Consciousness and Cognition 18 (2009) 300–305
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Consciousness and Cognition
journal homepage: www.elsevier.com/locate/concog
Short Communication
A question of intention in motor imagery
Carl Gabbard *, Alberto Cordova, Sunghan Lee
Texas A & M University, Department of Health & Kinesiology, TAMU 4243, College Station, TX 77843-4243, USA
a r t i c l e
i n f o
Article history:
Received 25 March 2008
Available online 23 August 2008
Keywords:
Motor imagery
Intention
Estimation of reach
a b s t r a c t
We examined the question—is the intention of completing a simulated motor action the
same as the intention used in processing overt actions? Participants used motor imagery
to estimate distance reachability in two conditions: Imagery-Only (IO) and Imagery-Execution (IE). With IO (red target) only a verbal estimate using imagery was given. With IE
(green target) participants knew that they would actually reach after giving a verbal estimate and be judged on accuracy. After measuring actual maximum reach, used for the
comparison, imagery targets were randomly presented across peripersonal- (within reach)
and extrapersonal (beyond reach) space. Results indicated no difference in overall accuracy
by condition, however, there was a significant distinction by space; participants were more
accurate in peripersonal space. Although more research is needed, these findings support
an increasing body of evidence suggesting that the neurocognitive processes (in this case,
intention) driving motor imagery and overt actions are similar.
Ó 2008 Elsevier Inc. All rights reserved.
1. Introduction
Although the debate on specific issues continues, there is a rather convincing body of evidence indicating that processes
involved in simulating a motor action via motor imagery are similar to that used for planning and executing an action (see
reviews by Grèzes and Decety (2001) and Jeannerod (2006)). While discussions concerning association and dissociation of
processing avenues continue, few would deny that at some level there is a functional relationship between motor imagery
and action processing (e.g., Glover, Dixon, Castiello, & Rushworth, 2005; Heremans, Helsen, & Feys, 2007; Nikulin, Hohlefeld,
Jacobs, & Curio, 2007; Sabate, Gonzales, & Rodriquez, 2004; Sharma, Jones, Carpenter, & Baron, 2008).
One issue in the discussion that appears to be somewhat vague is the role of intention. That is, is the intention of completing a simulated motor action the same as the intention used in processing overt actions? In other words, what affect does
the goal have on the relationship? Arguably, it could be viewed that with simulated movements, there is no real intent to
actually move; therefore the association between simulated and real action is questionable on at least one level. Although
the paradigms are different, studies of third person simulation are worth a note. That is, simulated actions in the third- rather
than first person, via the observation of others. Anquetil and Jeannerod (2007) report that simulated actions in the first and
in the third person perspectives share common representations. However, Decety and Grèzes (1999) contend that the neural
substrate for action planning (in observers) is activated during perception of action ‘only when the intention’ is to imitate that
action at some time in the future. Studies of observers have reported a so-called ‘intentional effect’ (Badets, Blandin, Bouquet,
& Shea, 2006; Gallese & Goldman, 1998; Grèzes & Decety, 2001; Jeannerod, 1999).
On the other hand, several statements in the literature lead one to believe that intention is inherent in motor imagery. For
example, Jeannerod (1997) proposed that motor imagery of a specific action is based on the internal representation of intended, yet unexecuted actions. The researcher continues by suggesting that motor representations via motor imagery are
* Corresponding author. Fax: +1 979 847 8987.
E-mail address: [email protected] (C. Gabbard).
1053-8100/$ - see front matter Ó 2008 Elsevier Inc. All rights reserved.
doi:10.1016/j.concog.2008.07.003
C. Gabbard et al. / Consciousness and Cognition 18 (2009) 300–305
301
in fact intentions. This idea seems reasonable if one assumes that the actor has a goal (intention) in mind when, for example,
he or she is asked to give a verbal estimate of whether an object is within or out of reach. Furthermore, Jeannerod (2003)
suggests that simulated action includes everything that is involved in an overt action, except for muscular contractions
and joint rotations. Complementing this idea is the notion that planning of simulated and executed movements differ primarily in that at some (currently unknown) point, inhibitory processes suppress motor output (e.g., Decety, 1996; Lotze
et al., 1999; Schwoebel, Boronat, & Coslett, 2002). Furthermore, selected propositions of internal model theory suggest that
in the case of the specification of an intended goal for action (intentional state)—the motor centers generate an appropriate
outflow signal so as to perform the planned movement (inverse model) (e.g., Jordan, 1995; Wolpert & Kawato, 1998); this
outflow signal has been described as an efference copy also known as an action representation and simulation of action
(Choudhury, Charman, Bird, & Blakemore, 2007; Glover, 2004; Jeannerod, 1997; Wilson et al., 2004).
From that discussion, two possibilities of intentional processes seem reasonable: one is that simulated and executed
movements do not share the same intentional processes and the other suggests that it is the same or at least closely related.
However, the literature provides a hint of another explanation—that is, intention exists in both motor imagery and the planning and execution of movement, but at different levels (intentional states). This hypothesis was derived from the works of
Johnson and Haggard (2002) and Coello and Delevoye-Turrell (2007) in reference to the idea that level of motor awareness
(via motor imagery) and subsequent performance is dependent on the participants’ ‘‘intentional state.” Therefore, we could
speculate that different levels of intention may drive motor imagery compared to processing imagined then executed
actions.
With the aim of clarifying the issue—is the intention for motor imagery the same as the intention for processing imagined
and then executed actions?—we tested the effects of intention using an estimation of distance reachability paradigm via motor imagery in which participants in the Imagery-Only (IO) condition (red target) provided a verbal estimate only. In the
Imagery-Execution (IE) condition (green target), participants knew that they would actually reach after giving a verbal estimate and be judged on accuracy. To support the idea that motor representations via motor imagery are in fact intentions, we
hypothesized that there would be no difference between IO and IE conditions. That is, motor imagery is an intention to carry
out the representation. Alternatively, if a difference was found, more credence could be given to the idea that simulated motor actions and actual movement are driven by different neurocognitive processes.
2. Methods
2.1. Participants
Participants were 32 right-handed volunteers between the ages of 19 and 23 years. All participants were screened using a
questionnaire to ensure that none had a history of past or present sensorimotor impairment. Only participants identified as
strong right-handers; those for whom all items scored in that lateral direction using the Lateral Preference Inventory questionnaire (Coren, 1993) were included. All participants signed informed consent forms approved by our Institutional Review
Board before beginning the experiment and were naïve to the hypotheses under investigation.
2.2. Apparatus
Actual maximum reach, used as the comparison, and imaged reach responses were collected via an overhead projection
system linked to a PC programmed with Visual Basic. Visual images were systematically projected onto a table surface at
midline (90°). The table was constructed on a sliding bracket frame, allowing it be moved back and forward for adjustment
to the participant. Participants sat in an adjustable ergonomics chair fixed to the floor, aligned with the midline of the table
and projected image midline. For scaling purposes, seatpan height (surface was metal and non-depressive) was set to 105% of
participant’s popliteal height. Popliteal height is the distance from the underside of the foot to the underside of the thigh at
the knees. Table height was then adjusted to the midpoint between seatpan height and seated eye height. Table and seatpan
positioning were modified from Carello, Grosofsky, Reichel, Soloan, and Turvey (1989) and Choi and Mark (2004). To aid in
establishing actual reach limitations for a 1-df action (described in the next section), a commercial seatbelt system was modified and secured to the back of the chair. The room was darkened with the exception of light from the computer monitor and
colored (green and red) visual images projected onto the table programmed with a gray background surface. The fixation
point was projected onto a rectangular box (with a 45° angle surface) placed at midline approximately 45 cm from most distal target.
2.3. Procedure
Participants were systematically positioned in the chair and introduced to the task for determining ‘actual’ maximum
reach—full extension of the right limb and middle finger to slide forward a penny using a 1-df reach (Carello et al., 1989).
A 1-df reach involved a comfortable effort of the hand forearm, and upper arm acting as a single functional skeletal unit.
Based on the actual maximum reach point using a systematic measurement line, seven imagery targets (2 cm diameter-penny size) were created with ‘‘4” being the actual reach complemented with three targets above (distal to the participant) and
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three below (proximal) touching at the rims (Fig. 1). In essence, actual reach was ‘scaled’ to individual anthropometrics,
therefore allowing acceptable comparison. Furthermore, targets 1–4 were identified as peripersonal (within reach) space,
whereas targets 5–7 defined extrapersonal (beyond reach) space.
For the motor imagery trials, also using the right limb, participants were asked to kinesthetically ‘feel’ themselves executing the movement (‘‘feel your arm extending. . .”); therefore being more sensitive to the biomechanical constraints of
the task (Johnson, Corballis, & Gazzaniga, 2001; Sirigu & Duhamel, 2001; Stevens, 2005). In addition, the right (focus) hand
was placed within a drawn box on the table close to the torso at midline and the non-dominant limb rested on the participant’s upper left thigh under the table. Data collection began with a 5 s ‘‘Ready!” signal—immediately be followed by a central fixation point lasting 3 s. The target image appeared immediately thereafter (either green or red) and after 2 s there was
a tone. The participant was instructed to respond immediately with a ‘‘Yes” or ‘‘No” in reference to whether the stimulus was
reachable or not.
With the IO condition (red target) the participant was asked to only give a verbal response via motor imagery. The participant remained in the fixed position until the beginning of the next trial. In the IE condition (green target), in addition to
providing a verbal estimate as with IO, the participant also actually reached to determine self-evaluative accuracy of estimation; the target remained in view for 3 s. Although the computer was programmed to determine actual accuracy (cm), based
on actual maximum reach limits, the tactic of self-evaluation was used to stimulate intention via feedback of response. It
should be noted that for IE trials, participants were asked to reach to all targets, including those beyond reach.
Prior to data collection, participants were involved in a training session (about 15 min) featuring imagery technique, body
position, and responses to red and green targets. Training stressed that with the green (IE) target, ‘you will be evaluated on
accuracy between verbal and actual responses.’ Five trials were given at each of the seven sites for a total of 35 trials. Targets
were presented randomly with half of the trials red and half green. A second experimenter served to reinforce instructions
regarding imagery technique, body position, and refocusing to the central fixation point with each trial. Testing required a
single session of approximately 40 min.
2.4. Treatment of the data
Total score, overall accuracy across targets, was defined as the number of correct responses out of the total number of
trials for the two conditions (IO/IE). A correct verbal estimation of reach was when the participant responded ‘‘yes” when
actually the target was within reach, or ‘‘no” when in fact, the target was out of reach. These data were analyzed using a
2 (Condition [IO/IE]) 2 (Space [peripersonal/extrapersonal]) repeated measures analysis of variance procedure. As appropriate, post hoc analyses using Duncan’s Multiple Range tests were performed (p < .05). For simplicity of presentation and
Fig. 1. Illustration of general experimental setup.
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the fact that there was a difference in the number of trials in peripersonal and extrapersonal space, presented as a proportion
(% accurate) of total score.
To determine the distribution of error across targets (where did the errors occur?), the number and differences between
wrong and right answers for each target, in each condition were calculated using frequency data analyses and v2 procedures.
The reader should keep in mind that there were seven target presentations with ‘‘4” representing the participant’s actual
maximum reach. Incorrect responses at the three targets above (distal to) the actual (5–7) indicated an ‘‘overestimation”,
whereas an incorrect response at any of the lower (proximal) targets (1–4) was considered an ‘‘underestimation.” For example, if a participant noted that target 5 was reachable (‘yes’) when in fact it was not, it was an overestimation. As noted earlier, targets 1–4 were identified as peripersonal (within reach) space, whereas targets 5–7 defined extrapersonal (beyond
reach) space.
3. Results
3.1. Accuracy
Fig. 2 illustrates overall accuracy (total score) across targets. Analysis of variance results indicated no effect for Condition,
F(1, 31) = 0.06, p > .05; however, there was a distinction for Space, F(1, 31) = 6.15, p < .05, as well as an interaction,
F(3, 93) = 5.90, p < .01. For Condition, total score for IO (M = 7.16 [84%], SD = 2.53 [20%]) and IE (M = 7.58 [83%], SD = 2.92
[20%]) was similar. In regard to Space, post hoc analyses revealed participants were significantly more accurate in peripersonal (M = 8.95 [90%], SD = 2.35 [15%]) compared to extrapersonal space (M = 5.78 [77%], SD = 2.10 [22%]). The only interaction effect was found between IE peripersonal space (M = 9.41 [94%], SD = 2.41 [11%]) and IE extrapersonal space (M = 5.81
[74%], SD = 2.16 [22%].
3.2. Distribution and general direction of error
Our attention at that point focused on where the errors occurred. Fig. 3 shows distribution of error profiles for the two
conditions. In essence, these data supported the accuracy by Space results. That is, participants showed significantly less error in peripersonal compared to extrapersonal space. Although there was a significant difference favoring IE at targets 2, 3,
and 4 (ps < .05), as noted earlier, the ANOVA for accuracy revealed no ‘overall’ Condition effect. In the IE condition, the highest frequency of error occurred at target 5 at a value of 51%, suggesting an overestimation bias. In the IO condition, most of
error occurred around targets 4 and 5 at values of 38% and 41%, respectively. These values present the relative midpoint between under- and overestimation. In reference to the general direction of error overall, participants displayed the tendency
to overestimate.
4. Discussion
To support the prediction that motor representations via motor imagery are in fact intentions, we expected that there
would be no difference between IO and IE conditions. Alternatively, if a difference was found, more credence could be given
to the view that simulated motor actions and actual movement are driven by different neurocognitive processes.
Our data indicated no statistical distinction in total score by condition; that is, estimation of reach was similar with motor
imagery only and when the participant knew that accuracy would be assessed after imagery and actual execution. In general,
this result supports the idea that the neurocognitive processes (in this case, intention) driving motor imagery and overt
Fig. 2. Total score (accuracy) by condition (IE and IO) and space (peripersonal and extrapersonal).
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Fig. 3. Distribution of error across targets by condition.
actions are at least similar. Obviously, this result does not establish if the intention processes are the ‘same’—it has been our
observation that much of the literature in this area uses the terms ‘same’ and ‘similar’ loosely and at times, interchangeably.
It is our position that the intention in motor imagery and the processing of overt actions is related. An interesting observation
of this issue may be interpreted from the works of Coello and Delevoye-Turrell (2007) mentioned in the introduction. In reference to the processes associated with action simulation (motor imagery) and distance estimation, the researchers suggest
that the degree of ‘‘motor awareness” seems to depend on the participants’ ‘‘intentional state”; a notion that was derived in
part from the works of Johnson and Haggard (2002). This idea contends that planning involves an action representation
based on perceptual awareness (knowledge of the goal) and movement awareness (knowledge of physical response). Movement awareness defines the kinematics and kinetics involved; in essence, motor (kinesthetic) imagery. In essence, intention
is present with motor imagery, as evidenced by the results reported here. An interesting point and future research item is
determining the perception to action process. The next section on peripersonal versus extrapersonal space alludes to this
idea.
Analysis also indicated that participants displayed significantly less error in peripersonal space, compared to extrapersonal space. First of all, in general it is not unusual for individuals to be more accurate with estimating reach in their own reachable—peripersonal space; with the overall tendency to overestimate (Coello & Iwanow, 2006; Fischer, 2000; Gabbard,
Ammar, & Lee, 2006; Gabbard, Ammar, & Rodrigues, 2005a, 2005b; Robinovitch, 1998; Rochat & Wraga, 1997). Part of the
explanation for better accuracy in peripersonal space has been linked to a lifetime of experience in reachable space via egocentric frame of reference and subsequent body-scaling. That is, the visual mapping of the actor’s effector (hand) and reaching object—a process that underscores motor imagery. Furthermore, an increasing body of evidence shows that there are
neurological and behavioral distinctions in human responses to the perception of each of these spaces. More specific and
of interest here is the contention that there are specialized visual neurons coded for the detection of reachable [peripersonal]
space (Iriki, Tanaka, & Iwamura, 1996; Legranda, Brozzoli, Rossetti, & Farnè, 2007; Longo & Lourenco, 2006; Ládavas, 2002).
Ládavas’s work extends the notion by suggesting that humans have an integrated system that controls both visual and tactile
inputs within peripersonal space around the face and the hand, based on visual experience of body parts. Overall, this body of
research suggests that there exist visual neurons that code for what is within reach. Regarding our experiment and these
observations, we have considered the possibility that participants perceived targets projected out of peripersonal space were
not reachable, therefore ‘‘intentional state” was not of the level seen in the extrapersonal condition. In other words, the targets presented in extrapersonal space were no longer ‘‘affordable” (within reach) therefore a lesser intentional state was
manifested.
In conclusion, although more research is needed, these findings support an increasing body of evidence suggesting that
the neurocognitive processes (in this case, intention) driving motor imagery and overt actions are similar. In addition, we are
intrigued by the notion that different intentional states may be linked with motor imagery.
References
Anquetil, T., & Jeannerod, M. (2007). Simulated actions in the first and in the third person perspectives share common representations. Brain Research,
1161(3), 125.
Badets, A., Blandin, Y., Bouquet, C., & Shea, C. (2006). The intention superiority effect in motor skill learning. Journal of Experimental Psychology: Learning,
Memory, and Cognition, 32(3), 491–505.
Carello, C., Grosofsky, A., Reichel, F. D., Soloan, H. Y., & Turvey, M. T. (1989). Visually perceiving what is reachable. Ecological Psychology, 1, 27–54.
Choi, H. J., & Mark, L. S. (2004). Scaling affordances for human reach actions. Human Movement Science, 23, 785–806.
Choudhury, S., Charman, T., Bird, V., & Blakemore, S. (2007). Development of actionrepresentation during adolescence. Neuropsychologia, 45(2), 255–262.
Coello, Y., & Delevoye-Turrell, Y. (2007). Embodiment, spatial categorization and action. Consciousness and Cognition, 16(3), 667–683.
C. Gabbard et al. / Consciousness and Cognition 18 (2009) 300–305
305
Coello, Y., & Iwanow, O. (2006). Effect of structuring the workspace on cognitive and sensorimotor distance estimation: No dissociation between perception
and action. Perception & Psychophysics, 68(2), 278–289.
Coren, S. (1993). The lateral preference inventory for measurement of handedness, footedness, eyedness, and eardness: Norms for young adults. Bulletin of
the Psychonomic Society, 31, 1–3.
Decety, J. (1996). The neurophysiological basis of motor imagery. Behavioural Brain Research, 77, 45–52.
Decety, J., & Grèzes, J. (1999). Neural mechanisms subserving the perception of human actions. Trends in Cognitive Sciences, 3, 172–178.
Fischer, M. H. (2000). Estimating reachability: Whole-body engagement or postural stability? Human Movement Science, 19, 297–318.
Gabbard, C., Ammar, D., & Lee, S. (2006). Perceived reachability in single- and multiple degree of freedom workspace. Journal of Motor Behavior, 38(6),
423–430.
Gabbard, C., Ammar, D., & Rodrigues, L. (2005a). Perceived reachability in hemispace. Brain and Cognition, 58(2), 172–177.
Gabbard, C., Ammar, D., & Rodrigues, L. (2005b). Visual cues and perceived reachability. Brain and Cognition, 59(3), 287–291.
Gallese, V., & Goldman, A. (1998). Mirrors neurons and the simulation theory of mind-reading. Trends in Cognitive Sciences, 2, 493–501.
Glover, S. (2004). Separate visual representations in the planning and control of action. Behavioural Brain Science, 27, 3–24.
Glover, S., Dixon, P., Castiello, U., & Rushworth, M. F. (2005). Effects of an orientation illusion on motor performance and motor imagery. Experimental Brain
Research, 166, 496–506.
Grèzes, J., & Decety, J. (2001). Functional anatomy of execution, mental simulation, observation, and verb generation of actions: A meta-analysis. Human
Brain Mapping, 12, 1–19.
Heremans, E., Helsen, W. F., & Feys, P. (2007). The eyes as a mirror of our thoughts: Quantification of motor imagery of goal-directed movements through
eye movement registration. Behavioural Brain Research, 187(2), 351–360.
Iriki, A., Tanaka, M., & Iwamura, Y. (1996). Coding of modified body schema during tool use by macaque postcentral neurons. NeuroReport, 7, 2325–2330.
Jeannerod, M. (1997). The cognitive neuroscience of action. Oxford: Blackwell.
Jeannerod, M. (1999). To act or not to act: Perspectives on the representation of actions. The Quarterly Journal of Experimental Psychology, 52, 1–29.
Jeannerod, M. (2003). The mechanism of self-recognition in humans. Behavioural Brain Research, 142, 1–15.
Jeannerod, M. (2006). Motor cognition. Oxford: Oxford University Press.
Johnson, S., Corballis, P., & Gazzaniga, M. (2001). Within grasp but out reach: Evidence for a double dissociation between imagined hand and arm
movements in the left cerebral hemisphere. Neuropsychologia, 39, 36–50.
Johnson, H., & Haggard, P. (2002). Motor awareness without perceptual awareness. Neuropsychologia, 43, 227–237.
Jordan, M. I. (1995). The organization of action sequences: Evidence from a relearning task. Journal of Motor Behaviour, 27, 179–192.
Ládavas, E. (2002). Functional and dynamic properties of visual peripersonal space. Trends in Cognitive Sciences, 6(1), 17–22.
Legranda, D., Brozzoli, C., Rossetti, Y., & Farnè, A. (2007). Close to me: Multisensory space representations for action and pre-reflexive consciousness of
oneself-in-the-world. Consciousness and Cognition, 16(3), 687–699.
Longo, M. R., & Lourenco, F. S. (2006). On the nature of near space: Effects of tool use and the transition to far space. Neuropsychologia, 44, 977–981.
Lotze, M., Montoya, P., Erb, M., Hulsmann, E., Flor, H., Klose, U., et al (1999). Activitation of cortical and cerebellar motor areas during executed and imagined
hand movements: An fMRI study. Journal of Cognitive Neuroscience, 11, 491–501.
Nikulin, V. V., Hohlefeld, F. U., Jacobs, A. M., & Curio, G. (2007). Quasi-movements: A novel motor-cognitive phenomenon. Neuropsychologia, 46(2), 727–742.
Robinovitch, S. N. (1998). Perception of postural limits during reaching. Journal of Motor Behavior, 30, 352–358.
Rochat, P., & Wraga, M. (1997). An account of the systematic error in judging what is reachable. Journal of Experimental Psychology: Human Perception and
Performance, 23, 199–212.
Sabate, M., Gonzales, B., & Rodriquez, M. (2004). Brain lateralization of motor imagery: Motor planning asymmetry as a cause of movement lateralization.
Neuropsychologia, 42, 1041–1049.
Schwoebel, J., Boronat, C. B., & Coslett, H. B. (2002). The man who executed ‘‘imagined” movements: Evidence for dissociable components of the body
schema. Brain and Cognition, 50, 1–16.
Sharma, N., Jones, P. S., Carpenter, T. A., & Baron, J. (2008). Mapping the involvement of BA 4a and 4p during Motor Imagery. NeuroImage, 41(1), 92–99.
Sirigu, A., & Duhamel, J. R. (2001). Motor and visual imagery as two complementary but neurally dissociable mental processes. Journal of Cognitive
Neuroscience, 13(7), 910–919.
Stevens, J. A. (2005). Interference effects demonstrate distinct roles for visual and motor imagery during the mental representation of human action.
Cognition, 95(3), 329–350.
Wilson, P. H., Maruff, P., Butson, P., Williams, J., Lum, J., & Thomas, P. R. (2004). Internal representation of movement in children with developmental
coordination disorder: A mental rotation task. Developmental Medicine & Child Neurology, 46, 754–759.
Wolpert, D. W., & Kawato, M. (1998). Multiple paired forward and inverse models for motor control. Neural Network, 11, 1317–1329.