
.726
.838
.823
-.455
-.317
-.483
.587
.505
.279
-.190
-.321
.434
-.023
-.062
.303
1
2
3
1
2
3
.851
.880
.724
.742
.826
.836
-.468
-.352
-.519
.567
.533
.268
-.171
-.243
.404
-.013
-.062
.246
1
2
3
1
2
3
.832
.868
.758
.709
.828
.861
-.467
-.408
-.521
.577
.525
.205
-.171
-.157
.321
.012
-.069
.126
1
2
3
1
2
3
.818
.813
.698
.696
.847
.838
-.463
-.341
-.497
.581
.444
.187
-.278
-.281
.383
.016
-.049
-.192
Occluded
Occluded
UPA
Occluded
SHO
Occluded
STEP
Occluded
** p <.01, * p < .05
81
Strikes. Based on an examination of the pattern of Eigenvalues, shown in the Scree Plot
in Figure 25, only the first three components were extracted. The first component had high
factor loadings on all thirty-six of the measures, an Eigenvalue of 24.23 and explained
67.29% of the variance. The second component had moderate to high factor loadings on
the third event of the occluded trials, an Eigenvalue of 3.71 and explained 10.29% of the
variance. The third component had moderate to high factor loadings on the first and third
event, an Eigenvalue of 2.92 and explained 8.09% of the variance. The marker loadings on
these three factors related to variability scores during spare trials can be found in Table 18.
There was a significant effect of skill level on the first component score of combined
execution variability during normal strike conditions, which comprised all the markers at
all the events, F (1, 26) = 23.45, p < .01. Consistent with the measures reported above, the
skilled participants were statistically more consistent than the novice participants. Again
there were no significant differences between skilled and novice participants for the
second, F (1, 26) = 0.38, p > .05, or third, F (1, 26) = .315, p > .05, components of
combined execution variability.
As with spare trials, the component score of execution variability of strike trials
combined across normal and occluded conditions exhibited relationships to success rate
similar to the correlations, based on separate analyses of normal and occluded conditions,
presented above. There was a significant negative correlation between the first combined
execution variability component and skill level (rs= -.734, N = 28, p < .01, two tailed) and
success rate during normal spare conditions (rs= -.685, N = 28, p < .01, two tailed). There
were no other significant correlations associated with the component scores derived from
execution variability combined across normal and occluded conditions.
82
Figure 25: Scree Plot from the Principal Component Analysis of variability scores during
combined occluded and normal strike trials.
83
Table 18
Marker Item Loadings Related to Variability Scores on a Single Factor during Strike
Trials from Principal Component Analysis
Marker
WRT
Event
Component 1
Component 2
Component 3
1
2
3
1
2
3
.931
.862
.776
.809
.833
.695
-.195
-.352
-.073
-.092
-.016
.619
-.037
-.046
-.538
.382
.033
.189
ARM
1
2
3
.939
.900
.815
-.222
-.300
.024
.075
-.038
-.529
Occluded
1
2
3
.798
.930
.709
-.133
-.003
.630
.471
.031
.183
ELB
1
2
3
1
2
3
.930
.900
.799
.766
.958
.703
-.230
-.320
.024
-.171
.043
.654
.099
-.088
-.564
.504
.071
.181
1
2
3
1
2
3
.916
.922
.821
.765
.936
.700
-.262
-.258
.114
-.246
.098
.676
.093
-.061
-.531
.453
.143
.158
1
2
3
1
2
3
.875
.933
.817
.730
.860
.710
-.310
-.165
.138
-.231
.230
.648
.086
-.110
-.512
.223
.101
.169
1
2
3
1
2
3
.598
.896
.563
.673
.816
.722
-.476
.047
.123
-.221
.258
.522
.041
-.287
-.497
.144
-.023
-.004
Occluded
Occluded
UPA
Occluded
SHO
Occluded
STEP
Occluded
** p <.01, * p < .05
84
The Difference in Mean Stopping Points between Normal and Occluded Conditions
The results presented above indicate that despite maintaining low levels of execution
variability for both normal and occluded trials, skilled participants experienced reduced
success rates in occluded condition as compared to normal condition. Although the
performance advantage of skilled participants was maintained in the strike trials, skilled
participants experienced this decrease in success rate during both spare and strike trials.
Thus, factors other than execution variability, as measured thus far, are responsible for the
decrease in success rate. In this section, based on the loadings of the markers at the third
occluded event (primarily spare trials) and multiple statements regarding participants’ fear
of crossing the foul line, the degree to which participants stop short during occluded
conditions, as compared to normal conditions, is explored.
Stopping point: spare. For spare trials, both skilled and novice participants stopped
short during occluded conditions as compared to normal conditions (mean skilled
difference, -31.91 cm, SD = 21.05; mean novice difference, -26.04 cm, SD = 32.47).
Moreover, ANOVA revealed no significant differences between the skilled and novice
participants with regard to the mean difference in stopping point.
In addition, the results of repeated measures ANOVA revealed a significant effect of
visual condition on distance from the foul for both skilled (normal condition mean of 68.20
cm versus occluded condition mean of 97.51 cm) and novice participants (normal condition
mean of 90.49 cm versus occluded condition mean of 111.58 cm), F (1, 28) = 25.47, p <
.01.
Stopping point: strike. For strike trials, both skilled and novice participants again
stopped short during occluded conditions as compared to normal conditions (mean skilled
difference, -31.97 cm, SD = 19.84; mean novice difference, -25.99 cm, SD = 33.65). Once
again, ANOVA revealed no significant differences between the skilled and novice
participants with regard to the mean difference in stopping point.
In addition, the results of repeated measures ANOVA revealed a significant effect of
visual condition on distance from the foul for both skilled (normal condition mean of 60.01
cm versus occluded condition mean of 91.20 cm) and novice participants (normal condition
mean of 94.33 cm versus occluded condition mean of 114.64 cm), F (1, 28) = 21.90, p <
.01.
85
Figure 26: Example of a shortened approach in the occluded condition (bottom) as
compared to the normal condition (top).
Are Participants Scaling the Approach or Stopping Abruptly?
Because of the finding that participants were stopping at different points during
occluded conditions compared to normal conditions, ratio scores were calculated in an
attempt to determine if the approach exhibited during normal conditions had been
shortened by scaling down. For example, the writing of a letter from the alphabet on a
piece of paper can be significantly smaller than a letter written on a chalkboard but the
ratio among the features of the letter can be identical. Ratios were calculated by dividing
the average distance created between the location of the steps and shoulder at the first and
second events (distance 1) by the average distance created by the same markers between
the second and third events (distance 2).
86
Ratio Score: Step. All Ratio Score measures regarding the steps were analyzed with a
mixed model ANOVA with skill level as a between subjects factor and visual condition as
a within subjects factor. a) Spares: There was not a significant effect of skill level or
visual condition (p > .05). Both the skilled and novice participants maintained
approximately the same ratio between the normal (skilled ratio: 0.53, novice ratio: 0.63)
and occluded (skilled ratio: 0.56, novice ratio: 0.66) conditions. b) Strikes: There was not
a significant effect of skill level or visual condition (p > .05). Both the skilled and novice
participants maintained approximately the same ratio between the normal (skilled ratio:
0.48, novice ratio: 0.64) and occluded (skilled ratio: 0.48, novice ratio: 0.66) conditions.
Ratio Score: Shoulder. All Ratio Score measures regarding the shoulder were analyzed
with a mixed model ANOVA with skill level as a between subjects factor and visual
condition as a within subjects factor. a) Spares: There was not a significant effect of skill
level or visual condition (p > .05). Both the skilled and novice participants maintained
approximately the same ratio between the normal (skilled ratio: 0.54, novice ratio: 0.57)
and occluded (skilled ratio: 0.55, novice ratio: 0.59) conditions. b) Strikes: There was not
a significant effect of skill level or visual condition (p > .05). Both the skilled and novice
participants maintained approximately the same ratio between the normal (skilled ratio:
0.53, novice ratio: 0.57) and occluded (skilled ratio: 0.54, novice ratio: 0.57) conditions.
The Relationship between Stopping Short and Success Rate
Both skilled and novice participants stopped short during the approach of occluded
conditions as compared to normal conditions and participants are maintaining the same
ratio between events during the approaches of the normal and occluded conditions. There
were no significant differences between skill groups regarding the mean distance of
stopping short. In this section, the correlation between stopping short and success rate
during occluded conditions for both spare and strike trials is explored.
Spare. For spare trials, there was a significant correlation between the mean difference
in stopping point during the approach for occluded conditions compared to normal
conditions and success rate during occluded spare trials (rs= .584, N = 30, p < .01, two
tailed). Thus the closer the mean difference in stopping points was to zero (stopping short
was represented by negative values), the higher the success rate during occluded spare
trials. Moreover, when the variables were entered into a regression equation, stopping short
87
accounted for 31.7% of the variance of success rate during occluded spare trials, F (1, 28)
= 14.46, p < .01.
Strike. For strike trials, the correlation between the mean difference in stopping point
during the approach for occluded conditions compared to normal conditions and success
rate during occluded strike trials was not significant.
Why is the Scaled-down Approach More Disruptive for Spares Compared to Strikes?
As indicated in the section above, stopping short during occluded conditions is more
disruptive during spare trials than during strike trials. However, the question remains as to
why there is a greater disruption of success rate by stopping short during spare trials
compared to strike trials. Furthermore, the relationship between stopping point and success
rate during occluded spare trials is stronger than for occluded strike trials. A stronger look
at some of the unique characteristics of the spare trials may help explain this differential
effect of stopping short on success rate.
For the spare trials, the task requires the participant to knock down the ten-pin, which
is the right-most pin in the arrangement. For strike trials, the participants’ task is to knock
down as many of the ten pins as possible, which obviously span the width of the lane. If
participants (particularly skilled) are scaling down the approach and releasing at an earlier
point in the approach during occluded as compared to normal conditions, the ball path may
take a course that is identical to normal conditions but short of the pins to the degree that
the approach was shortened. Assuming that the ball takes an equivalent but shortened path
(relative to the pins) during spare trials, it would have no place to travel except into the
gutter. Conversely, a shortened path relative to the pins during a strike trial still has
potential to strike multiple pins, even if it misses the pocket. A comparison of the number
of gutter balls during the occluded spare trials as compared to the occluded strike trials
should indicate this differential effect of a shortening of the ball path relative to the pins
resulting from a scaled-down approach.
An analysis comparing the percentage of gutter balls of skilled participants during
occluded spare versus occluded strike trials confirms this differential effect of scaling the
approach by shortening the path of the ball relative to the pins. The results of a paired t-test
indicated a significantly higher percentage of gutter balls by skilled participants during the
occluded spare trials (45.33 %) than during the occluded strike trials (25.33%; t = 2.96, df
88
= 14, p <.01). Moreover, there was no such significant difference between the percentages
of gutter balls during normal spare trials (4.00%) compared to normal strike trials (1.33%).
The results of the analysis of the gutter balls during occluded spare trials versus occluded
strike trials offers a reasonable explanation regarding the more extreme effect on success
rate of scaling the approach during occluded spare trials as compared to occluded strike
trials.
In addition, the frequency of the amount of pins hit by skilled participants in the
normal compared to occluded strike trials is presented in Table 19. During normal strike
conditions, 78.6% of the attempts knocked down at least 8 of the pins (92.0% of the
attempts knocked down at least 7 of the pins). In contrast, during the occluded trials, only
30.67% of the attempts knocked down at least 8 of the pins and 40.0% of the attempts
knocked down between 1 and 7 of the pins. These findings are again consistent with the
argument that a shortened ball path, relative to the pins, is likely responsible for the
differential effect of occlusion during spare as compared to strike trials.
Section Summary
A more refined variability measure was created by identifying the final three steps for
each participant bowling action on normal versus occluded trials. The average deviation
from the mean for a given marker was calculated at each of the events (i.e., variability
scores). Initially, variability scores were separated by visual condition and reduced via a
principal component analysis. The results indicated that skilled participants were
significantly more consistent on a single component, loaded with all markers, than novice
participants during both normal and occluded conditions on both spare and strike trials. For
all conditions, the second component was loaded with each marker at the third event
(occluded strikes was also loaded with the markers at the first event) but there was not a
significant difference between skilled and novice participants. Despite the significant
difference between skill groups for the first component during normal and occluded
conditions on both spare and strike trials, this component was only significantly correlated
with success rate during normal conditions.
89
Table 19
The Frequency of the Amount of Pins Hit by Skilled Participants in the Normal Compared
to Occluded Strike Trials
Amount
Normal
Occluded
0
1
22
1
1
4
2
0
0
3
0
5
4
0
2
5
2
5
6
2
6
7
10
8
8
10
2
9
19
8
10
30
13
The variability at the three events was subsequently analyzed via principal component
analysis separately for spare and strike trials with normal and occluded conditions
combined. The primary findings were that a single component loaded with all markers
(normal and occluded) was found for both spare and strike trials. Moreover, there were
significant differences of skill level on this single component. Thus, low execution
variability was again associated with skill level and success rate during normal conditions,
but not success rate during occluded conditions.
To further investigate the reason for the reduced success rate for skilled participants
during occluded conditions, despite the low levels of execution variability, the degree to
90
which participants stopped short during occluded conditions, as compared to normal
conditions, was explored. There were no significant differences in skill with regard to
stopping short during occluded conditions as compared to normal conditions on spare trials
(mean skilled difference, -31.91 cm, SD = 21.05; mean novice difference, -26.04 cm, SD =
32.47) or strike trials (mean skilled difference, -31.97 cm, SD = 19.84; mean novice
difference, -25.99 cm, SD = 33.65). Moreover, comparisons of the participants’ distances
from the foul line for spare and strike trials indicated a significant effect of visual condition
on distance from the foul line for both skilled and novice participants.
To determine if the shortened approaches could be the result of participants’ scaling
down their approach, ratios were calculated by dividing the average distance between
event 1 and event 2 by the average distance between event 2 and event 3 for both the
shoulder marker and the location of the participants’ steps. Analysis of the ratios indicated
that there were no significant differences in the ratio of the approach between normal and
occluded conditions during spare or strike trials for either skilled or novice participants.
Thus, the approaches are potentially being scaled down during the approach for occluded
conditions.
The effect of the scaled-down approach had differential effects on the success rate of
occluded spare trials compared to occluded strike trials. The differences inherent in the
task requirements of a spare trial versus a strike trial in the present study were explored as
offering potential clues to this differential effect on success rate. If the ball path is
shortened, relative to the pins, to roughly the same degree as the participant is stopping
short, this effect should be revealed in an analysis of the gutter balls associated with each
occluded condition. Indeed, the results of a paired t-test indicated a significantly higher
percentage of gutter balls by skilled participants during the occluded spare trials (45.33 %)
than during the occluded strike trials (25.33%) that was not found in a comparison of the
percentage of gutter balls by skilled participants during normal spare (4.00%) and strike
trials (1.33%).
Engagement in deliberate practice activities is predicted to be responsible for the low
levels of execution variability of skilled participants observed under both normal and
occluded conditions of the present study. The engagement in deliberate practice is posited
to lead to refined representations of task. More specifically, refinements of task
91
requirements are proposed to lead to reduced, and eventually consistently low, levels of
execution variability. These consistently low levels of execution variability mediate
consistently high success rates. In the next chapter, Chapter 7, participants’ estimates of
engagement in bowling-related activities and their relation to success rate and lower
execution variability are explored.
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CHAPTER SEVEN
THE RELATIONSHIP BETWEEN PARTICIPANTS’ DELIBERATE PRACTICE
HISTORIES, SKILL-LEVEL, AND PERFORMANCE
In the introduction, the hypotheses of the alternative accounts were presented. The
expert-performance framework proposed that the improved performance of the expert
bowlers could be explained by their engagement in deliberate practice. In contrast the
ecological/dynamical systems framework does not have a clear prediction about which
types of training activities, if any, will be associated with superior bowling performance. In
this section, participants’ accumulated and current histories of bowling-related activities
are analyzed by estimating correlation coefficients to further investigate this proposed
relationship.
Method
The deliberate practice questionnaire (Appendix C) used in the present study was
adapted from Duffy, Baluch, and Ericsson (2004). Each participant was asked to estimate
how many hours they have previously (accumulated) or currently (per week) engage in the
following activities: a) playing in bowling leagues, b) playing for fun, c) playing in
competitions, d) engaging in solitary deliberate practice, and e) deliberate practice with
others.
Results
For each of the activities, the means of estimated engagement are presented in Table
20. A simple t-test showed that the expert participants had engaged in significantly more
deliberate practice. There were, however, several other activities where the expert had
engaged in substantially more activities especially when assessing the amount
retrospectively. T-tests for all the activities are also given in Table 20.
Performance for normal trials. Moreover, the degree to which these accumulated and
current practice activities are associated with participants’ success rates for spares and
strikes during normal conditions was investigated by calculation of a correlation matrix.
As shown in Table 21, engagement in deliberate practice (solitary and team) is
93
significantly correlated with success rate during normal conditions. However, other types
of activities, such as participation in bowling leagues and competitions, are also
significantly correlated with the success rate during normal conditions. To assess whether
these variables would explain performance above and beyond the measures of deliberate
practice a regression analysis was conducted. When predicting success rate during the
normal condition for strikes and spares respectively, there was no significant improvement
in prediction when the deliberate practice measures had been entered first.
The measures of accumulated and current solitary and group deliberate practice hours
were entered stepwise into the regression equation. Current hours engaged in solitary
deliberate practice most successfully predicted success rate during normal spare trials,
accounting for 55.7% of the variance, F (1, 27) = 36.16, p < .01. The additional measures
of accumulated and current hours of playing for fun, playing in competitions, and playing
in bowling leagues were subsequently entered into the analysis. Accumulated hours spent
in bowling leagues accounted for a significant amount of additional variance (15.4%),
Fchange (1, 26) = 14.58, p < .01, as did accumulated hours playing for fun (6.8%), Fchange (1,
26) = 8.30, p < .01. The measures of accumulated and current hours of playing in
competitions, current playing for fun, or current playing in bowling leagues did not
account for a significant amount of additional variance. Accumulated hours engaged in
solitary deliberate practice most successfully predicted success rate during normal strike
trials, accounting for 50.5% of the variance, F (1, 27) = 29.52, p < .01. The additional
measures of accumulated and current hours of playing for fun, playing in competitions, and
playing in bowling leagues did not account for a significant amount of additional variance.
Performance for occluded trials. The correlations between accumulated and current
practice activities and success rate during occluded trials are presented in Table 22. For
occluded strike trials, as predicted both the measures of accumulated and current hours
engaged in deliberate practice (solitary and team) were significantly correlated with
success rate during occluded strike trials. However, for the occluded spare trials, there
were no significant correlations with the four measures of deliberate practice.
The measures of accumulated and current solitary and group deliberate practice hours
were entered stepwise into the regression equation. Accumulated hours engaged in solitary
deliberate practice most successfully predicted success rate during occluded strike trials,
94
Table 20
Means from Skilled and Novice Participants’ Accumulated and Current Practice Histories
Skilled (SD)
Novice (SD)
Starting Age
11.03 (4.81)
9.53 (4.6)
Accumulated Hours
Bowling League
1950.71 (3333.67)
20.00 (77.46) **
For Fun
905.79 (1469.89)
75.40 (61.93) **
In Competitions
1047.57 (2611.95)
8.00 (25.69) *
Solitary Deliberate
Practice
1831.43 (3881.96)
2.33 (7.76) *
Team Deliberate
Practice
1590.93 (3917.52)
1.67 (3.62) *
Current Hours (Weekly)
Bowling League
5.20 (4.16)
0.00 (0.00) **
For Fun
3.80 (4.11)
2.33 (1.68) **
In Competitions
4.87 (4.14)
0.13 (0.52) **
Solitary Deliberate
Practice
12.13 (12.09)
0.13 (0.52) **
Team Deliberate
Practice
9.93 (12.71)
1.00 (2.59) *
** p <.01, * p < .05
accounting for 19.8% of the variance, F (1, 27) = 7.93, p < .01. None of the other three
variables measuring deliberate practice would explain significant additional variance. The
additional measures of other types of activities, such as accumulated and current hours of
playing for fun, playing in competitions, and playing in bowling leagues were
subsequently entered into the analysis. The additional measures of accumulated and current
hours of playing for fun, playing in competitions, and playing in bowling leagues did not
account for a significant amount of additional variance.
95
Prediction of execution variability. The degree to which accumulated and current
practice activities are associated with measures of execution variability was also explored.
Overall, measures of execution variability were negatively correlated with measures of
practice histories. The correlations between accumulated and current practice activities
and execution variability, as measured by the component scores derived from principal
component analysis, for normal spare trials are presented in Table 23. For normal spare
conditions, all measures with the exception of starting age and current hours playing for
fun are negatively correlated with execution variability. Regression analyses were
conducted to assess whether any other variable was able to explain execution variability
above and beyond the measures of deliberate practice. The measures of accumulated and
current solitary and group deliberate practice hours were entered stepwise into the
regression equation. Accumulated hours engaged in team deliberate practice most
successfully predicted execution variability during normal spare trials, accounting for
43.2% of the variance, F (1, 25) = 20.77, p < .01. None of the other three variables
measuring deliberate practice would explain significant additional variance. The additional
measures of other types of activities, such as accumulated and current hours of playing for
fun, playing in competitions, and playing in bowling leagues were examined to find
significant additional predictors of execution variability. The only additional measure
accounting for a significant amount of additional variance (10.1%) was accumulated hours
spent in bowling leagues, Fchange (1, 24) = 5.45, p < .05. The measures of accumulated and
current hours of playing for fun, playing in competitions, or current playing in bowling
leagues did not account for a significant amount of additional variance. Thus, only one
other variable explained a significant amount of variance once the deliberate practice
measures had been entered in the regression equation.
These correlations are presented for normal strike trials in Table 24. The results for
normal strike conditions were very similar to normal spare conditions as most accumulated
and current practice activities are negatively correlated with execution variability.
Regression analyses were conducted to assess whether any other variable was able to
explain execution variability for any of the three events above and beyond the measures of
deliberate practice. The measures of accumulated and current solitary and group deliberate
practice hours were entered stepwise into the regression equation. Accumulated hours
96
engaged in team deliberate practice most successfully predicted execution variability
during normal strike trials, accounting for 33.4% of the variance, F (1, 25) = 14.02, p <
.01. None of the other three variables measuring deliberate practice would explain
significant additional variance. The additional measures of other types of activities, such as
accumulated and current hours of playing for fun, playing in competitions, and playing in
bowling leagues were examined to find significant additional predictors of execution
variability. The only additional measures accounting for a significant amount of additional
variance (10.0%) was accumulated hours spent in bowling leagues, Fchange (1, 24) = 4.44, p
< .05. The measures of accumulated and current hours of playing for fun, playing in
competitions, and playing in bowling leagues did not account for a significant amount of
additional variance. Thus, only one other variable explained a significant amount of
variance once the deliberate practice measures had been entered in the regression equation.
The relationship between accumulated and current practice activities and execution
variability during occluded spare trials are presented in Table 25. In general, accumulated
deliberate practice hours were negatively correlated with execution variability. There was
generally no correlation between current practice hours and execution variability.
Regression analyses were conducted to assess whether any other variable was able to
explain execution variability for any of the three events above and beyond the measures of
accumulated deliberate practice. The measures of accumulated and current solitary and
group deliberate practice hours were entered stepwise into the regression equation.
Accumulated hours engaged in solitary deliberate practice most successfully predicted
execution variability during occluded spare trials, accounting for 19.4% of the variance, F
(1, 25) = 7.27, p < .05. None of the other three variables measuring deliberate practice
would explain significant additional variance. The additional measures of other types of
activities, such as accumulated and current hours of playing for fun, playing in
competitions, and playing in bowling leagues were examined to find significant additional
predictors of execution variability. No other variable explained a significant amount of
variance once the accumulated deliberate practice measures had been entered in the
regression equation.
The relationship between accumulated and current practice activities and execution
variability during occluded strike trials are presented in Table 26. The observed
97
relationships for occluded strike trials were very similar to the relationships observed in the
analysis of normal strike trials. In particular, there was negative correlation between both
accumulated and current deliberate practice and execution variability. Regression analyses
were conducted to assess whether any other variable was able to explain execution
variability for any of the three events above and beyond the measures of deliberate
practice. The measures of accumulated and current solitary and group deliberate practice
hours were entered stepwise into the regression equation. Accumulated hours engaged in
solitary deliberate practice most successfully predicted execution variability during
occluded strike trials, accounting for 30.5% of the variance, F (1, 25) = 12.39, p < .01.
None of the other three variables measuring deliberate practice would explain significant
additional variance. The additional measures of other types of activities, such as
accumulated and current hours of playing for fun, playing in competitions, and playing in
bowling leagues were examined to find significant additional predictors of execution
variability. The only additional measures accounting for a significant amount of additional
variance (12.6%) was accumulated hours spent in bowling leagues, Fchange (1, 24) = 5.60, p
< .05. The measures of accumulated and current hours of playing for fun, playing in
competitions, and playing in bowling leagues did not account for a significant amount of
additional variance. Thus, only one other variable explained a significant amount of
variance once the deliberate practice measures had been entered in the regression equation.
Thus, only one other variable explained a significant amount of variance once the
deliberate practice measures had been entered in the regression equation.
As a final step, the relationship between accumulated and current practice activities and
the difference in mean stopping point during normal and occluded conditions for the final
event was explored. However, there were no correlations between accumulated and
current practice hours and difference in mean stopping point during normal and occluded
conditions for either strikes or spares. This relationship is reflective of the finding reported
above of skilled participants maintaining consistency within both the normal and occluded
conditions but stopping at an earlier point during the approach of the occluded condition
when compared to the normal condition.
98
Do Hours Engaged in Deliberate Practice Activities Account for the Relation Between
Execution Variability and Success Rate?
Regression analyses were conducted to assess whether execution variability predicted
success rate above and beyond the measures of deliberate practice. When entered first,
execution variability accounted for 48.8% of the variance of predicting success rate during
normal spare trials, F (1, 25) = 25.87, p < .01. The measures of accumulated and current
solitary and group deliberate practice hours were then entered stepwise into the regression
equation. Current hours engaged in solitary deliberate practice most successfully predicted
success rate during normal spare trials, accounting for 52.9% of the variance, F (1, 25) =
30.21, p < .01. Execution variability accounted for a significant amount of additional
variance (12.2%), Fchange (1, 24) = 8.83, p < .01. Thus, the deliberate practice measures
solely accounted for the 36.6% of the variance. The results were similar for normal strike
trials, as when entered first, execution variability predicted success rate during normal
strike trials, accounting for 40.7% of the variance, F (1, 25) = 18.88, p < .01. The measures
of accumulated and current solitary and group deliberate practice hours were then entered
stepwise into the regression equation. Accumulated hours engaged in solitary deliberate
practice most successfully predicted success rate, accounting for 52.2% of the variance, F
(1, 25) = 27.33, p < .01. Execution variability accounted for a significant amount of
additional variance (7.7%), Fchange (1, 24) = 4.43, p < .05, but similar to normal spare trials,
deliberate practice measures solely accounted for 33.0% of the variance.
99
Table 21
Correlations between Accumulated and Current Practice Histories, House Average, Skill
Level and Success Rate during Normal Spare and Strike Trials
Average
Skill
Spare
Strike
0.12
0.21
0.11
0.24
Bowling League
0.85**
0.88**
0.78**
0.82**
For Fun
0.50**
0.43*
0.31
0.35
In Competitions
0.85**
0.80**
0.72**
0.73**
Solitary Deliberate
Practice
0.82**
0.89**
0.71**
0.79**
Team Deliberate
Practice
0.79**
0.87**
0.71**
0.74**
Bowling League
0.81**
0.83**
0.69**
0.79**
For Fun
0.06
0.04
-0.05
0.16
In Competitions
0.86**
0.79**
0.70**
0.64**
Solitary Deliberate
Practice
0.79**
0.90**
0.83**
0.71**
Team Deliberate
Practice
0.70**
0.79**
0.73**
0.70**
Starting Age
Accumulated Hours
Current Hours (Weekly)
** p <.01, * p < .05
100
Table 22
Correlations between Accumulated and Current Practice and Success Rate during
Occluded Spare and Strike Trials
Occluded Spare
Starting Age
Occluded Strike
0.17
- 0.01
Bowling League
0.20
0.34
For Fun
0.09
0.21
In Competitions
0.20
0.32
Solitary Deliberate
Practice
0.22
0.48**
Team Deliberate
Practice
0.19
0.38*
0.30
0.38*
- 0.25
0.16
Accumulated Hours
Current Hours (Weekly)
Bowling League
For Fun
In Competitions
0.38*
0.26
Solitary Deliberate
Practice
0.31
0.70**
Team Deliberate
Practice
0.27
0.37*
** p <.01, * p < .05
101
Table 23
Correlations between Accumulated and Current Practice Histories, and Execution
Variability (Component Score Loadings) on Normal Spare Trials
Component 1
Component 2
-0.28
0.19
Bowling League
-0.82**
0.21
For Fun
-0.46*
-0.52
In Competitions
-0.77**
0.14
Solitary Deliberate
Practice
-0.72**
0.10
Team Deliberate
Practice
-0.78**
0.01
Bowling League
-0.76**
0.04
For Fun
-0.01
-0.16
In Competitions
-0.73**
0.19
Solitary Deliberate
Practice
-0.75**
0.12
Team Deliberate
Practice
-0.66**
0.21
Starting Age
Accumulated Hours
Current Hours (Weekly)
** p <.01, * p < .05
102
Table 24
Correlations between Accumulated and Current Practice Histories, and Execution
Variability (Component Score Loadings) on Normal Strike Trials
Component 1
Component 2
-0.18
-0.24
Bowling League
-0.75**
-0.28
For Fun
-0.46*
-0.31
In Competitions
-0.65**
-0.26
Solitary Deliberate
Practice
-0.65**
-0.26
Team Deliberate
Practice
-0.59**
-0.42*
Bowling League
-0.59**
-0.26
For Fun
-0.33
0.14
In Competitions
-0.55**
-0.19
Solitary Deliberate
Practice
-0.69**
-0.26
Team Deliberate
Practice
-0.56**
-0.55
Starting Age
Accumulated Hours
Current Hours (Weekly)
** p <.01, * p < .05
103
Table 25
Correlations between Accumulated and Current Practice Histories, and Execution
Variability (Component Score Loadings) on Occluded Spare Trials
Component 1
Component 2
-0.01
-0.15
Bowling League
-0.60**
0.03
For Fun
-0.37
-0.72
In Competitions
-0.54**
-0.29
Solitary Deliberate
Practice
-0.50**
-0.04
Team Deliberate
Practice
-0.50**
-0.11
Bowling League
-0.40*
-0.04
For Fun
-0.13
-0.36
In Competitions
-0.41*
0.10
Solitary Deliberate
Practice
-0.36
0.02
Team Deliberate
Practice
-0.36
-0.28
Starting Age
Accumulated Hours
Current Hours (Weekly)
** p <.01, * p < .05
104
Table 26
Correlations between Accumulated and Current Practice Histories, and Execution
Variability (Component Score Loadings)) on Occluded Strike Trials
Component 1
Component 2
-0.04
-0.12
Bowling League
-0.66**
-0.03
For Fun
-0.44*
-0.12
In Competitions
-0.57**
-0.89
Solitary Deliberate
Practice
-0.55**
0.05
Team Deliberate
Practice
-0.60**
0.14
Bowling League
-0.48**
0.02
For Fun
-0.15
0.67
In Competitions
-0.54**
-0.17
Solitary Deliberate
Practice
-0.51**
0.26
Team Deliberate
Practice
-0.36
-0.28
Starting Age
Accumulated Hours
Current Hours (Weekly)
** p <.01, * p < .05
Section Summary
Skilled performance was predicted to be associated with higher levels of engagement in
deliberate practice activities through the development of refined representations of task
requirements. Furthermore, these refined representations of task performance were deemed
responsible by permitting reduced execution variability during bowling performance. As
predicted accumulated hours engaged in deliberate practice activities (individual and team)
was significantly correlated with success rate and execution variability (negatively) during
105
normal spare and strike trials. Estimated hours engaged in deliberate practice activities
were also significantly correlated with success rate and execution variability during
occluded spare (accumulated only) and strike trials. Moreover, regression analyses
revealed that accumulated and current hours engaged in solitary or team deliberate practice
accounted for the majority of the variance for success rate and execution variability in
normal and occluded spare and strike trials. As predicted, engagement in deliberate
practice activities solely accounted for variance regarding success rate beyond that of
execution variability and solely accounted for around 33% of the variance during both
normal spare and strike trials. As expected based on the results presented in Chapter 6,
there was no relationship between deliberate practice histories and stopping short during
the approach.
106
CHAPTER EIGHT
GENERAL DISCUSSION
The review of the research presented in the introduction introduced two approaches to
understanding task performance, in general, and skilled performance, in particular. The
expert performance approach and the ecological/dynamical systems approach were
introduced, along with each approach’s specific claims with regard to consistency during
task performance. For tasks involving motor performance, consistency of task performance
can be considered on at least three levels, execution variability, result variability, or
success rate.
Two of these components, execution variability and success rate, were considered in
the present study. In addition, the relationship between execution variability and success
rate and their relationship to skilled performance were explored with regard to the two
aforementioned theoretical approaches. While both approaches would agree that skilled
performers would achieve a high success rate, the two approaches diverge regarding
execution variability and the role of environmental information during task performance.
More specifically, proponents of the expert performance approach (Ericsson, 2002) argue
that on tasks for which consistency is desirable, skilled performers will exhibit less
execution variability over successive trials than novices. Some advocates of an
ecological/dynamical systems (Williams, David, Williams, 1999) approach posit that
highly reproducible performance (when consistency is desirable) can be a component of
skilled performance, whereas other advocates (e.g., Davids et al, 2008) of the approach
maintain that even for static tasks with consistent task requirements (e.g., pistol shooting),
relatively high execution variability is a characteristic of skilled performance.
The two approaches are in most direct disagreement regarding the role of
environmental information in task performance. Proponents of the ecological/dynamical
systems approach would argue that ongoing environmental information (e.g., visual input)
is necessary for task performance, with many positing a direct perception-action link (e.g.,
Bootsma & van Wieringen, 1990). Conversely, proponents of a mental representations
approach argue that performance is mediated via internalized representations and that task
107
performance can be accomplished in the absence of environmental information (e.g., visual
input).
The expert performance approach was proposed by Ericsson and Smith (1991), and
emphasizes empirically measured, reliably superior performance on domain-representative
tasks. Moreover, by objectively identifying expert performers and using representative
tasks that allow for the capture of expert performance on a consistent basis under
controlled conditions, an exploration of the two disparate approaches of performance (a
mental representations approach versus an ecological/dynamical systems approach) is
possible.
Thus, with the two disparate approaches in mind, the present study adopted the expert
performance approach as framework to address the following research questions of the
present study: a) is task consistency in the form of low variability a necessary component
of skilled performance in the task of ten-pin bowling?, b) is low execution variability
necessary for a high success rate in ten-pin bowling?, c) can high levels of bowling
performance be maintained (high success rate) in instances of high execution variability?,
d) if low execution variability is found to be correlated with low result variability and high
success rates, can high performance levels be maintained in the absence of environmental
information?, e) can skilled performers provide accurate verbal reports of task
performance, even when environmental sources of information are removed?, and f) how
would an approach that disavows any form of cognitive mechanism (including mental
representations) as playing a role in task performance account for the accurate content
contained in these reports?
Based upon these research questions of interest, the following hypotheses of the
present study were generated: a) skilled performers will have a significantly higher success
rate (e.g., a higher percentage of the appropriate pins knocked down) than novice
participants, b) skilled performers will exhibit less execution variability over successive
trials than novices, c) lower execution variability will be associated with higher success
rates, and d) skilled performers will maintain high success rates and low levels of
execution variability, relative to novice participants, following removal of environmental
information.
108
Success Rate of Skilled versus Novice Participants
Success rates confirmed that successful objective identification of expert performers
had been accomplished. As hypothesized, skilled participants exhibited significantly higher
rates of success than novice participants during both spare (93.3% versus 37.3%
successful) and strike (85.9% versus 54.1% successful) trials. As stated above, both the
mental representations and ecological/dynamical systems approaches would predict skilled
performers to achieve high rates of success.
Execution Variability of Skilled versus Novice Participants
With regard to execution variability, it was hypothesized that skilled performers would
exhibit less within-participant execution variability than novice performers over successive
trials. As predicted, analysis of a single component, with loadings from all the markers,
revealed that skilled participants exhibited significantly less execution variability than
novice participants during both spare and strike trials. Given that ten-pin bowling is a task
for which consistency is considered desirable and actively sought, these findings are very
much aligned with a mental representations approach. Moreover, the findings mirror
Ericsson’s (2002) account of skilled golf performance that a golfer’s swing must be highly
reproducible in order to compete at high levels and that this consistency is the necessary
component of skilled golf performance.
Within an ecological/dynamical systems framework, those proponents who maintain
that task performance on well-rehearsed tasks can exhibit low degrees of execution
variability (Williams, David, Williams, 1999) would argue that the results are presumably
reflecting an efficient system with very little system organization is required. However, the
findings with regard to execution variability obviously do not support the view of
proponents of the ecological/dynamical systems approach proposing no specific prediction
with regard execution variability (i.e., it is not important) or that relatively higher levels of
execution variability is a characteristic of skilled performance.
The Correlation between Skill Level, Execution Variability, and Success Rate
It was hypothesized that lower execution variability will be associated with higher
success rates and that this consistency of task performance is a characteristic of skilled
performance. As predicted, Spearman rho correlation coefficients indicated that skill level
was significantly correlated with success rate and negatively correlated with execution
109
variability for both spare and strike trials. Moreover, execution variability was negatively
correlated with success rate for both spare and strike trials.
Once again, the findings with regard to skill level, execution variability, and success
rate are supportive of a mental representations approach. The findings related to the
performance of skilled performers’ success rate and execution variability confirmed that
both are components of skilled performance in the domain of ten-pin bowling. The results
of the correlations confirm the interrelationship between skill level, success rate, and
execution variability. Many advocates of the ecological/dynamical systems approach
would find it difficult to account for such a relationship. However, once again some
ecological/dynamical systems advocates would argue that this relationship again is just a
reflection of a well-practiced efficient system.
The Effect of the Removal of Environmental Information on Performance
Skilled and novice participants were asked to bowl 20 times each under both normal
and environmentally occluded conditions. Environmental information was removed (or
reduced), via occlusion glasses and earplugs, following set-up but prior to task
performance. It was hypothesized that skilled performers would maintain high success
rates and low levels of execution variability, relative to novices, following removal of
environmental information.
Success rate. As predicted, skilled performers maintained a performance advantage
over novice participants during occluded strike trials (48.3% versus 28.7% successful) but,
contrary to predictions, skilled performers did not maintain a significant performance
advantage over novice participants (30.7% versus 25.3% successful) during occluded spare
trials. The findings with regard to success rate during occluded strike trials again lend
support to a mental representations approach as skilled participants were able to maintain a
significant performance advantage over novice participants in the absence of
environmental information. The results of the occluded spare trials with regard to success
rate were not as predicted. Moreover, the findings appear to lend support to an
ecological/dynamical systems approach, in that the success rate for skilled participants was
not higher than the success rate of novice participants following occlusion. Upcoming
discussions, regarding execution variability and the tendency for participants of both skill
110
levels to stop earlier in the approach during occluded trials, provide additional insight into
these findings regarding the success rate during occluded spare trials.
Execution variability: subsequent score measures. For occluded spare trials, the
markers were collapsed into two component scores, one of which was highly loaded with
execution variability measures of the upper body and the other with execution variability
measures of lower body. As predicted, occlusion was less disruptive for skilled performers
than novice participants with regard to upper body execution variability. However, there
was not a significant skill difference with regard to lower body execution variability. For
occluded strike trials, the markers were collapsed into a single component score with
loadings from all markers. Again, as predicted, occlusion was less disruptive for skilled
performers than novice participants with regard to execution variability. Thus, with the
exception of lower body execution variability during occluded spare trials, skilled
performers maintained a performance advantage over novice participants with regard to
execution variability during occluded conditions as measured by the measures of execution
variability over subsequent trials (i.e., subsequent score measures). This finding
demonstrates that not only do skilled participants exhibit low levels of execution
variability during task performance but that execution variability remains low even during
occluded conditions.
The combination of maintaining both low execution variability and high success rates
during occluded strike conditions again lend support to a mental representations account of
skilled performance. However, further investigation was needed with regard to occluded
spare trials as to why success rate was not maintained, despite the maintenance of low
execution variability. The lack of a skill difference with regard to the execution variability
component highly loaded with measures of foot placement (lower-body execution
variability) during occluded spare trials provided an initial clue as to the possible
differential effect of occlusion on spare trials as compared to occluded strike trials. A more
refined analysis was implemented to better account for differences in the positions of the
markers and foot placement at various intervals during the approach.
Event-based measures of execution variability. A more refined measure of consistency
was derived by using the final three steps for a given participant as stages to compare all
markers on normal versus occluded trials. This additional measure allowed a comparison
111
of each marker, along with foot placement, at three distinct points along the approach. The
scores were derived by calculating the difference in the position of the marker on an
individual frame and the mean of the combined frames for both spare and strike trials
(variability scores). As with the previous consistency measures, the variables were reduced
into single component scores loaded with measures for all markers at each of the three
stages for both normal and occluded conditions during both spare and strike trials. The
findings indicated that skilled participants maintained lower levels of execution variability,
as measured by the variability scores, for all four conditions than novices. The question
remained regarding why the maintenance of low execution variability during occluded
conditions did not always translate into maintenance of success rate.
Many participants indicated a fear of falling via crossing the foul line and slipping,
perhaps leading many participants to stop well short of their normal endpoint. An analysis
of the stopping points during occluded versus normal conditions revealed that participants
were indeed stopping short during the approach on occluded trials. An attempt was made
to determine if this variability was likely the result of a general scaling of the approach
during occluded trials by dividing the average distance between event 1 and event 2 by the
average distance between event 2 and event 3 for both the shoulder marker and the location
of the participant’s heel. Analysis of the ratio measures did not indicate significant changes
in the ratio of the approach between normal or occluded conditions for either spare or
strike trials, thus it appears that participants were scaling their approaches during occluded
conditions.
It was proposed that as a result of this scaled-down approach, the path of the ball was
shortened, relative to the pins, to roughly the same degree that the approach was shortened.
This account would also offer an explanation regarding the differential effect of shortening
the approach on occluded spare trials as compared to occluded strike trials. More
specifically, a shortened ball path, relative to the pins, during spare trials offers no outlet
other than the gutter, whereas a shortened ball path during strike trials would provide the
opportunity for pins to be encountered, even if the pocket is missed. An analysis of the
percentage of gutter balls in the occluded conditions supported this explanation, in that
skilled participants experienced a significantly higher percentage of gutter balls in the
occluded spare condition than in the occluded strike condition.
112
An Investigation of the Participants’ Deliberate Practice Histories
The concept of deliberate practice was introduced above and posited to be responsible
for skilled performance via refined representations of task requirements and performance.
For example, in a landmark study of expert performance by musicians, Ericsson, Krampe,
and Tesch-Römer (1993) identified a specific training methodology (designated “deliberate
practice”) to be the primary factor in the development of expert performance. Moreover,
properly administered deliberate practice leads to improvements in planning, analysis,
problem-solving, and motor control via refined representations of the task domain
(Ericsson & Lehmann, 1996). Because of this proposed relationship between engagement
in deliberate practice activities and refined representations of task performance, deliberate
practice histories were collected from the participants. From these estimates, the
relationship between the deliberate practice histories, skill level, success rate, and
execution variability was explored. As expected, skilled performers had accumulated many
deliberate practice hours and these activities were strongly correlated with house average,
skill level, and success rates (with the exception of occluded spare trials). A particularly
interesting finding was that during occluded strike trials, accumulated and current
deliberate practice hours (solitary and team) were all significantly correlated with success
rate but current hours engaged in solitary (individual) deliberate practice exhibited the
strongest relationship (rs= .70). This relationship is similar to the finding of Ericsson et al.
(1993) that the most important factor distinguishing the most elite musicians from slightly
less accomplished musicians was time spent practicing alone, i.e., solitary deliberate
practice.
With regard to execution variability, accumulated solitary and individual deliberate
practice hours were negatively correlated with execution for normal conditions of spare
and strike trials. Similar findings were found in the occluded conditions, with the exception
of the occluded spare trials for which accumulated but not current engagement in
deliberate practice hours was negatively correlated with execution variability. Moreover,
when the respective deliberate practice measures were entered into a regression equation,
no other past activities accounted for a significant or majority amount of the variance.
In addition, regression analyses were conducted to assess whether execution variability
predicted success rate above and beyond measures of deliberate practice for normal
113
conditions. The results of the analysis indicated that deliberate practice accounted for the
majority of the variance for both spare (52.9%) and strike (52.2%) trials. Execution
variability accounted for a significant but minimal amount of additional variance during
spare (12.2%) and strike (7.7%) trials. The findings of this analysis suggest that
engagement in deliberate practice activities mediate skilled performance via reduction of
variability through the acquisition of refined representations of task performance.
A Brief Discussion of Participants’ Retrospective Verbal Reports
Two of the research questions proposed above were related to the verbal reports of the
participants: a) can skilled performers provide accurate verbal reports of task performance,
even when environmental sources of information are removed?, and b) how would an
approach that disavows any form of cognitive mechanism (including mental
representations) as playing a role in task performance account for the accurate content
contained in these reports?
During normal conditions, skilled performers provided retrospective verbal reports that
were rich in detail with regard to performance goals, performance monitoring and needed
adjustments. For example, the following is an excerpt from a skilled performer’s
retrospective verbal report following a strike trial under normal conditions:
“Um I needed to clean my ball off and uh I was thinking about where I
needed to stand and where I needed to throw the ball in order to get the ball
into the pocket in order to strike. I was standing on board 8 crossing about
board 3. Straight up 3 and then hoping I got into the pocket to get strike.
Well I got the strike so I was glad I got the strike.”
The skilled participant appears to be very aware of the factors which would lead to good
outcome during this attempt (i.e., strike). The following is an excerpt of a skilled
performer’s retrospective verbal report following a normal spare trial:
“Um, I placed my feet where I normally stand to pick up a right-side spare
and uh I missed, I threw it into the gutter now I'm thinking about I need to
adjust in order to pick that spare up. I [need to] move my target to the left
one board and see if I can pick it up doing that.”
Again, the skilled participant is making cognitively mediated assessments as to the
required adjustments needed for success on later spare trials.
114
A common theme for both skilled performers and novice participants during occluded
conditions was a fear of crossing the foul line and falling. However, the skilled participants
also provided retrospective verbal reports that would to indicate strategic adjustment based
upon the newly encountered environmental occlusion:
“Uh [I] just wanted to set up right. Pictured, visualized in my head uh…you
know what a good shot would look like. Um try to go through that good
shot but when I finished I actually could tell something was different so I
don't really think I threw a very good shot just based on how I released it
and it just didn't feel very good.”
Other retrospective verbal reports of skilled performers during occluded conditions seem to
indicate active cognitive monitoring of performance:
“Uh I knew it was difficult but with the earplugs in it was easier to think.
With the glasses on I was worried more about my feet and where I would
end up rather than on where the shot was gonna’ go and my release. So I
was counting my steps making sure I did my four and a slide and hopefully
that my slide didn't go too far and I got out on the lane.”
Thus, these example retrospective verbal reports provided by the skilled participants
appear to provide evidence of some form of cognitive mediation on the part of the
participant. When considered alone, these verbal reports are not necessarily enough to
support a mental representations approach. However, combined with the evidence reported
and discussed above, the retrospective verbal reports are another piece of evidence in
making the case for a mental representations account of skilled performance.
Conclusion
Ericsson and Smith (1991) proposed the expert performance approach emphasizing
empirically measured, reliably superior performance on domain-representative tasks. The
present study adhered to the expert performance approach by objectively identifying expert
performers and using representative tasks that allow for the capture of expert performance
on a consistent basis under controlled conditions to investigate the role of environmental
information in skilled performance. With regard to the present study, the expert
performance approach (Ericsson & Smith, 1991) provided a framework from which to
115
investigate the claims of the two theoretical approaches, a mental representations approach
and an ecological/dynamical systems approach.
Ericsson and Lehmann (1996) proposed that properly administered deliberate practice
leads to improvements in planning, analysis, problem-solving, and motor control via
refined representations of the task domain, a mental representations approach. Lehmann
(1997) further posited that mental representations allowing skilled performance are
comprised of representations of: a) the desired performance goal, b) production aspects of
the task, and c) representations of actual task performance. As described by Ericsson
(2002), a performer begins with the desired performance goal, uses his or her
representation of how to execute performance, uses the representation to monitor
performance and makes a new performance goal if performance is not satisfactory. This
reiterative process allows for a consistency of task performance that may be absent in lessskilled performers.
An alternative presented above was the ecological/dynamical systems approach,
advocates of which argue that performance does not rely upon an internalized
representation but that task performance is reliant only upon motor responses to
environmental information (Gibson 1954, 1979; Nakayama, 1994), that motor system is
self-organizing entity for which systematic constraints (i.e., degrees of freedom) are also
self generated (Frank, Peper, Daffertshofer, & Beek, 2006), and that the system
automatically makes necessary adjustments to achieve the appropriate state or regain the
previous state (Van Gelder and Port 1995, as cited in Treur 2005).
The results of the present study indicate that high success rate and low execution
variability is a characteristic of skilled performance during ten-pin bowling. The present
study has demonstrated the ability of skilled performers to maintain high success rates
(strike trials) and low levels of execution variability, even under environmentally occluded
conditions. The finding of low execution variability by skilled performers during occluded
conditions is a compelling argument for cognitive mediation of performance, i.e., a mental
representation. Moreover, the source of the decreased success rate during occluded
conditions was identified as a scaling of the bowling approach (likely due to a fear of
falling at the foul line), resulting in a ball path that was shortened relative to the pins.
These are very important findings theoretically because, although some advocates of a
116
dynamical/ ecological systems approach argue that task performance on well-rehearsed
tasks can exhibit low degrees of execution variability (Williams, David, Williams, 1999),
it is a basic tenet of the mental representations for tasks for which consistency is desirable.
Moreover, for ecological/dynamical systems advocates, these low degrees of execution
variability are presumed to reflect an efficient system, as very little system organization is
required, however, this system is posited to be reliant upon environmental information for
system maintenance. Because skilled participants exhibited low levels of execution
variability, when compared to novice participants, during both the normal and occluded
conditions, the findings support cognitive mediation, consistent with a mental
representation. Moreover, the low levels of execution variability of skilled performers in
environmentally-occluded conditions would argue against an environmentally mediated
explanation of skilled performance, an ecological/dynamical systems approach.
Recent claims by Davids et al., (2008) suggest that the Gibsonian accounts of
performance posited by advocates of the ecological/dynamical systems never actually
claimed that some forms of cognitive representations did not exist, but that these cognitive
representations were of no importance during task performance and such considerations
were not necessary because of environmental guidance. This recent argument regarding the
existence of cognitive representations on the part of ecological/dynamical systems
advocates appears to be one of convenience. Moreover, the results of execution variability
during occluded conditions (and the success rate during occluded strike trials) counter such
an argument as most aspects of skilled performance remained intact in the absence of
environmental information.
A potential counterargument of an ecological/dynamical systems advocate would be
that the source of environmental information allowing for system self organization during
task performance had shifted to information sources such as kinesthetic information. This
argument is similar to Robertson and Elliot’s (1996) suggestion that during the task
walking a balance beam with no vision, experts were able to make necessary adjustments
based upon kinesthetic information, vestibular information, or a combination of the two.
Planned future research projects should allow additional clarification of the contested
issues of the two approaches. For example, examining the execution variability of a skilled
bowler making the approach with a ball of modified weight or without a ball, would allow
117
an investigation of the proposed shift to kinesthetic sensations as environmental
information. Other planned manipulations include having skilled bowlers attempt to bowl
in a shortened approach, providing experimental control to the scaled approach observed in
the occluded conditions. In addition, manipulation of the timing of the visual occlusion
during the bowling approach would allow one to eliminate the fear of crossing the foul line
(i.e., the trend of participants shortening their approach), It is hypothesized that once the
fear of crossing the foul line is eliminated, the decrements in success rate by skilled
performers observed in the present study would be significantly reduced.
Finally, as stated in the introduction, the expert performance approach stands in
contrast to views of skilled performance proposing innate factors as the primary source of
superior performance capabilities. In the present study, engagement in deliberate practice
activities was found to lead to the development of refined representations of task
performance, which were responsible for reducing execution variability leading to high
success rates. Thus, because engagement in deliberate practice activities ultimately
mediated performance capabilities, the importance of innate factors for skilled ten-pin
bowling performance can be rejected.
118
APPENDIX A
DESCRIPTION OF SUBSEQUENT SCORE CALCULATION
119
For each participant, the
difference in the x and y
coordinates for each marker (e.g.,
WRT) during Frame 1 for the 5
related trials (e.g., occluded
strikes) is calculated by comparing
Trial 1 to Trial 2, Trial 2 to Trial
3, Trial 3 to Trial 4, and Trial 4 to
Trial 5.
Time
Analysis
Frame X
Frame 1
120
Trial 1
Trial 2
Difference
in cm
Trial 3
Trial 4
Trial 5
Trial 3
Trial 4
Trial 5
121
Trial 1
Trial 1
Difference
in cm
Trial 2
Trial 4
Difference
in cm
122
Trial 5
Trial 5
Trial 1
Trial 2
Trial 3
123
Difference
in cm
This process was repeated for
all frames working backward
from the foul-line.
Time
Analysis
Frame X
Frame 2
124
This process was repeated for
all frames working backward
from the foul-line.
Time
Analysis
Frame X
Frame 3
125
This process was repeated for
all frames working backward
from the foul-line.
Time
Analysis
Frame X
126
APPENDIX B
HUMAN SUBJECTS COMMITTEE APPROVAL
127
Lstare
UNIVER S ITY
Office of the Vice President For Research
Human Subjects Committee
Tallahassee. FloOda 32306-2742
(850) 544-8633· FAX (850) 644-4392
REAPPROVALMEMORANDUM
Date: 1130/2007
To:
Kevin Harrla
2303 Ryan Place
Tallatlaaa&e, FL 32309
Dept
PSYCHOLOGY DEPARTMENT
From: Thomas L Jacobsoo, Chair
Re.
ｾ＠
｣ Ｍ ｡ｾ ｾ＠
Reapproval of Use of Human aubjecta In Research:
Deliberate Practice, Mental Representations, and Skilled Performance In Ten-Pin Bowling
Your request
to continue the research project listed above involving human subjects has been approved
by the Human Subjects Committee. If your project has not been completed by 1/2912008 please
request renewed approval.
You are reminded that a chal'lge in protocol in this project must be approved by resubmission of the
project to the Committee for approval. Also. the principal investigator must report to the Chair promptly.
and in writing. any ul'lanticipated problems involving risks to subjects or others.
By copy of this memorandum. the Chairman of your department andlor ｹｯ＼ｾｲ＠
major professor are
reminded of their responsibility for being informed concernif'lg research projects invoNing human
subjects in their department T hey are advised to review the protocols of such investtgations as often
as necessary to insure that the project is being conducted in compliance with our institution and with
DHHS regulations.
Cc: Anders Ericsson
HSC No. 2007.025-R
128
APPENDIX C
DELIBERATE PRACTICE HISTORY INTERVIEW
129
Practice History Interview
Participant #_____
Biographical Information
1. Age:
2. Gender: F M
3. Handedness: L M
4. At what age did you begin bowling? __________
Activity Chart
Past Activity
Accumulated hours:
At 3 years
At 5 years
At 10 years
At 15 years
Note: Since the age listed on question 4.
So if you answered 11 years old for
question 6 then “At 3 years” will
represent the age of 14 years old (5 = 16,
etc.)
Playing in bowling leagues
Playing for fun
Playing in competitions
Engaging in solitary
deliberate practice
Deliberate practice
with others (e.g. team practice)
Current Activity
Hours spent
per week
Relevance
0-10 scale
Playing in
bowling
leagues
Playing for
fun
130
Effort
0-10 scale
Pleasure
0-10 scale
Playing in
competitions
Engaging in
solitary
deliberate
practice
Deliberate
practice
with others
131
APPENDIX D
INFORMED CONSENT FORM
132
IHFORM!O CONSEN T FORM
I freety and votuntamy and wit_h out ﾷｾｯｦ＠
fort:·• 0t eoeteiotll. conunt to be 1 partici-pant In tf'\1 reiHTCh
projoct oni!Uod Ｂ ＰＮＱｩｾｲ｡ｴ･＠
practico, m•ntol repreunlatiOf\1, ｾｮ､＠
sl<illotd perfor,.,onco in ten-pin bowling• . In
particular. my participation, or e-hoic. 110C Co partic.lpate.ll 1'\0t telated 1o ｡ｮｾ＠
fom. of ....,...m•n1 by the Oep1nment of
PoyclloiOQy.
Thl$ teSeMCh Is bein9 conducted by Ke>.ttn R. Harrta and Or. "nd•rw Ertcaaon1 •team ot .,wearcr.ers of
poycnotOQy It the Florida S - Untwrolty. lundel'$1ano tho ""'P- ot their rooNTCh ｰ＼ｯｪｾｴ＠
is to boller undorotond how
pflle<l polform... lellm to imfJ<O.,.. porfom1anu.
1undoral8nd !loot tho obJdy c;ould lutas ｬｯｮｾ＠
n 2 houro . If I ponielpot>o In 111e ontl"' ot\ldy.l will rocelvo uo lor
my time . I wtll afeo rwe.ive pritltfd i l1fomt:atton •bo4.1t tha obJec.dvet ｾ＠ ltlc rti'Marel\.
1 ｵｮｾＭ
my Dettlclpatton IS «>tally voiUnwy ond ' "'"' stop partlcloatton at anytmwo. lll d-.:ldo to ..w..o..w, 1
ｓＱ ｾ＠Ｐ
I und•taa.r>d ltlato....,U.Ing 1say will
will be paid ror tile poltion of IN oludy I hovo comt>lnd to.; . 113 ｣･＾ｭｰｬｯｴ＼ｮｾ＠
be k•pt atricttr eoT'I'fldlntial to tM e.unt aUowaDie by ｾ
Ｎ＠
data tMt '"'ultlo f1-orq my partlOJ*tJOn will be made totaUy
anonymou•, end Will not be attributAble to any lndSvfdiHII.
in thla t\\ldr. TP'lua, t
1un<Miratand ttl•rw ;• • poulbUity of .a rnlnlt'tul J.ev.tl of riak tnvolwd tf 1 agree to ｰ｡ｦｴＺｬ･ｾ＠
at Ｂｾ＠
Ume I with. I uf1dwttafld tt.at th4H'e ar. sh fe.Wf"h of 1he study proceea dt:utlgned
.am abj,e co stop rnJ' ｐｩｬｲｴ｣ｾｰｭｯｮ＠
""'I
to
ma•trnlze my anonymity. Thn• aN daacribed below.
l ..J .u,M... ｾ＠
tb.a1J ｣｡ｮ
ｾ＠
m.aHtM'
delot.<f frcn> l . Tt-4 d..:cslon 1$ at my ｾ･ｬｩｯ
co!lede.d
ｦｬｾ
｣､･Ｎｳ｡ｅｉｲｴＺＲｾ＠
2. 1 un.O.r$ta!'d tflat l ｣Ｎｾ ｮ＠ withdfa-« from rtte study t.t Arty tJTWt. erne: ha-\le ｭ
"''I pre.er.c:e Thia d• cit ion 1& Jt my thccreton.
3. 1ｵｬＢｾｳＮｴ｡ｮ､＠
that. at eny t:me aftec ｴｾ･＠
my pfeMnOE. •long witt'\ any other ｲ･ｾ＠
ｌｯｬ･ｴｾＮ
ＴｍｑＩＮｰ
ｱ ＠ l t 'P CJ A-JJ....- - --
｡ｴ
ｴｾ ｴｩ＠
coUecteo l!cwn ｾ＠
c;t8s!roytd in
study. ! ear request 1:-..et ttle materials conac;;ted trorn me be destroyed i--.
data obS.alned ｴｨｔｯｾｧｮ＠
m)' ,,.ttiell)lfion Th1s d•e•&ion is at mt disc,..tlbn
-4. t underatand thllt 1..e t&pt's thai resulf fr1m m)' p•n•o•pat1or. w.l: be m.<Jtl<.&<l 011ty W'lth ilfl anonymovs ｣ ｾ＠
name of the ras.ardl•r 1fllt ｣ｯｮ＼Ｑ
ｵｾ＠
the Jt\.I<Jy.
6.
ａ
ｾ＠
--
Ｎ＠
• ·1 d tl'\e
taJ:'eS wili ba. «.epl b ) \toe researcher I"'& IDCkeo 'lkng cab•net.
t unden.tand th•r•.,.. btntfits for paruc:ipating Jn ttl Ia r..ureh ｾｲ･ｪ｣ｴ
Ｎ＠ Fi,.t. my own ｡ｷｴｲ･ｾｳ＠
about
performanc• may De •nt:Nastd. I will
be pro..,idlng ptycnotogleta W1th valuabl•lnalghts into
how ptoplo pNCIJ. . and impraw porformance wtlich mey be usoful to eWtyon...
1 ｵｾｲｷｴ｡ｮ､＠
that tNs toi\MI'tt ""*>' be withdtawn lit any lime wtthol.rt p,..judlc., penalty or loa& of ｢･ｴｾ｡ｦｳ＠
to
which 1 am oCherwte• ｾｴＩ･､
Ｎ＠ \have been given th9 nght to •k ana ha,f' af'lswend any •nqu1ty concernltlg l:tl• Ｎｴｵ､ｾ
Ｎ＠
OUKllo!\1, II • .,.,. haft - n anawonoo to my nMfaclion.
1 unde..anct that I may c;ontact tha faclmy odllttor. 0.. K. An<*'s E<lcaaon, Flortlla s . - Unlvoroily, O.pertmom
ＢＧｐ｡ｊ＼ｉ＾ｯｬｾＱ
ﾷ＠ UJ ><.now RnHn:h Bulldi"9la110h. .soo, Ffortdo 32301·2S40.(l501 M4·tl60, Ｈ ｾｲｩ｣･ｯ
ｮ Ｘｐｯｹ
Ｎ ｬ｡ｵ
Ｎ ｯ､ｵ
Ｉ＠ lor
ans.w.t"' to queetfone about thil ,...arch or my rJgtrtt.. 1 undMWtllnD I c:an also contact t he Ofl"iol of fte.sMrc-h. Human
5ubj- Commi!IM , 2035 E. Paul Dirac Drlvo, Box 1e, 100 Sll110r llldg.. ｬｮｾｴＢ＠
P&rk. Tollah-ee. FL 32306-2763 {85().
•••o
mothods for Ｖｦｮｰｲｯｖｩｾ＠
644-3633) wiCh tny ｧｲｾｮ｣Ｚ
Ｎ＠
The rMUttli of the atudy will biJ Mnl to m• upon my 1'9qi.IHt.
t hive ,...d •nd oncttr"Stand tt'lfs cona.m form. f am 18 years ot eg• or older.
---
- -- - -- ·· - -
{Dolo)
(Subjed)
133
···-
- -·-- · ·- -- --
APPENDIX E
PARTICIPANTS’ AVERAGE VARIABILITY AT THE THREE EVENTS IN
CENTIMETERS FOR SPARE TRIALS
134
Marker Skill
WRT
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Full
Spare 1
13.42
6.34
6.68
8.26
10
4.05
7.42
5.75
17.12
7.59
9.57
5.49
15.38
13.47
.
Spare 2
9.06
6.38
5.8
7.49
8.14
2.44
6.08
3.87
13.63
9.04
14
5.95
16.57
14.21
.
Spare 3
13.02
4.65
9.02
4.27
4.14
7.61
10.94
10.36
22.72
11.76
9.91
3.19
14.41
3.86
5.44
Occluded
Spare 1
9.65
7.59
4.58
6.3
17.81
5.86
12.41
5.05
8.02
36.77
12.23
4.37
3.2
19.09
.
Spare 2
9.43
9.81
5.71
4.97
11.29
8.5
9.16
5.82
11.41
26.22
24.44
4.97
8.25
19.03
.
Spare 3
8.87
4.13
9.7
7.83
16.08
10.04
10.85
7.93
21.39
21.15
16.47
5.49
9.18
13.63
75.45
WRT
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
1.91
2.09
2.05
3.53
3.18
2.05
3.62
2.03
1.64
1.93
1.95
2.13
3.13
3.33
1.03
3.39
1.9
.
4.35
2.69
2.8
4.98
2.91
3.39
2.84
3.43
4.02
3.87
5.34
1.6
5.09
7.16
2.59
1.52
2.28
2.23
5.69
2.56
3.32
1.83
2.86
2.6
4.25
6.2
5.15
2.67
2.98
3.41
3.44
5.93
2.49
2.56
3.88
1.64
2.21
4.59
5.04
6.39
3.53
6.61
2.5
4.45
.
7.56
4.47
3.23
3.71
4.03
2.22
5.08
4.53
11.17
8.73
8.24
8.4
5.69
6.65
11.18
9.51
7.86
5.34
6.3
6.23
4.87
9.99
5.03
15.31
16.19
9.5
7.95
ARM
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
9.06
6.38
5.8
7.49
8.14
2.44
6.08
3.87
13.63
9.04
14
5.95
16.57
14.21
.
13.02
4.65
9.02
4.27
4.14
7.61
10.94
10.36
22.72
11.76
9.91
3.19
14.41
3.86
5.44
9.65
7.59
4.58
6.3
17.81
5.86
12.41
5.05
8.02
36.77
12.23
4.37
3.2
19.09
.
9.43
9.81
5.71
4.97
11.29
8.5
9.16
5.82
11.41
26.22
24.44
4.97
8.25
19.03
.
8.87
4.13
9.7
7.83
16.08
10.04
10.85
7.93
21.39
21.15
16.47
5.49
9.18
13.63
75.45
9.06
6.38
5.8
7.49
8.14
2.44
6.08
3.87
13.63
9.04
14
5.95
16.57
14.21
.
135
Marker Skill
ARM
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Full
Spare 1
.
4.35
2.69
2.8
4.98
2.91
3.39
2.84
3.43
4.02
3.87
5.34
1.6
Spare 2
2.59
1.52
2.28
2.23
5.69
2.56
3.32
1.83
2.86
2.6
4.25
6.2
5.15
Spare 3
3.41
3.44
5.93
2.49
2.56
3.88
1.64
2.21
4.59
5.04
6.39
3.53
6.61
Occluded
Spare 1
.
7.56
4.47
3.23
3.71
4.03
2.22
5.08
4.53
11.17
8.73
8.24
8.4
Spare 2
11.18
9.51
7.86
5.34
6.3
6.23
4.87
9.99
5.03
15.31
16.19
9.5
7.95
Spare 3
.
4.35
2.69
2.8
4.98
2.91
3.39
2.84
3.43
4.02
3.87
5.34
1.6
ELB
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
13.55
6.29
6.35
8.58
8.18
3.83
6.93
5.27
17.07
7.45
9.29
5.63
15.29
13.13
.
8.85
6.14
5.95
6.72
6.88
1.99
6
4.96
12.22
8.78
12.73
5.61
15.56
12.81
.
12.33
4.99
9.22
4.51
4.54
7.64
10.57
9.7
23.14
11.26
9.95
3.42
14.03
3.52
4.97
9.32
7.99
4.53
6.52
13.04
5.94
12.05
4.79
8.86
35.66
10.02
4.45
2.64
19.53
.
8.5
9.07
5.15
4.55
10.14
7.93
7.89
5.73
11.04
26.35
19.02
4.73
8.46
19.05
.
8.59
4.4
9.3
7.66
16.72
10.09
10.89
7.84
21.21
21.41
15.66
5.76
10.08
14.17
75.25
ELB
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
2.04
2
1.98
3.49
2.93
1.92
3.45
2.2
1.8
1.83
2.03
2.22
3.01
3.08
1.26
2.82
1.72
.
4.05
2.68
2.71
5.07
3.35
3.5
2.67
3.25
4
3.87
4.97
1
5.17
6.82
2.44
1.39
2.16
2.13
5.38
2.3
2.99
1.86
2.86
2.6
3.85
4.89
5.22
2.44
2.98
3.53
3.61
5.68
2.53
2.8
4.09
1.64
2.34
4.56
5.07
6.09
4.1
6.86
2.31
4.17
.
7.02
4.33
3.29
3.71
4.3
2.08
4.83
4.4
11.06
8.7
7.01
8.65
5.42
6.03
10.73
9.45
7.69
5.42
6.55
5.78
4.54
9.93
5.03
15.38
15.25
9.38
7.2
136
Marker Skill
UPA
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Full
Spare 1
12.48
5.62
6.55
8.4
8.41
3.52
6.51
5.38
17.99
7.13
9.43
5.77
15.15
13.51
.
Spare 2
9.53
6
6.58
6.15
6.57
1.77
5.69
4.58
12.87
8.5
11.89
5.24
14.54
11.39
.
Spare 3
12.25
4.69
9.35
4.31
4.94
8.23
9.98
8.87
25.53
10.82
10.28
3.73
14.55
3.58
5.07
Occluded
Spare 1
9.57
7.73
4.53
6.12
10.35
6.21
11.21
4.9
9.71
34.52
10.64
4.51
3.03
19.42
.
Spare 2
7.7
9.08
4.12
4.2
10.6
7.82
7.87
5.63
10.94
26.77
16.09
4.9
8.4
19.06
.
Spare 3
9.08
4.73
8.38
8.42
16.93
10.07
11.38
7.8
20.91
21.89
14.84
5.73
11.77
14.84
76.24
UPA
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
2.98
1.66
1.4
2.66
2.77
1.79
3.9
2.49
2.06
1.83
1.99
2.11
2.77
3.52
2.01
2.93
1.72
.
3.86
2.86
2.75
5.17
2.63
3.48
2.72
3.18
3.75
4.64
4.97
1.55
5.85
5.87
2.24
1.71
2.41
2.03
5.2
2.45
2.59
1.84
2.94
2
2.39
4
3.13
2.97
2.82
3.71
3.51
5.64
2.47
3.01
5
1.78
2.46
4.36
5.16
5.39
4.24
7.47
2.51
4.02
.
7.56
4.26
3.59
3.46
4.4
2.08
4.8
4.41
10.89
8.2
6.23
8.62
5.01
5.25
10.4
11.42
7.24
5.02
6.98
6.08
3.74
10.31
4.88
15.18
15.09
9.43
7.06
SHO
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
12.91
5.05
6.9
7.85
10.57
2.16
5.45
5.61
19.23
6.44
9.32
6.48
14.71
13.68
.
9.45
5.8
6.04
6.96
5.81
2.34
5.2
3.56
14.39
8.98
10.1
5.01
15.46
9.51
.
11.86
5.3
10.22
3.83
3.97
6.22
8.84
5.75
25.33
10.4
12.08
4.83
14.84
4.64
5.36
9.28
6.73
4.89
5.6
4.99
5.71
9.5
5.54
9.22
32.65
9.87
4.12
2.12
18.79
.
7.23
8.11
4.18
4.22
9.68
6.89
7.73
5.62
10.73
26.63
16.74
4.73
8.62
19.24
.
9.23
6.11
7.45
9.04
18.78
10.55
10.66
8.43
21.55
21.79
13.1
6.39
14.07
17.18
75.89
137
Marker Skill
SHO
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Full
Spare 1
1.93
1.58
1.85
2.03
2.65
1.73
3.86
2.59
2.28
2.28
1.86
2.62
2.13
Spare 2
2.63
2.18
2.74
3.54
3.16
2.86
4.82
3.23
3.13
2.75
3.25
4.33
3.77
Spare 3
5.47
5.53
2.14
1.94
2.65
1.96
4.96
1.82
1.83
2.11
2.98
1.12
2.84
Occluded
Spare 1
3.05
1.89
3.61
4.06
5.85
2.14
3.03
5.65
2.01
2.69
3.63
6.02
5.56
Spare 2
2.38
3.18
4.2
6.82
4.32
4.32
3.74
3.16
2.19
4.91
4.26
11.4
9.26
Spare 3
3.55
4.56
9.67
11.61
6.66
5.18
6.71
5.06
2.67
10.14
5.21
14.75
13.64
STEP
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
9.44
5.61
6.81
6.28
6.06
3.3
6.38
5.5
13.1
6
8.86
6
15.16
11.83
.
10.14
3.65
9.69
7.11
3.11
4.8
6.91
6.17
13.18
8.25
9.79
5.23
14.18
17.46
.
7
3.41
9.21
5.72
1.92
5.89
5.5
4.69
26.61
9.46
11.83
5.56
11.28
3.65
2.69
9.12
8.07
5.12
4.79
4.54
4.36
7.24
3.32
7.26
34.37
12.69
4.2
3.74
14.56
.
6.31
5.86
2.75
4.44
13.99
6.18
6.75
6.03
12.16
27.05
20.16
6.79
12.35
17.3
.
7.27
5.78
7.84
6.02
19
11.53
12.49
3.67
27.28
23.03
24
6.39
15.79
42.14
75.09
STEP
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
0.99
1.52
3.13
3.26
3.8
1.92
3.19
1.49
2.41
1.68
2.33
3.14
1.34
0.96
1.63
1.93
3.12
2.25
2.08
2.78
2.83
4.21
2.53
2.46
1.75
3.6
2.8
3.43
0.81
1.1
5.86
5.47
0.9
2.08
2.49
3.04
4.19
2.35
2.25
1.06
1.53
1.55
2.08
2.91
1.59
3.11
2.43
3.75
5.04
3.81
1.71
3.1
5.37
1.82
2.71
3.82
5.73
4.4
2.53
8.83
3.17
3.63
5.24
5.6
3.54
4.22
5.72
4.21
1.77
6.3
3.01
11.37
9.2
5.31
8.78
2.46
4.64
11.89
8.41
8.17
5.76
8.23
4.88
2.79
8.87
5
15.64
13.48
7.09
5.71
138
APPENDIX F
PARTICIPANTS’ AVERAGE VARIABILITY AT THE THREE EVENTS IN
CENTIMETERS FOR STRIKE TRIALS
139
Marker Skill
WRT
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Full
Strike 1
3.69
5.23
6.87
4.43
11.26
3.02
7.22
5.72
17.13
26.75
15.42
2.85
10.87
20.92
.
Strike 2
5.31
9.89
10.41
4.21
6.59
6.57
7.45
6.09
17.55
17.56
21.82
4.49
11.75
27.96
.
Strike 3
7.15
3.35
17.18
7.97
6.48
3.97
8.47
9.7
19.51
13.65
17.84
5.17
10.88
9.49
5.32
Occluded
Strike 1
8.59
6.86
5.45
6.61
17.11
4.46
19.02
7.18
6.91
20.54
20.06
5.56
9.92
20.64
.
Strike 2
22.08
12.09
10.13
13.64
13.61
7.45
17.87
6.51
18.79
39.26
31.05
6.97
13.55
18.78
.
Strike 3
20.85
2.83
6.71
12.71
22.64
20.52
16.75
11.63
22.6
21.53
7.76
9.37
21.21
16.43
60.12
WRT
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
1.9
1.21
.
10.86
2.15
2
3.28
1.8
1.8
2.11
1.72
3.22
3.39
4.01
1.2
3.83
1.99
2.23
8.2
3.02
2.21
2.83
4.79
3.22
4.98
2.25
3.47
1.77
6.73
4.14
3.35
3.75
5.03
5.45
5.6
3.01
4.4
8.96
2.9
5.71
3.71
3.48
4.5
3.88
4.41
3.42
3.33
.
5
3.76
3.16
6.47
5.3
1.39
5.14
3.51
4.5
9.78
5.21
2.25
4.53
3.03
5.97
7.89
5.03
2.1
10.06
6
4.71
7.17
6.07
5.3
12.18
11.34
4.67
7.62
5.44
12.53
11.84
4.76
2.38
11.82
7.44
4.56
12.05
5.04
8.9
14.74
4.98
9.74
ARM
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
3.43
4.25
7.76
4.38
9.42
2.18
7.48
5.42
15.7
22.69
11.21
2.45
9.45
22.24
.
4.69
7.6
9.94
3.48
5.95
5.62
7.24
6.57
15.1
17.3
16.32
3.38
13.09
24.8
.
7.28
3.47
15.73
7.22
6.26
3.86
7.57
8.62
19.12
13.29
15.69
5.74
12.71
8.56
4.64
7.89
7.44
6.24
5.75
14.04
3.33
16.64
6.96
6.41
15.02
13.35
3.71
7.19
20.5
.
12.24
10.49
10.29
9.58
12.87
7.19
14.59
6.42
18.55
30.13
22.02
6.9
15.82
18.32
.
20.39
2.67
5.97
12.57
22.03
19.35
17.12
11.2
22.24
21.39
7.04
9.03
22.92
16.29
61.2
140
Marker Skill
ARM
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Full
Strike 1
1.47
1.45
.
9.94
1.99
1.79
2.82
1.74
1.37
2.17
2.05
2.74
3.72
Strike 2
3.37
2.09
.
7.52
2.88
1.92
2.94
4.26
3.34
5.01
2.1
3.36
1.81
Strike 3
2.55
3.58
5.07
4.69
4.31
1.76
4.13
6.81
2.24
4.44
3.36
2.69
4.55
Occluded
Strike 1
2.76
3.35
.
4.32
3.77
2.33
7.46
5.69
1.32
4.94
2.81
4.6
9.7
Strike 2
3.86
2.85
.
7.18
5.15
1.82
8.58
6.16
4.36
7.53
5.31
4.95
11.97
Strike 3
6.7
6.03
11.85
10.85
5.14
2.98
11.39
7.2
4.3
11.9
5.15
8.61
13.48
ELB
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
3.6
4.52
7.68
4.62
8.24
2.11
7.9
5.47
15.16
21.45
8.77
2.09
8.39
21.87
.
4.53
6.45
12.61
3.14
4.85
5.29
6.9
6.57
14.34
17.31
13.79
3.35
12.12
23.67
.
7.27
3.54
18.01
7.24
6.16
3.87
7.14
8.08
18.58
13.33
14.48
5.77
13.71
8.26
4.48
8.13
7.89
7.08
5.47
11.72
3.02
16.76
7.2
5.99
14.04
9.84
4.65
5.41
20.47
.
8.52
9.54
9.8
7.43
12.01
6.81
13.04
6.12
18.95
24.46
14.15
6.67
15.22
17.8
.
20.19
2.84
5.7
12.47
20.97
18.96
17.26
10.71
22.43
21.28
6.37
9.29
24.41
16.05
60.87
ELB
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
1.66
1.65
.
9.74
1.91
1.92
2.82
1.88
1.14
2.21
2.24
2.63
3.94
2.93
1.69
2.92
1.95
.
7.32
2.78
1.9
2.94
4.37
3.26
4.74
2.11
3.29
1.41
4.59
4.56
2.3
3.16
4.64
4.69
4.07
1.78
4.04
6.08
1.95
4.25
2.78
2.39
3.23
3.68
3.32
2.35
3.26
.
3.93
3.67
2.29
7.67
5.66
1.39
4.88
2.72
4.64
9.41
4.12
2.54
3.06
2.75
.
6.98
5.17
1.8
10.34
6.39
4.26
7.67
4.95
4.87
11.58
9.24
4.58
6.54
6.69
11.42
10.5
5.16
3.09
11.08
6.79
4.39
11.7
5.25
8.24
15.41
5.56
6.37
141
Marker Skill
UPA
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Full
Strike 1
2.62
4.05
9.64
4.41
8.35
2.23
8.15
5.3
15.11
19.8
7.42
2.03
6.96
21.98
.
Strike 2
4.37
4.79
11.25
2.55
5.43
4.5
7.09
6.09
14.45
17.43
11.28
3.09
12.17
22.03
.
Strike 3
7.97
3.39
15.74
7.43
6.14
4.45
7.1
7.32
17.91
14.15
14.22
5.58
14.88
7.38
4.34
Occluded
Strike 1
6.21
8.15
8.95
4.39
10.97
3.52
14.41
7.53
6.17
12.78
10.13
4.72
5.03
20.28
.
Strike 2
6.74
8.47
9.89
6.65
12.84
6.57
12.09
5.92
16.97
18.34
9.28
6.63
14.75
17.44
.
Strike 3
20.8
3.01
5.2
12.92
20.03
17.95
17.83
9.82
22.11
21.8
6.19
9.45
28.06
15.53
61.27
UPA
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
2.35
1.42
.
8.87
1.8
2.03
2.77
1.71
1.05
1.88
2.29
2.44
4.21
2.86
2.17
3.24
1.8
.
7.76
2.97
1.45
3.39
3.77
2.89
4.63
2.04
3.21
1.51
4.23
3.54
2.89
3.13
4.5
3.86
3.35
1.37
4.77
5.48
1.97
4.67
2.2
2.09
3.34
3.84
2.69
2.98
3.43
.
3.4
3.62
2.51
7.82
6.34
1.64
5.16
2.32
4.17
9.74
4.57
2.43
2.85
2.68
.
7.89
5.23
1.83
9.72
6.63
4.16
8.27
4.86
4.85
11.68
9.02
4.73
5.24
5.42
10.46
9.89
5.1
3.5
11.34
6.73
4.13
12.22
5.21
7.89
15.95
6.65
5.64
SHO
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
2.36
4.12
12.41
3.99
8.07
1.62
7.98
5.14
15.39
19.65
5.01
2.36
4.74
22.76
.
4.01
4.3
11.27
3.31
5.43
4.53
6.37
6.18
14.04
17.81
7.47
2.98
12.12
17.75
.
8.38
3.88
15.3
7.45
6.33
3.67
7.53
6.36
17.25
14.49
15.15
6.07
16.5
6.66
4.89
5.03
8.06
12.07
3.89
13.36
3.32
9.78
7.49
7.37
46.12
9.2
4.22
3.42
19.84
.
10.97
7.31
10.24
6.67
11.6
6.6
10.66
5.62
14.69
10.51
12.04
5.99
16.37
17.34
.
21.48
4.23
5.18
13.2
17.42
16.63
17.74
8.66
19.6
22.15
7.02
11.71
29.91
16.81
59.61
142
Marker Skill
SHO
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Full
Strike 1
1.71
1.65
.
7.96
1.76
1.97
2.19
2.81
0.92
2.05
2.89
2.97
4.91
Strike 2
1.54
1.86
1.3
7.46
3.17
1.45
2.95
4.34
2.83
4.34
1.98
2.67
2.33
Strike 3
2.27
2.95
3.54
4.1
2.85
1.33
5.06
3.7
2.26
4.5
1.49
1.56
2.62
Occluded
Strike 1
1.88
2.81
.
3.29
3.21
2.6
8.24
6.69
2.3
5.38
2.74
4.08
10.05
Strike 2
1.7
3.31
5.31
7
5.42
2.47
10.16
6.43
3.87
8.69
4.37
5.01
12.92
Strike 3
4.28
4.53
9.75
9.32
4.4
3.71
10.46
5.99
3.5
11.64
5.31
6.98
17.46
STEP
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
Novice
3.37
32.15
16.01
3.22
7.87
2.73
8.98
5.45
15.57
18.77
8.49
3.04
5.71
21.12
.
8.5
4.82
11.52
3.32
3.39
4.14
8.05
6.7
15.24
13.8
7.63
3.58
14.44
12.06
.
9.08
2.2
19.78
27.07
3.78
2.16
3.02
3.56
15.98
25.97
8
4.33
13.06
3.9
2.78
5.61
8.53
9.61
4.2
9.82
3.46
11.39
7.46
13.99
13.07
8.67
3.82
7.9
17.09
.
7.71
9.9
8.09
8.2
9.07
5.62
11.54
7.5
16.8
11.68
12.18
4.23
20.53
14.62
.
10.99
7.5
10.63
12.44
15.96
12.06
9.88
6.33
19.02
15.84
14.23
9.51
21.01
12.55
56.71
STEP
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
Skilled
1.78
1.97
.
17.73
2.31
1.71
3.03
1.69
2.68
1.82
2.23
3.88
2.58
2.02
2.21
1.76
2.32
1.72
6.14
2.2
0.64
2.57
6.58
1.28
3.31
1.92
0.8
3.08
2.12
2.87
2.55
3.99
12.05
1.8
5.49
1.44
3.29
2.83
2.64
4.8
2.17
1.6
2.71
1.79
4.52
2.46
3
.
5.56
3.92
0.99
7.07
8.52
18.19
6.45
3.49
4.67
8.7
4.7
2.5
2.22
3.55
4.63
7.79
3.31
1.41
9.71
7.46
3.86
11.4
5.65
7.46
11.31
6.69
4.03
5.59
8.8
9.68
9.41
6.01
3.55
14.37
5.89
4.38
12.75
6.41
8.87
18.54
8.79
3.6
143
APPENDIX G
ANOVA AT THE THREE EVENTS IN CENTIMETERS FOR SPARE TRIALS BY
SKILL LEVEL
144
WRT Spare Full 1
WRT Spare Full 2
WRT Spare Full 3
WRT Spare Occluded 1
WRT Spare Occluded 2
WRT Spare Occluded 3
ARM Spare Full 1
ARM Spare Full 2
ARM Spare Full 3
ARM Spare Occluded 1
ARM Spare Occluded 2
ARM Spare Occluded 3
N
Mean
SD
ANOVA
Novice
Skilled
Total
14
15
29
10.25
2.67
6.33
3.99
1.01
4.77
F (1, 27) = 50.69, p < .01
Novice
Skilled
Total
14
15
29
10.44
3.57
6.89
5.45
1.22
5.17
F (1, 27) = 22.66, p < .01
Novice
Skilled
Total
15
15
30
9.57
4.72
7.14
5.69
2.08
4.88
F (1, 28) = 9.61, p < .01
Novice
Skilled
Total
14
15
29
12.43
3.98
8.06
9.74
1.60
7.99
F (1, 27) = 11.00, p < .01
Novice
Skilled
Total
14
15
29
12.61
5.85
9.11
7.52
2.94
6.51
F (1, 27) = 10.44, p < .01
Novice
Skilled
Total
15
15
30
15.83
9.09
12.46
16.93
3.79
12.53
F (1, 28) = 2.26, p > .05
Novice
Skilled
Total
14
15
29
9.32
2.37
5.73
4.03
0.78
4.51
F (1, 27) = 43.05, p < .01
Novice
Skilled
Total
14
14
28
8.76
3.39
6.08
4.27
1.06
4.10
F (1, 26) = 20.80, p < .01
Novice
Skilled
Total
15
15
30
9.02
3.69
6.35
5.26
1.76
4.71
F (1, 28) = 13.85, p < .01
Novice
Skilled
Total
14
15
29
10.92
3.82
7.25
8.91
1.56
7.15
F (1, 27) = 9.25, p < .01
Novice
Skilled
Total
14
14
28
11.36
5.59
8.48
6.93
2.72
5.94
F (1, 26) = 8.40, p < .01
Novice
Skilled
Total
15
15
30
15.88
8.51
12.19
17.27
3.53
12.81
F (1, 27) = 2.62, p > .05
145
ELB Spare Full 1
ELB Spare Full 2
ELB Spare Full 3
ELB Spare Occluded 1
ELB Spare Occluded 2
ELB Spare Occluded 3
UPA Spare Full 1
UPA Spare Full 2
UPA Spare Full 3
UPA Spare Occluded 1
UPA Spare Occluded 2
UPA Spare Occluded 3
N
Mean
SD
ANOVA
Novice
Skilled
Total
14
15
29
9.06
2.35
5.59
4.08
0.67
4.43
F (1, 27) = 39.54, p < .01
Novice
Skilled
Total
14
14
28
8.23
3.26
5.75
3.79
1.13
3.73
F (1, 26) = 22.10, p < .01
Novice
Skilled
Total
15
15
30
8.92
3.47
6.19
5.21
1.63
4.70
F (1, 28) = 14.93, p < .01
Novice
Skilled
Total
14
15
29
10.38
3.89
7.02
8.49
1.52
6.75
F (1, 27) = 8.50, p < .01
Novice
Skilled
Total
14
14
28
10.54
5.42
7.98
6.45
2.66
5.50
F (1, 26) = 7.56, p < .05
Novice
Skilled
Total
15
15
30
15.93
8.25
12.09
17.20
3.45
12.80
F (1, 28) = 2.88, p > .05
Novice
Skilled
Total
14
15
29
8.99
2.40
5.58
4.23
0.70
4.45
F (1, 27) = 35.50, p < .01
Novice
Skilled
Total
14
14
28
7.95
3.30
5.63
3.62
1.10
3.54
F (1, 26) = 21.13, p < .01
Novice
Skilled
Total
15
15
30
9.08
3.11
6.10
5.68
1.43
5.07
F (1, 28) = 15.59, p < .01
Novice
Skilled
Total
14
15
29
10.17
4.00
6.98
8.14
1.53
6.46
F (1, 27) = 8.35, p < .01
Novice
Skilled
Total
14
14
28
10.23
5.36
7.79
6.40
2.57
5.39
F (1, 26) = 6.96, p < .01
Novice
Skilled
Total
15
15
30
16.20
8.20
12.20
17.38
3.64
12.99
F (1, 28) = 3.04, p > .05
146
SHO Spare Full 1
SHO Spare Full 2
SHO Spare Full 3
SHO Spare Occluded 1
SHO Spare Occluded 2
SHO Spare Occluded 3
STEP Spare Full 1
STEP Spare Full 2
STEP Spare Full 3
STEP Spare Occluded 1
STEP Spare Occluded 2
STEP Spare Occluded 3
N
Mean
SD
ANOVA
Novice
Skilled
Total
14
15
29
9.03
2.24
5.52
4.66
0.56
4.70
F (1, 27) = 31.44, p < .01
Novice
Skilled
Total
14
15
29
7.76
3.12
5.36
3.81
0.82
3.55
F (1, 27) = 21.27, p < .01
Novice
Skilled
Total
15
15
30
8.90
2.85
5.88
5.70
1.39
5.11
F (1, 28) = 15.91, p < .01
Novice
Skilled
Total
14
15
29
9.21
4.13
6.58
7.84
1.85
6.08
F (1, 27) = 5.97, p < .05
Novice
Skilled
Total
14
15
29
10.03
5.14
7.50
6.51
2.66
5.42
F (1, 27) = 7.20, p < .05
Novice
Skilled
Total
15
15
30
16.68
7.62
12.15
17.19
3.67
13.06
F (1, 28) = 3.99, p > .05
Novice
Skilled
Total
14
15
29
7.88
2.19
4.94
3.37
0.92
3.75
F (1, 27) = 39.81, p < .01
Novice
Skilled
Total
14
15
29
8.55
2.51
5.43
4.18
0.91
4.24
F (1, 27) = 29.87, p < .01
Novice
Skilled
Total
15
15
30
7.63
2.62
5.13
6.05
1.49
5.02
F (1, 28) = 9.67, p < .01
Novice
Skilled
Total
14
15
29
8.81
3.88
6.26
8.09
1.84
6.20
F (1, 27) = 5.30, p < .05
Novice
Skilled
Total
14
15
29
10.58
5.40
7.90
6.98
2.62
5.74
F (1, 27) = 7.18, p < .05
Novice
Skilled
Total
15
15
30
19.16
7.53
13.34
18.71
3.78
14.52
F (1, 28) = 5.56, p < .05
147
APPENDIX H
ANOVA AT THE THREE EVENTS IN CENTIMETERS FOR STRIKE TRIALS BY
SKILL LEVEL
148
WRT Strike Full 1
WRT Strike Full 2
WRT Strike Full 3
WRT Strike Occluded 1
WRT Strike Occluded 2
WRT Strike Occluded 3
ARM Strike Full 1
ARM Strike Full 2
ARM Strike Full 3
ARM Strike Occluded 1
ARM Strike Occluded 2
ARM Strike Occluded 3
N
Mean
SD
ANOVA
Novice
Skilled
Total
14
14
28
10.10
2.90
6.50
7.40
2.44
6.53
F (1, 26) = 11.94, p < .01
Novice
Skilled
Total
14
15
29
11.26
3.71
7.36
7.28
1.83
6.40
F (1, 27) = 15.14, p < .01
Novice
Skilled
Total
15
15
30
9.74
4.54
7.14
5.14
1.52
4.57
F (1, 28) = 14.12, p < .01
Novice
Skilled
Total
14
14
28
11.35
4.44
7.90
6.47
2.04
5.87
F (1, 26) = 14.52, p < .01
Novice
Skilled
Total
14
15
29
16.56
6.40
11.30
9.33
2.89
8.44
F (1, 27) = 16.15, p < .01
Novice
Skilled
Total
15
15
30
18.24
8.26
13.25
13.26
3.72
10.83
F (1, 28) = 7.89, p < .01
Novice
Skilled
Total
14
14
28
9.15
2.70
5.92
6.76
2.22
5.93
F (1, 26) = 11.51, p < .01
Novice
Skilled
Total
14
14
28
10.08
3.61
6.84
6.34
1.62
5.61
F (1, 26) = 13.67, p < .01
Novice
Skilled
Total
15
15
30
9.32
3.83
6.57
4.82
1.27
4.45
F (1, 28) = 18.12, p < .01
Novice
Skilled
Total
14
14
28
9.60
4.26
6.93
5.28
2.22
4.82
F (1, 26) = 12.16, p < .01
Novice
Skilled
Total
14
14
28
13.96
6.08
10.02
6.63
2.84
6.42
F (1, 26) = 16.69, p < .01
Novice
Skilled
Total
15
15
30
18.09
7.86
12.98
13.64
3.26
11.04
F (1, 28) = 7.98, p < .01
149
ELB Strike Full 1
ELB Strike Full 2
ELB Strike Full 3
ELB Strike Occluded 1
ELB Strike Occluded 2
ELB Strike Occluded 3
UPA Strike Full 1
UPA Strike Full 2
UPA Strike Full 3
UPA Strike Occluded 1
UPA Strike Occluded 2
UPA Strike Occluded 3
N
Mean
SD
ANOVA
Novice
Skilled
Total
14
14
28
8.70
2.74
5.72
6.43
2.13
5.60
F (1, 26) = 10.86, p < .01
Novice
Skilled
Total
14
14
28
9.64
3.44
6.54
6.12
1.56
5.40
F (1, 26) = 13.48, p < .01
Novice
Skilled
Total
15
15
30
9.33
3.49
6.41
4.99
1.18
4.63
F (1, 28) = 19.46, p < .01
Novice
Skilled
Total
14
14
28
9.12
4.18
6.65
4.99
2.21
4.54
F (1, 26) = 11.49, p < .01
Novice
Skilled
Total
14
14
28
12.18
5.97
9.08
5.45
2.90
5.32
F (1, 26) = 14.14, p < .01
Novice
Skilled
Total
15
15
30
17.99
7.88
12.93
13.64
3.41
11.04
F (1, 28) = 7.75, p < .01
Novice
Skilled
Total
14
14
28
8.43
2.70
5.57
6.33
1.93
5.44
F (1, 26) = 10.49, p < .01
Novice
Skilled
Total
14
14
28
9.04
3.32
6.18
5.89
1.61
5.14
F (1, 26) = 12.28, p < .01
Novice
Skilled
Total
15
15
30
9.20
3.34
6.27
4.77
1.18
4.53
F (1, 28) = 21.29, p < .01
Novice
Skilled
Total
14
14
28
8.80
4.30
6.55
4.64
2.29
4.26
F (1, 26) = 10.63, p < .01
Novice
Skilled
Total
14
14
28
10.90
6.03
8.46
4.49
2.92
4.47
F (1, 26) = 11.58, p < .01
Novice
Skilled
Total
15
15
30
18.13
7.69
12.91
13.93
3.54
11.31
F (1, 28) = 7.92, p < .01
150
SHO Strike Full 1
SHO Strike Full 2
SHO Strike Full 3
SHO Strike Occluded 1
SHO Strike Occluded 2
SHO Strike Occluded 3
STEP Strike Full 1
STEP Strike Full 2
STEP Strike Full 3
STEP Strike Occluded 1
STEP Strike Occluded 2
STEP Strike Occluded 3
N
Mean
SD
ANOVA
Novice
Skilled
Total
14
14
28
8.26
2.81
5.53
6.74
1.76
5.57
F (1, 26) = 8.56, p < .01
Novice
Skilled
Total
14
15
29
8.40
2.92
5.56
5.22
1.57
4.66
F (1, 26) = 15.09, p < .01
Novice
Skilled
Total
15
15
30
9.33
2.89
6.11
4.89
1.11
4.78
F (1, 28) = 24.72, p < .01
Novice
Skilled
Total
14
14
28
10.94
4.31
7.63
11.11
2.44
8.59
F (1, 26) = 4.75, p < .05
Novice
Skilled
Total
14
15
29
10.47
5.90
8.11
3.79
2.97
4.06
F (1, 27) = 13.14, p < .01
Novice
Skilled
Total
15
15
30
18.09
7.38
12.74
13.45
3.82
11.14
F (1, 26) = 8.79, p < .01
Novice
Skilled
Total
14
14
28
10.89
3.40
7.15
8.71
4.17
7.71
F (1, 26) = 8.43, p < .01
Novice
Skilled
Total
14
15
29
8.37
2.62
5.40
4.34
1.70
4.33
F (1, 27) = 22.72, p < .01
Novice
Skilled
Total
15
15
30
9.64
3.58
6.61
8.71
2.65
7.04
F (1, 28) = 6.66, p < .05
Novice
Skilled
Total
14
14
28
8.90
5.73
7.32
4.01
4.26
4.37
F (1, 26) = 4.11, p > .05
Novice
Skilled
Total
14
15
29
10.55
6.03
8.21
4.44
3.14
4.40
F (1, 27) = 10.12, p < .01
Novice
Skilled
Total
15
15
30
15.64
8.44
12.04
12.05
4.20
9.59
F (1, 28) = 4.78, p < .05
151
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BIOGRAPHICAL SKETCH
Kevin Harris was born in Jackson, Tennessee in 1973. He completed a Bachelor of
Science degree in Psychology at the University of Tennessee at Martin and earned his
Master of Science degree in Experimental Psychology at Mississippi State University,
earning an award for outstanding master’s-level research. He later enrolled at Florida State
University to work with Professor K. Anders Ericsson and Dr. Paul Ward. His current
primary research interests involve the exploration of all aspects of expertise and expert
performance.
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