Brain and Cognition 82 (2013) 1–5
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Brain and Cognition
journal homepage: www.elsevier.com/locate/b&c
Aging in movement representations for sequential finger movements: A comparison
between young-, middle-aged, and older adults
Priscila Caçola a,⇑, Jerroed Roberson a, Carl Gabbard b
a
b
Developmental Motor Cognition Lab, Center for Healthy Living and Longevity, The University of Texas at Arlington, United States
Motor Development Lab, Department of Health and Kinesiology, Texas A&M University, United States
a r t i c l e
i n f o
Article history:
Accepted 4 February 2013
Keywords:
Aging
Sequential finger movements
Mental representation
Motor imagery
a b s t r a c t
Studies show that as we enter older adulthood (>64 years), our ability to mentally represent action in the
form of using motor imagery declines. Using a chronometry paradigm to compare the movement
duration of imagined and executed movements, we tested young-, middle-aged, and older adults on their
ability to perform sequential finger (fine-motor) movements. The task required number recognition and
ordering and was presented in three levels of complexity. Results for movement duration indicated no
differences between young- and middle-aged adults, however both performed faster than the older
group. In regard to the association between imagined and executed actions, correlation analyses indicated that values for all groups were positive and moderate (r’s .80, .76, .70). In summary, whereas the
older adults were significantly slower in processing actions than their younger counterparts, the ability
to mentally represent their actions was similar.
Ó 2013 Elsevier Inc. All rights reserved.
1. Introduction
In order to perform skilled motor actions, it is necessary to
create an appropriate and effective movement representation used
to plan and execute motions. The nature of mental (movement)
representation is a central issue for understanding cognitive and
motor development across the lifespan. The concept of mental
representation has been cast from several perspectives. One of
the more common views is that mental representation is an internal cognitive construct that represents external reality. This interpretation contends that action representation is a key feature of an
internal forward model, a neural system that simulates the
dynamic behavior of the body in relation to the environment
(Wolpert, 1997). These representations are hypothesized to be an
integral part of action planning (Caeyenberghs, Tsoupas, Wilson,
& Smits-Engelsman, 2009; Molina, Tijus, & Jouen, 2008).
Central to the present paper is the notion that motor imagery is
part of an internal forward model and is equivalent to a ‘plan’ of
the action to follow; that is, it reflects an internal action representation (Jeannerod, 2001; Munzert & Zentgraf, 2009). The basis for
much of the discussion related to the role of motor imagery in
action representation and planning is the so-called equivalence
hypothesis (e.g., Jeannerod, 2001); suggesting that motor
simulation and motor control processes are functionally equivalent
⇑ Corresponding author. Address: Department of Kinesiology, University of Texas
at Arlington, 500 W. Nedderman Dr., Arlington, TX 76019, United States.
E-mail address: [email protected] (P. Caçola).
0278-2626/$ - see front matter Ó 2013 Elsevier Inc. All rights reserved.
http://dx.doi.org/10.1016/j.bandc.2013.02.003
(Kunz, Creem-Regehr, & Thompson, 2009; Lorey et al., 2010;
Munzert & Zentgraf, 2009; Ramsey, Cummings, Eastough, &
Edwards, 2010).
Using the general motor imagery paradigm, studies have shown
that advanced age (>64 years) is associated with functional
decrements in the ability to mentally represent action (e.g., Gabbard, Caçola, & Cordova, 2011b; Mulder, Hochstenbach, Heuvelena,
& Otter, 2008; Personnier, Bally, & Papaxanthis, 2010a; Saimpont,
Mourey, Manckoundia, Pfitzenmeyer, & Pozzo, 2010; Skoura, Personnier, Vinter, Pozzo, & Papaxanthis, 2008). According to Gabbard,
Caçola, and Bobbio (2011a), the vast majority of previous work
used tasks requiring the simulation and execution of gross-motor
movements; namely the trunk, shoulders and limbs, with the
exception of their investigation using a chronometry paradigm
(described later) to compare the movement duration of imagined
and executed sequential finger movements between children and
young adults. The underlying intent, as with the present study,
was to gain a better understanding of the age-related ability to
create internal models for action requiring fine-motor movements.
The researchers found that 7-year-olds and adults were significantly different from 9- and 11-year-olds. Our goal with the present study, using the same paradigm, was to examine possible aging
effects by comparing young-, middle-aged, and older adults.
Behaviorally, one of the most common tactics used to examine
movement representation via motor simulation is chronometry.
Specifically, chronometry paradigms measure the correspondence
between the time-course of the participant’s imagined (I) and
executed (E) actions (Gabbard et al., 2011a). This tactic follows
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P. Caçola et al. / Brain and Cognition 82 (2013) 1–5
the premise that there is a functional relationship between motor
imagery and execution. Theoretically, the closer the relationship
between I and E movement time-course, the more refined the action representation (internal model). This method has proven to
give reliable and replicable results (e.g., see review by Malouin,
Richards, Durand, & Doyon, 2008; Sirigu et al., 1996). With adults,
durations of imagined movements have been reported not to differ
significantly from executed movements (e.g., Calmels, Holmes, Lopez, & Naman, 2006; Carrillo, Galdo-Alvarez, & Lastra-Barreira,
2008; Louis, Guillot, & Maton, 2008; Sabate, Gonzalez, & Rodriguez,
2007).
For example, Personnier, Kubicki, Laroche, and Papaxanthis
(2010b) used a chronometry paradigm with imagined and executed arm pointing (gross-motor) actions in young and older
adults and found that whereas older adults displayed the ability
to mentally represent action, the quality (i.e., isochrony between
executed and imagined movements) declined with advancing
age. From another perspective, those findings indicate that there
is likelihood of weakness in internal models of action in the elderly.
In that study and a subsequent investigation (Personnier et al.,
2010b), the researchers concluded that the decline in motor imagery might reflect functional changes in the aging brain; for example, the parietal cortex. Complementing those reports, Saimpont
et al. (2010) reported a significant age effect regarding the ability
to mentally simulate a complex sequential action involving the
whole body (rising from the floor). That is, compared to younger
adults, older persons displayed a significant level of difficulty.
Even though functional decrements with aging have been well
established in the literature, it is still unknown when declines
associated with movement representation begin; which is especially relevant in regard to the representation of fine-motor movements. To that end, we compared imagined (I) and executed (E)
movements of young adults (18–32 years), middle-aged adults
(40–63 years), and older adults (65–93 years) using a chronometry
paradigm with a task involving sequential finger (fine-motor)
movements. We predicted that the ability to mentally represent
action would decrease gradually with advancing age. That is, there
would be a larger difference between I and E conditions as age increased. We also anticipated that movement duration would slow
as age increased. As noted earlier, our goal was to gain a better
understanding of advancing age during adulthood on the ability
to mentally represent action requiring fine-motor movements.
2. Method
2.1. Participants
The study involved a convenience sample of 99 participants
with ages ranging between 18 and 93 years of age. Participants
were divided in three age groups: Young adults (n = 33,
M = 22.30, SD = 2.72, range 18–32 years, 16 females, 17 males),
middle-aged adults (n = 33, M = 49.76, SD = 6.09, range 40–
63 years, 19 females, 14 males), and older adults (n = 33,
M = 74.52, SD = 6.69, range 65–93 years, 18 females, 15 males).
All participants completed an eligibility questionnaire and were
excluded from the study if they had any of the following: known
visual conditions or impairments affecting daily function (e.g.
reading, driving, etc.); neurological disorders, diagnosed cognitive
decline and low endurance or inability to maintain stance while
seated. The experimental protocol and consent form were approved by the Institutional Review Board (IRB) for the ethical treatment of human subjects at the University of Texas at Arlington.
Participants received written and verbal descriptions of the
experimental procedures and voluntarily signed a consent form
before participating in this study.
2.2. Task and procedure
Participants were tested individually in an isolated testing
room. The setup included the use of a dual monitor computer with
two 20-in. screens positioned back-to-back 6 in. from each other so
that each of the monitors faced either the participant or the experimenter. This set-up allowed the experimenter to control what the
participant viewed each time a trial was presented. Participants
were seated comfortably in an upright position facing the computer monitor that was approximately 12 in. away with their dominant hand placed palm down on the table in front of them at
midline and the opposite hand resting on the thigh on the same
side. Height of the chair was adjusted in order to match the participant’s head height to the top of the computer screen. Movement
duration was programmed via computer to begin after the ‘Get
Ready’ cue and completed by an experimenter via keypad (sitting
adjacent to the participant) timed with the participant’s ‘Stop’ response. Programming was created using MatLab software. The test
was modified from adult work by Sabate, Gonzalez, and Rodriguez
(2004) and the experimental paradigm has been reported elsewhere with children and young adults (Gabbard et al., 2011a).
The task involved producing sequential finger movements
through imagined (I) and actual executed (E) movements. Numbered stickers were placed on the proximal segment of the fingers,
numbered 1 through 5 on each finger, from their little finger
through to their thumb. Movement sequences of 3, 4, and then 5
numbers (representing number load) appeared on the screen,
and the participant was asked to either imagine lifting and tapping
(I condition) or actually executing the task; corresponding numbered finger to match the numbers that appeared on the screen
(example, Fig. 1). Participants used the dominant hand with the
palm down and the wrist and the fingertips resting on the table
surface. All fingers were slightly flexed as if they were prepared
to begin typing on a keyboard. During testing, participants sat upright and remained relaxed. Movements of each finger began with
a dorsal extension separating the fingertip from the table surface
and were followed by a ventral flexion that returned the finger
to its original position on the table. Each trial was composed of
repetitive movements performed by different fingers. Once the sequence of taps was completed, the participant said ‘Stop’. This was
repeated 5 times each for both E and I conditions at each of the
three load levels. Prior to the imagery condition, participants were
trained to use motor (kinesthetic) imagery, meaning that they
were trained to focus on and feel the individual effector (finger),
thereby being more sensitive to the biomechanical constraints of
the task (Johnson, Corballis, & Gazzaniga, 2001; Sirigu & Duhamel,
2001; Stevens, 2005).
Participants were randomly assigned to I or E condition to start
the task which was then reversed for the start of the next level of
complexity. Before the series of numbers appeared on the monitor,
the participant was prompted with a ‘‘Get Ready’’ visual cue lasting
2 s. Participants were allowed breaks every block of 5 trials to
Fig. 1. Illustration of the general experimental setup.
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P. Caçola et al. / Brain and Cognition 82 (2013) 1–5
Table 1 shows movement duration values for the significant
interactions, Age Load, F(4, 96) = 10.38, p < .01, g2p = .178,
Age Condition, F(2, 96) = 4.57, p < .05, g2p = .087, and Load Condition, F(2, 192) = 3.61, p < .05, g2p = .036. For Age Load, simple
main effect analysis revealed that young and middle-aged adults
did not differ from each other in any load, while older adults were
slower than both groups in all loads. For Age Condition, as in
Age Load, simple main effect analysis indicated that both young
and middle-aged adults were faster than older adults in both execution and imagination. Each group was faster in the imagined
compared to the executed condition. For Load Condition, simple
main effect analysis showed that in all possible combinations, load
3 was performed faster than load 4, which was performed faster
than load 5. For all loads, the imagined duration was always faster
than the executed duration.
Correlation analysis for Condition and Age indicated there were
moderate positive correlations between imagined and executed
movements for young adults (r = .80, p < .01), middle-aged adults
(r = .76, p < .01), and older adults (r = .70, p < .01). Fisher’s-Z results
for the size of the correlations between E and I responses and Age
indicated no significant differences.
avoid fatigue. To limit memorization of finger location the order of
the numbered stickers was systematically changed with each level.
For the first level, in which 3 numbers appeared on screen, fingers
were numbered 1–5 starting from the little finger to the thumb;
with 4 numbers the order was 4, 5, 3, 2, 1, and for the sequence
of 5 numbers, order was 3, 5, 2, 1, 4.
2.3. Treatment of the data
The data were analyzed using a 3 [Age] 3 [Load] 2 [Condition] mixed-design ANOVA procedure, where Age was a between-subject factor [young adults, middle-aged adults, older
adults], and Load [Levels 3, 4, and 5] and Condition [Imagined/Executed] were within-subject factors. Movement duration (in seconds) was used as the dependent variable. Tukey post hoc and
simple main effect analyses were performed when appropriate.
To determine the relationship between E and I conditions across
age groups, each participant’s mean executed movement duration
was plotted against his or her mean imagined movement duration
for each level using Pearson’s product moment correlation
calculations.
To test whether the size of the correlations between E and I responses significantly differed between the age groups, Fisher’s Z
was utilized. According to this test, if the Z value was above 1.96,
the correlations were significantly different at the p < .05 level,
and if the Z value was 2.58 or over, the correlations were significantly different at the p < .01 level.
4. Discussion
This investigation compared young-, middle-aged, and older
adults with the intent of examining advanced aging effects on
the ability to mentally represent sequential finger movements.
We predicted that movement representation ability would decrease gradually with increasing age. More specifically, we anticipated that there would be a weaker relationship between I and E
conditions with increasing age. We also predicted that movement
durations would slow as age increased.
In reference to movement duration, our results indicated an age
effect. While no statistical distinctions were found between young
and middle-aged adults, both groups performed faster than older
adults in every condition and load. Perhaps the most unexpected
finding was that scores for the young- and middle-aged adults
were similar for movement duration. In reference to the association (chronometry) between conditions, moderate relationships
were found for each age group with a slight decrease with age
(r’s .80, .76, and .70); differences were not statistically significant.
Previous reports on aging and fine-motor movements involving
timing, sequencing, and executive control processing generally
indicate that with advanced aging there is a slowing of processing
3. Results
Duration (sec)
Fig. 2 illustrates the comparison between E and I responses by
Age. ANOVA results indicated significant effects for Age,
F(2, 96) = 20.60, p < .01, g2p = .300, Load, F(2, 192) = 446.7, p < .01,
g2p = .823, and Condition, F(1, 96) = 97.46, p < .01, g2p = .504. Posthoc analysis for Age indicated that older adults (M = 4.89,
SD = 2.5) were slower than young (M = 3.56, SD = .5) and middleaged adults (M = 3.84, SD = .5). Young and middle-aged adults were
not different from each other. For Load, post hoc analysis revealed
that all were significantly different, with movement duration
shorter on Level 3 (M = 2.95, SD = .83) compared to Levels 4 and
5, which were also different (Level 4: M = 4.22, SD = 1.1; Level 5:
M = 5.11, SD = 1.3). For Condition, the mean for E movements
(M = 4.43, SD = 1.1) was higher than for I movements (M = 3.76,
SD = 2.9).
9.00
8.50
8.00
7.50
7.00
6.50
6.00
5.50
5.00
4.50
4.00
3.50
3.00
2.50
2.00
1.50
1.00
0.50
0.00
Young
Middle-Aged
Older
Executed
Imagined
Level 3
Executed
Imagined
Level 4
Executed
Imagined
Level 5
Fig. 2. Comparison between E and I condition responses by level and age group.
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P. Caçola et al. / Brain and Cognition 82 (2013) 1–5
Table 1
Mean values (seconds) for movement duration for age, Condition, and Load.
Young adults
Mean
Middle-aged adults
SD
Mean
SD
Load 3
Executed
Imagined
2.78
2.43
.55
.53
3.08
2.60
.72
.93
3.73
3.07
1.24
.89
Load 4
Executed
Imagined
4.02
3.43
.91
1.03
4.23
3.56
1.03
1.14
5.45
4.65
1.16
1.25
Load 5
Executed
Imagined
4.51
4.20
.97
1.04
5.24
4.31
1.09
1.39
6.87
5.55
1.78
1.52
and performance (for example, see review by Krampe, 2002;
Seidler, Alberts, & Stalmach, 2009), so the result found here was
not surprising. That is, other than the observation that there was
no difference between young- and middle-age groups. More interesting, however; was the finding of strong correlations between
imagined and executed movements for all age groups. In view of
the results for the oldest group, we were somewhat surprised that
they were able to maintain a moderate relationship between the
two conditions (r = .70). In this study, the older group displayed a
positive and relatively strong association between imagined and
executed movements as did the younger groups, but was slower
than their young counterparts. Would this be typical of normal
healthy older adults? We do wish to note that the older adults in
our sample may not be typical of the average aging population,
as they were all regular attendees (three times a week) in a university program that included Pilates and Wii exercise, and most
tended to engage in other types of physical activities outside of
the program. Whereas we can only speculate given the design
used, there are indications in the literature that level of physical
fitness may serve as a neuroprotective function against the detriments of advanced aging (Hillman, Erickson, & Kramer, 2008; Nemati, Hosseini, Nemati, & Esmaily, 2012). Obviously, this
suggestion requires future inquiry using participants with a range
of weekly physical activity habits.
Agreeing with Gabbard, Caçola and Bobbio’s study (2011a), our
results also showed a decrease in performance, as indicated by
longer response times with increasing task complexity (e.g.,
Choudhury, Charman, Bird, & Blakemore, 2007; Young, Pratt, &
Chau, 2009). In reference to E and I responses, in every case (Load
and Age), movement durations took longer in E compared to I conditions. Imagined (virtual) responses were always faster; a finding
that has been reported previously (e.g., Anquetil & Jeannerod,
2007; Calmels et al., 2006; Gabbard et al., 2011a; Grealy & Shearer,
2007). Gabbard and colleagues offered the explanation with which
we agree on: since during imagined movements the participant
does not ‘actually’ encounter the biomechanical constraints of
the task, imagined times are typically faster.
4.1. Brain involvement
Several studies using healthy young adults have demonstrated
that most of the brain regions that are active during overt movement execution, such as the parietal cortex, premotor cortex, basal
ganglia, and cerebellum, are also active during mental simulation
(Decety et al., 1994). The parietal cortex is suspected to be the area
where motor images are stored (Gerardin et al., 2000) and the cerebellum is associated with the ability to process movements in
time (Blakemore & Sirigu, 2003; Rivkin et al., 2003).
Regarding advanced age and specific areas activated during a
finger-movement task, Hutchinson et al. (2002) reported patterns
of activation in the contralateral primary sensorimotor cortex
(SMC), lateral premotor cortex (PMC), supplementary motor area
(SMA), and ipsilateral cerebellum. More important to the present
Mean
Older adults
SD
study, the authors report significant differences in activation patterns with aging, with the older group displaying significantly
greater activation than the younger group in a widespread distribution involving both motor and non-motor areas. This ‘‘age-related cerebral activation patterns’’ within the motor systems
suggests that older subjects maintain level of performance
by recruiting additional brain regions (Rypma, Berger, Genova,
Rebbechi, & D’Esposito, 2005; Ward & Frackowiak, 2003). Speculately, that may be one factor that contributed to performance of
the older adults in our study.
Using a gross-motor task (walking) with older adults,
Personnier et al. (2010b) reported that the timing of actual and
imagined movements was dissimilar. It is possible that our task,
because of its fine-motor and repetitive nature, allowed participants more control over maintaining performance levels between
actual and imagined movements. Movement speed, however, was
much slower for older- compared to younger adults, which could
be due to a variety of factors such as sarcopenia (Narici, Maganaris,
Reeves, & Capodaglio, 2003), reduction in motor unit activation
capacity (Winegard, Hicks, Sale, & Vandervoort, 1996), coactivation
of antagonist muscles (Klein, Rice, & Marsh, 2001; Seidler, Alberts,
& Stalmach, 2002), decline in several aspects of proprioceptive sensitivity (Verschueren, Brumagne, Swinnen, & Cordo, 2002) and
reduction in the central mechanisms operating during visuomotor
information processing (Briggs, Raz, & Marks, 1999; Ketcham,
Seidler, Van Gemmert, & Stelmach, 2002; Smith et al., 1999). Most
likely however, older participants here matched their imagined
finger taps to their executed movements, and not the other way
around.
In summary, these results indicate that the ability to mentally
represent action in the context of sequential finger (fine-motor)
movements is relatively preserved through older adulthood. We
predicted that the ability to mentally represent action would decrease gradually with advancing age, but instead, it appears that
there is a significant drop in speed of response at the beginning
of older adulthood. Indeed this is an interesting finding, given that
most reports of this nature use gross-motor oriented tasks, showing a decline for ages >64 (Mulder et al., 2008; Personnier et al.,
2010a; Saimpont et al., 2010; Skoura et al., 2008). In regard to
the speed of processing (movement duration), our results did indicate a significant decline beginning around the age of 65 years.
To our knowledge, this is the first study mapping aging and motor representations via sequential (fine-motor) finger movements,
across adulthood (young- to older adults). As noted earlier, several
studies show that with more gross-motor type actions, mental representation declines with advanced age, typically in the mid 60s.
From a clinical and applied perspective, this information provides
an insight into when the typical aging-related decline starts with
the representation and internal modeling of fine-motor movements. For example, if a middle-aged individual displays a significant decline in the ability to mentally represent action with their
fingers, it could be an indication of future difficulty in planning
movements. Such actions, even among the elderly, are important
P. Caçola et al. / Brain and Cognition 82 (2013) 1–5
to daily living skills and communication. The important message
here is that clinicians should be aware that healthy older adults
are slower than younger adults, but are able to create relatively
accurate internal models for action. Further experiments are
needed to investigate different types of fine-motor actions and
the effects of training and practice on performance.
References
Anquetil, T., & Jeannerod, M. (2007). Simulated actions in the first and in the third
person perspectives share common representations. Brain Research, 1130(3),
125–129.
Blakemore, S. J., & Sirigu, A. (2003). Action prediction in the cerebellum and in the
parietal lobe. Experimental Brain Research, 153, 239–245.
Briggs, S. D., Raz, N., & Marks, W. (1999). Age-related deficits in generation and
manipulation of mental images: I. The role of sensorimotor speed and working
memory. Psychology and Aging, 14, 427–435.
Caeyenberghs, K., Tsoupas, J., Wilson, P. H., & Smits-Engelsman, B. C. M. (2009).
Motor imagery in primary school children. Developmental Neuropsychology,
34(1), 103–121.
Calmels, C., Holmes, P., Lopez, E., & Naman, V. (2006). Chronometric comparison of
actual and imaged complex movement patterns. Journal of Motor Behavior,
38(5), 339–348.
Carrillo, M. T., Galdo-Alvarez, S., & Lastra-Barreira, C. (2008). Equivalent is not equal:
Primary motor cortex (MI) activation during motor imagery and execution of
sequential movements. Brain Research, 1226, 134–143.
Choudhury, S., Charman, T., Bird, V., & Blakemore, S. (2007). Adolescent
development of motor imagery in a visually guided pointing task.
Consciousness and Cognition, 16(4), 886–896.
Decety, J., Perani, D., Jeannerod, M., Bettinardi, V., Tadary, B., Woods, R., et al. (1994).
Mapping motor representations with positron emission tomography. Nature,
371(6498), 600–602.
Gabbard, C., Caçola, P., & Bobbio, T. (2011a). Examining age-related action
representation and execution via sequential finger movements. Brain and
Cognition, 77, 459–463.
Gabbard, C., Caçola, P., & Cordova, A. (2011b). Is there an advanced aging effect on
the ability to mentally represent action? Archives of Gerontology and Geriatrics,
53, 206–209.
Gerardin, E., Sirigu, A., Lehericy, S., Poline, J., Gaymard, B., Marsault, C., et al. (2000).
Partially overlapping neural networks for real and imagined hand movements.
Cerebral Cortex, 10, 1093–1104.
Grealy, M., & Shearer, G. (2007). Timing processes in motor imagery. European
Journal of Cognitive Psychology, 20(5), 867–892.
Hillman, C. H., Erickson, K. I., & Kramer, A. F. (2008). Be smart, exercise your heart:
Exercise effects on brain and cognition. Nature Reviews Neuroscience, 9, 58–65.
Hutchinson, S., Kobayashi, M., Horkan, C. M., Pascual-Leone, A., Alexander, M. P., &
Schlaug, G. (2002). Age-related differences in movement representation.
NeuroImage, 17, 1720–1728.
Jeannerod, M. (2001). Neural simulation of action: A unifying mechanism for motor
cognition. Neuroimage, 14, 103–109.
Johnson, S., Corballis, P., & Gazzaniga, M. (2001). Within grasp but out of reach:
Evidence for a double dissociation between imagined hand and arm movements
in the left cerebral hemisphere. Neuropsychologia, 39, 36–50.
Ketcham, C. J., Seidler, R. D., Van Gemmert, A. W. A., & Stelmach, G. E. (2002). Agerelated differences as influenced by task difficulty, target size, and movement
amplitude. Journals of Gerontology Series B: Psychological Sciences and Social
Sciences, 57, 54–64.
Klein, C. S., Rice, C. L., & Marsh, G. D. (2001). Normalized force, activation, and
coactivation in the arm muscles of young and old men. Journal of Applied
Physiology, 91, 1341–1349.
Krampe, R. T. (2002). Aging, expertise and fine motor movement. Neuroscience &
Biobehavioral Reviews, 26(7), 769–776.
Kunz, B. R., Creem-Regehr, S. H., & Thompson, W. B. (2009). Evidence for motor
simulation in imagined locomotion. Journal of Experimental Psychology: Human
Perception and Performance, 35(5), 1458–1471.
Lorey, B., Pilgramm, S., Walter, B., Stark, R., Munzert, J., & Zentgraf, K. (2010). Your
mind’s hand: Motor imagery of pointing movements with different accuracy.
NeuroImage, 49(2), 3239–3247.
5
Louis, M., Guillot, A., & Maton, S. (2008). Effect of imagined movement speed on
subsequent motor performance. Journal of Motor Behavior, 40(2), 117–132.
Malouin, F., Richards, C. L., Durand, A., & Doyon, J. (2008). Reliability of mental
chronometry for assessing motor imagery: Ability after stroke. Archives of
Physical Medicine and Rehabilitation, 89(2), 311–319.
Molina, M., Tijus, C., & Jouen, F. (2008). The emergence of motor imagery in children.
Journal of Experimental Child Psychology, 99(3), 196–209.
Mulder, T., Hochstenbach, J. B. H., Heuvelena, M. J. G., & Otter, A. R. (2008). Motor
imagery: The relation between age and imagery capacity. Human Movement
Science, 26, 203–211.
Munzert, J., & Zentgraf, K. (2009). Motor imagery and its implications for
understanding the motor system. Progress in Brain Research, 174, 219–229.
Narici, M. V., Maganaris, C. N., Reeves, N. D., & Capodaglio, P. (2003). Effect of aging
on human muscle architecture. Journal of Applied Physiology, 95, 2229–2234.
Nemati, K. H., Hosseini, M., Nemati, M., & Esmaily, H. O. (2012). Lifelong physical
activity affects mini mental state exam scores in individuals over 55 years of
age. Journal of Bodywork and Movement Therapies, 16(2), 230–235.
Personnier, P., Bally, Y., & Papaxanthis, C. (2010a). Mentally represented motor
actions in normal aging III: Electromyographic features of imagined arm
movements. Behavioural Brain Research, 206, 184–190.
Personnier, P., Kubicki, A., Laroche, D., & Papaxanthis, C. (2010b). Temporal features
of imagined locomotion in normal aging. Neuroscience Letters, 476, 146–149.
Ramsey, R., Cummings, J., Eastough, D., & Edwards, M. (2010). Incongruent imagery
interferes with action initiation. Brain and Cognition, 74(3), 249–254.
Rivkin, M., Vajapeyam, S., Hutton, C., Weiler, M., Hall, K., et al. (2003). A functional
magnetic resonance imaging study of paced finger tapping in children. Pediatric
Neurology, 28(2), 89–95.
Rypma, B., Berger, J. S., Genova, H. M., Rebbechi, D., & D’Esposito, M. (2005).
Dissociating age-related changes in cognitive strategy and neural efficiency
using event-related fMRI. Cortex, 41(4), 582–594.
Sabate, M., Gonzalez, B., & Rodriguez, M. (2004). Brain lateralization of motor
imagery: Motor planning asymmetry as a cause of movement lateralization.
Neuropsychologia, 42(8), 1041–1049.
Sabate, M., Gonzalez, B., & Rodriguez, M. (2007). Adapting movement planning to
motor impairments: The motor-scanning system. Neuropsychologia, 45(8),
378–386.
Saimpont, A., Mourey, F., Manckoundia, P., Pfitzenmeyer, P., & Pozzo, T. (2010).
Aging effects on the mental simulation/planning of the rising from the floor
sequence. Archives of Gerontology and Geriatrics, 51(3), 41–45.
Seidler, R. D., Alberts, J. L., & Stalmach, G. E. (2002). Change in multi-joint
performance with age. Motor Control, 6, 19–31.
Sirigu, A., & Duhamel, J. R. (2001). Motor and visual imagery as two complementary
but neurally dissociable mental processes. Journal of Cognition Neuroscience,
13(7), 910–919.
Sirigu, A., Duhamel, J. R., Cohen, L., Pillon, B., Dubois, B., et al. (1996). The mental
representation of hand movements after parietal cortex damage. Science, 273,
1564–1568.
Skoura, X., Personnier, P., Vinter, A., Pozzo, T., & Papaxanthis, C. (2008). Decline in
motor prediction in elderly subjects: Right versus left arm differences in
mentally simulated motor actions. Cortex, 44, 1271–1278.
Smith, C. D., Umberger, G. H., Manning, E. L., Slevin, J. T., Wekstein, D. R., & Schmitt,
F. A. (1999). Critical decline in fine motor hand movements in human aging.
Neurology, 53, 1458–1461.
Stevens, J. A. (2005). Interference effects demonstrate distinct roles for visual and
motor imagery during the mental representation of human action. Cognition, 95,
329–350.
Verschueren, S. M. P., Brumagne, S., Swinnen, S. P., & Cordo, P. J. (2002). The effect of
aging on dynamic position sense at the ankle. Behavioural Brain Research, 136,
593–603.
Ward, N. S., & Frackowiak, R. S. J. (2003). Age-related changes in the neural
correlates of motor performance. Brain, 126, 873–888.
Winegard, K. J., Hicks, A. L., Sale, D. G., & Vandervoort, A. A. (1996). A 12-year followup study of ankle muscle function in older adults. Journals of Gerontology Series
A: Biological and Medical Sciences, 51, 202–207.
Wolpert, D. M. (1997). Computational approaches to motor control. Trends in
Cognitive Sciences, 1, 209–216.
Young, S. J., Pratt, J., & Chau, T. (2009). Misperceiving the speed-accuracy tradeoff:
Imagined movements and perceptual decisions. Experimental Brain Research,
192(1), 121–132.