Edited by: Aron K. Barbey, University of Illinois at Urbana-Champaign, USA
Reviewed by: Alissa Fourkas, National Institutes of Health, USA; Lucía Amoruso, Laboratory of Experimental Psychology & Neuroscience (LPEN) - INECO Foundation (FINECO), Argentina
*Correspondence: Stephanie Cacioppo, High-Performance Electrical NeuroImaging Laboratory, Center for Cognitive and Social Neuroscience, The University of Chicago, 940 East 57th Street, Chicago, IL 60637, USA e-mail:
This article was submitted to the journal Frontiers in Human Neuroscience.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Studying the way athletes predict actions of their peers during fast-ball sports, such as a tennis, has proved to be a valuable tool for increasing our knowledge of intention understanding. The working model in this area is that the anticipatory representations of others' behaviors require internal predictive models of actions formed from pre-established and shared representations between the observer and the actor. This model also predicts that observers would not be able to read accurately the intentions of a competitor if the competitor were to perform the action without prior knowledge of their intention until moments before the action. To test this hypothesis, we recorded brain activity from 25 male tennis players while they performed a novel behavioral tennis intention inference task, which included two conditions: (i) one condition in which they viewed video clips of a tennis athlete who knew in advance where he was about to act/serve (initially intended serves) and (ii) one condition in which they viewed video clips of that same athlete when he did not know where he was to act/serve until the target was specified after he had tossed the ball into the air to complete his serve (non-initially intended serves). Our results demonstrated that (i) tennis expertise is related to the accuracy in predicting where another server intends to serve when that server knows where he intends to serve
“If there is something you don't want to be on a tennis court, it is predictable,”
John McEnroe, ESPN, U.S Open, 8-30-13.
The way in which athletes read and anticipate the actions of their opponent during fast-ball sports, such as a tennis, is a challenging and complex process that is a remarkable feat in itself. A tennis player's ability to predict an opponent's intentions quickly and accurately is particularly important during the return of serves, where the time required to plan and initiate a response typically exceeds the flight time for the ball (Glencross and Cibich,
Several studies have investigated the mechanisms underlying predictive motor skills in such time-constrained situations (Williams et al.,
Theories of simulation and embodied cognition provide a neural basis for such early predictive ability in experts by specifying the involvement (and re-activation) of the inferior fronto-parietal network (possibly including the hMNS), which is known to be activated by one's own motor performance as well as perspective taking, sensorimotor integration, and procedural memory (Rizzolatti et al.,
Although embodied cognition is not a prerequisite to act or to understand others' actions, simulation theories suggest that the more these observed actions are congruent with integrated templates of past self-related motor experiences, the easier it is to read these observed actions and intentions as the actor and the observer share a mental map of the action (Niedenthal et al.,
In the context of sport athletes, intention understanding among peers is based, in part, upon a shared mental representation of actions, with sport mates being able to better anticipate one another's actions due to greater experience observing each other's actions in different situations or due to shared experience in a specific sport (Wegner et al.,
Reciprocally, this model of shared representation predicts that this facilitation effect fades away when an actor plans their actions with an unusual mental representation of their intentions (as it can be the case in fool actions). In a recent behavioral study, Tomeo and colleagues tested this notion by manipulating the congruence between a soccer kicker's bodily movements and the subsequent ball trajectory and investigated the prediction performance from 16 kickers, 16 goalkeepers, and 16 novices (Experiment 1; Tomeo et al.,
To address this argument, we designed a novel behavioral intention inference task (IIT), which included two conditions: (i) one condition showing video clips of an unfamiliar right-handed tennis athlete (hereafter the server) who knew in advance where he was about to act/serve (
We recorded brain activity from 25 male tennis experts (hereafter, the observers) while they were performing this tennis IIT (
Interestingly, in the case of a tennis match, the same action (e.g., a tennis serve) may reflect different intentions (e.g., to serve to the T or the wide side of the service box). Given the importance of not being predictable on the tennis court, expert tennis players are taught to perform the same perceptible actions regardless of their service intentions. Therefore, expert tennis players not only have to rely on their past experiences of serving in tennis to predict the intended location of their opponent's serve but they must eschew any masking behavioral cues that would hide the intention of their opponent. The study of expert tennis players while they try to predict accurately the ultimate intentions (direction of a serve) of another player, thus, constitutes a unique and ecologically valid opportunity to better understand the mechanisms underlying the assumed visuo-motor matching in embodied cognition.
All 25 fMRI participants were right-handed (Edinburgh Handedness Inventory, Oldfield,
Prior to participation, volunteers provided written informed consent that had been approved by the Ethical Committee of the University of Chicago, Illinois. The study took place over a single visit. Upon arriving at the University of Chicago Brain Imaging Center, participants completed a series of standard screening forms. Then, they completed a series of tennis-related questionnaires, and a brief practice of the tennis intention inference task (practice
Participants' tennis expertise was ascertained by their United States Tennis Association (USTA) playing level, which was, on average, 4.24 (
In the practice
As in the practice
Each trial began with a 500 ms-fixation cross that was followed immediately by a 5 s-target video clip (Figure
Stimuli consisted of eight video clips [2 types of serves (IIS and NIIS) × 2 starting positions (left and right) × 2 ball landing sides (to the center “T” and to wide side “W” of the service box)] of an unfamiliar right-handed male expert tennis player (from Syracuse University, Upstate New York) performing a tennis serve (one per video). The video clips showed the tennis player on two different starting positions [standing either on the right side from the participant's perspective of the tennis court (half of the video clips), or on the left side of the tennis court] in order to control for any participants' lateralized attentional bias during the experimental
Across serve conditions, the server was able to perform the same movements repeatedly, independently of the ultimate outcome of his serves (either to the center or the wide side of the service box) because of his high tennis level (USTA level: 7, which denotes a world class player). He was also able to bounce the ball, toss it and serve in the same way, using his regular action with a relatively consistent velocity, independently of the intentionality manipulation (see Supplementary Movies
Videos of the server were taken with a digital Sony Cybershot camera. The camera was located on a tripod on the baseline next to the service box of the court diagonal from where the tennis player stood (Figure
In order to test the viability of our novel paradigm and test the similarity of the server's movements across serve conditions, we performed three different steps. First, we performed a quantitative analysis of all the tennis video clips using Dartfish i.e., a performance video analysis software. Extensive research on anticipatory skills in sport, in which the visual information available to understand a tennis serve is cut off at some specific time frames (temporal occlusion) during the serve, indicates that a key event for tennis is the ball/racket contact, with the movement of the arm and the racket prior that key event being the source of critical cues for racket sports (Tenenbaum et al.,
Second, we performed a visual qualitative analysis of the video clips by asking three persons (SC, JTC, BM) who are knowledgeable (although non expert) in tennis to view all the video clips, one by one, and tried to determine whether any obvious visual differences appeared between the two serve conditions i.e., IIS and NIIS. Although these three persons were aware of the two different experimental conditions, none of them was able to identify any visual differences between video clips. This result was reinforced with the behavioral performance from 29 other individuals [18 men, 11 women; mean age of 31.55 (
All 29 participants were non-expert tennis players (as ascertained by their self-report USTA tennis levels:
Then, to make sure that this novel task was suitable for tennis experts, we asked a pro-tennis player (FF), who is also an active pro-tennis coach on the ATP tour, to watch the video clips and perform a qualitative analysis. Although he was not aware of the two conditions, he was able to detect nuances at the level of the hips of the server that differed between the two conditions. Interestingly, he was not able to name or identify the two conditions after identifying two different types of stimuli. All he could report was that some tennis serves (the IIS, according to SC's observation of FF's performance) were easier to anticipate than others in the set of video clips. This procedure suggests that a pro-tennis player could
The present
Response accuracy (in percent, %), reaction times from the onset of the video (in milliseconds, ms), and brain activity were recorded while participants made a decision as to ultimate direction of the serves. In addition, to account for a potential intention advantage, we calculated a conventional accuracy index score (Marshall et al.,
In line with our hypotheses, we collapsed across match hypothetical contexts (friendly or competitive), starting position (left or right) and ball landing sides (“T” or “W”), yielding a repeated-measures design with serve type (IIS vs. NIIS) as a within-subjects factor. Mean reaction times and percentage were calculated for each subject and condition. Outliers were removed by eliminating responses greater than 3.5 standard deviations from the grand mean. Using this cutoff resulted in the removal of 4.5% of all trials (across participants). Repeated measures ANOVAs were utilized to analyze potential differences in reaction times and accuracy between serve types in the
USTA level | 0.24 | 0.30 | 0.22 | −0.09 | 0.12 | −0.23 |
Hours playing tennis/week | −0.15 | −0.13 | −0.18 | 0.44 |
0.43 |
0.24 |
Hours watching tennis/week | 0.17 | 0.19 | 0.15 | 0.17 | −0.05 | 0.05 |
Age first learned tennis | −0.03 | −0.06 | −0.004 | −0.31 | −0.26 | −0.23 |
Imaging was performed on a 3-T Philips Achieva Quasar Dual 16 Ch scanner with quadrature head coil used for spin excitation and signal reception. High-resolution volumetric T1-weighted spoiled gradient-recalled (SPGR) images were obtained for each participant in one hundred sixty-one 1.0-mm sagittal slices with 8° flip angle and 24 cm field of view (FOV) for use as anatomical images. Functional images using a block design and were acquired using a echo-planar acquisition with Z-Shimming with 32 × 4-mm coronal slices with an inter-slice gap of 0.5 mm spanning the whole brain (
Image pre-processing and analyses were performed using Analysis of Functional NeuroImages software (AFNI, Medical College of Wisconsin). For each participant, motion detection and correction were undertaken using a six-parameter, rigid-body transformation. Functional images were co-registered and spatially smoothed using a 5-mm full width at half maximum Gaussian filter. Individual-subject analyses were conducted using the general linear model to generate estimates of blood oxygenation level-dependent (BOLD) signal on a voxelwise basis (Ward,
On average, participants reported: (a) having a USTA level of 4.24 (
As predicted, the behavioral results showed that the observers were better at predicting initially intended serves, IIS (64.16% correct,
Significant positive correlations were observed between the number of hours the participants reported playing tennis per week and three behavioral measures: (i) the overall accuracy; (ii) the accuracy for IIS; and the
In line with our behavioral results, our neuroimaging results showed regional changes in hemodynamic activity for correct behavioral predictions of initially intended (IIS) serves (compared to non-initially intended, NIIS, serves) in four main cortical areas: right occipital cortex, right superior parietal lobule (SPL), left extrastriate body area (EBA), and left inferior parietal lobule (IPL, extending to the left temporo-parietal junction, TPJ; Figure
27.8% overlap with Right Calcarine Gyrus (BA18) | 15,579 | 18.2 | −76.6 | 4.1 | 3.9245 |
24.5% overlap with Right Lingual Gyrus | |||||
12.9% overlap with Right Cuneus | |||||
11.1% overlap with Right Superior Occipital Gyrus | |||||
10.9% overlap with Right Middle Occipital Gyrus | |||||
5.3% overlap with Right Fusiform Gyrus | |||||
32.2% overlap with Right Superior Parietal Lobule (BA7) | 5832 | 2.8 | −66.8 | 54.3 | 3.1006 |
23.9% overlap with Precuneus | |||||
14.9% overlap with Superior Parietal Lobule | |||||
12.1% overlap with Precuneus | |||||
31.7% overlap with Right Thalamus | 3105 | 27.7 | −21.5 | 3 | 3.2895 |
11.7% overlap with Right Superior Temporal Gyrus | |||||
7.7% overlap with Right Insula Lobe | |||||
6.2% overlap with Right Putamen | |||||
56.5% overlap with Left Thalamus | 2646 | −16.5 | −23.5 | 7.9 | 3.325 |
10.9% overlap with Left Hippocampus | |||||
78.1% overlap with Left Inferior Temporal Gyrus (BA37) | 2538 | −45.1 | −63.9 | 2.4 | 3.2919 |
18.2% overlap with Left Middle Occipital Gyrus | |||||
66.5% overlap with Left Inferior Parietal Lobule (BA40) | 1512 | −50.1 | −42.7 | 35 | 3.2227 |
31.4% overlap with Left SupraMarginal Gyrus | |||||
58.8% overlap with Right Putamen | 1107 | 23.2 | 12.9 | 9.5 | 3.1579 |
23.5% overlap with Right Caudate Nucleus | |||||
81.4% overlap with Left Middle Occipital Gyrus (BA18) | 999 | −31.7 | −82.3 | −4.5 | 3.1296 |
18.6% overlap with Left Inferior Occipital Gyrus | |||||
94.5% overlap with Left Middle Occipital Gyrus (BA19) | 891 | −34.5 | −83.1 | 20.9 | 3.1188 |
56.4% overlap with Right Inferior Frontal Gyrus (BA47) | 756 | 25.8 | 32.3 | −6.3 | 3.1743 |
To further determine whether the above brain areas were specific to correct trials of the
40.9% overlap with Right Lingual Gyrus (BA18) | 10,692 | 12 | −72 | 0 | 4.26 |
40.9% overlap with Right Calcarine Gyrus | |||||
9.1% overlap with Right Cuneus | |||||
28.1% overlap with Left Rolandic Operculum (BA41) | 1647 | −38 | −31 | 17 | 3.22 |
12.5% overlap with Left Superior Temporal Gyrus | |||||
25.2% overlap with Left Precentral gyrus (BA44) | 1593 | −44 | 13 | 7 | 3.12 |
20.5% overlap with Left Inferior Frontal Gyrus (p. Opercularis) | |||||
14.4% overlap with Left Temporal Pole | |||||
14.0% overlap with Left Inferior Frontal Gyrus (p. Triangularis) | |||||
49.3% overlap with Right Rolandic Operculum (BA42) | 1080 | 59 | −14 | 12 | 3.12 |
42.3% overlap with Right Superior Temporal Gyrus | |||||
60.1% overlap with Right Inferior Parietal Lobule (BA40) | 702 | 62 | −31 | 25 | 3.09 |
24.6% overlap with Right Superior Temporal Gyrus | |||||
58.2% overlap with Right Superior Parietal Lobule (BA 7) | 702 | 15 | −73 | 56 | 3.22 |
37.4% overlap with Right Precuneus |
Similar to the correct trials, the brain activity associated with incorrect behavioral predictions of non-initially intended serves (compared to initially intended serves) revealed only one specific hemodynamic increase in the left cuneus extending to the left calcarine gyrus—a brain region associated with visual information processing. Different from the neuroimaging results for the correct trials, incorrect trials were also characterized by brain activity in right TPJ (right superior temporal gyrus) and left precentral gyrus/inferior frontal, and right IPL, but not in the right inferior frontal gyrus nor left IPL (Table
For
15.1% overlap with Cerebellum | 3105 | 0 | −30 | −21 | 0.57 |
8.8% overlap with Cerebellar Vermis | |||||
8.4% overlap with Cerebellar Vermis | |||||
57.7% overlap with Left Middle Temporal Gyrus (BA21) | 2268 | −47 | −30 | −2 | 0.57 |
9.5% overlap with Left Superior Temporal Gyrus | |||||
30.5% overlap with Left Caudate Nucleus | 1944 | −8 | 17 | 16 | 0.56 |
19.9% overlap with Right Caudate Nucleus | 918 | 19 | −19 | 27 | 0.56 |
NO CLUSTERS FOUND |
For
NO CLUSTERS FOUND | |||||
52.2% overlap with Right Superior Frontal Gyrus (BA8) | 1809 | 4 | 34 | 44 | 0.56 |
51.0% overlap with Right Parahippocampal Gyrus (BA 30) | 1026 | 30 | −58 | 6 | −0.57 |
10.4% overlap with Left Calcarine Gyrus | 47,466 | −4 | −46 | −1 | 0.58 |
8.0% overlap with Left Lingual Gyrus | |||||
6.4% overlap with Right Lingual Gyrus | |||||
5.2% overlap with Left Calcarine Gyrus | |||||
3.9% overlap with Left Fusiform Gyrus | |||||
3.9% overlap with Left Middle Occipital Gyrus | |||||
3.8% overlap with Left Hippocampus | |||||
21.5% overlap with Left Postcentral Gyrus | 9558 | −25 | −17 | 28 | 0.56 |
9.4% overlap with Left SupraMarginal Gyrus, | |||||
5.6% overlap with Left Thalamus, | |||||
5.4% overlap with Left Caudate Nucleus | |||||
4.6% overlap with Left Middle Frontal Gyrus | |||||
3.3% overlap with Left Inferior Parietal Lobule | |||||
34.9% overlap with Right Superior Temporal Gyrus | 6264 | 37 | −22 | −3 | 0.56 |
17.9% overlap with Right Hippocampus | |||||
10.6% overlap with Right ParaHippocampal Gyrus | |||||
6.8% overlap with Right Middle Temporal Gyrus | |||||
5.4% overlap with Right Fusiform Gyrus | |||||
3.3% overlap with Right Insula Lobe | |||||
17.1% overlap with Right Parietal Lobe/Precuneus (BA 7) | 3240 | 27 | −65 | 29 | 0.56 |
16.3% overlap with Right Angular Gyrus | |||||
14.7% overlap with Right Superior Occipital Gyrus | |||||
14.6% overlap with Right Precuneus | |||||
13.7% overlap with Right Cuneus | |||||
3.8% overlap with Right Middle Temporal Gyrus | |||||
3.4% overlap with Right Superior Parietal Lobule | |||||
44.2% overlap with Right Postcentral Gyrus (BA3) | 2619 | 43 | −20 | 41 | 0.55 |
34.6% overlap with Right Precentral Gyrus | |||||
3.5% overlap with Right Inferior Parietal Lobule | |||||
33.8% overlap with Right Middle Cingulate Gyrus (BA 24) | 1917 | 6 | 0 | 46 | 0.54 |
31.9% overlap with Right SMA | |||||
13.7% overlap with Right SMA | |||||
9.0% overlap with Middle Cingulate Cortex | |||||
4.4% overlap with Right Superior Frontal Gyrus | |||||
83.9% overlap with Left Parietal Lobule/Precuneus (BA 7) | 1566 | −22 | −61 | 52 | 0.54 |
11.0% overlap with Left Inferior Parietal Lobule | |||||
4.1% overlap with Left Precuneus | |||||
24.2% overlap with Left Anterior Cingulate Cortex (BA32) | 999 | −23 | 33 | 12 | 0.57 |
3.6% overlap with Left Inferior Frontal Gyrus | |||||
80.8% overlap with Right Precentral Gyrus (BA4) | 918 | 58 | −9 | 22 | 0.55 |
15.0% overlap with Right Rolandic Operculum | |||||
65.2% overlap with Left Insula Lobe (BA 13) | 729 | −35 | 21 | 12 | −0.57 |
33.5% overlap with Left Inferior Frontal Gyrus | |||||
NO CLUSTERS FOUND | |||||
21.6% overlap with Right Pallidum | 2916 | 15 | −1 | −3 | –0.59 |
14.5% overlap with Right Caudate Nucleus | |||||
5.8% overlap with Right Thalamus | |||||
4.6% overlap with Right Hippocampus | |||||
4.0% overlap with Right Amygdala | |||||
51.8% overlap with Right Medial Frontal Gyrus (BA 9) | 2268 | 4 | 41 | 19 | 0.60 |
38.7% overlap with Anterior Cingulate Cortex | |||||
32.7% overlap with Right Insula (BA 13) | 1458 | 38 | 8 | −11 | −0.60 |
12.0% overlap with Right Temporal Pole | |||||
6.5% overlap with Right Middle Temporal Gyrus | |||||
5.3% overlap with Right Superior Temporal Gyrus | |||||
52.4% overlap with Left Insula (BA 13) | 1404 | −41 | 3 | −11 | −0.62 |
29.0% overlap with Left Superior Temporal Gyrus | |||||
4.1% overlap with Left Middle Temporal Gyrus | |||||
94.9% overlap with Left Middle Temporal Gyrus (BA 39) | 1350 | −53 | −56 | 9 | 0.61 |
5.1% overlap with Left Superior Temporal Gyrus | |||||
50.9% overlap with Right Anterior Prefrontal Cortex (BA10) | 1134 | 28 | 49 | 21 | 0.59 |
39.7% overlap with Right Middle Frontal Gyrus | |||||
51.2% overlap with Right Cerebellum | 891 | 16 | −52 | −43 | 0.65 |
99.7% overlap with Right Cerebellum | 891 | 32 | −61 | −30 | 0.58 |
68.0% overlap with Right Superior Temporal Gyrus (BA 22) | 891 | 50 | −18 | 8 | 0.60 |
28.5% overlap with Right Heschls Gyrus | |||||
3.0% overlap with Right Rolandic Operculum | |||||
100% overlap with Right Inferior Frontal Gyrus (BA 9) | 891 | 52 | 15 | 21 | 0.56 |
96.7% overlap with Right Inferior Frontal Gyrus (BA 13) | 783 | 43 | 29 | 4 | 0.56 |
67.0% overlap with Left Angular Gyrus (BA 39) | 729 | −38 | −57 | 33 | 0.56 |
32.3% overlap with Left Inferior Parietal Lobule |
NO CLUSTERS FOUND | |||||
41.0% overlap with Right Superior Temporal Gyrus (BA41) | 1161 | 42 | −38 | 9 | −0.59 |
49.6% overlap with Right Angular Gyrus (BA 39) | 5184 | 41 | −60 | 22 | −0.57 |
19.7% overlap with Right Middle Temporal Gyrus | |||||
7.1% overlap with Right Middle Occipital Gyrus | |||||
38.0% overlap with Left Cerebellum | 3672 | −25 | −60 | −33 | −0.55 |
22.8% overlap with Left Cerebellum | |||||
12.7% overlap with Left Fusiform Gyrus | |||||
64.1% overlap with Left Precuneus (BA 7) | 2889 | −6 | −69 | 41 | −0.56 |
20.9% overlap with Left Superior Parietal Lobule | |||||
13.0% overlap with Right Precuneus | |||||
91.7% overlap with Right Middle Frontal Gyrus (BA 9) | 2349 | 32 | 35 | 37 | −0.59 |
7.2% overlap with Right Superior Frontal Gyrus | |||||
30.5% overlap with Left Posterior Cingulate Cortex (BA 29) | 2052 | −3 | −49 | 6 | −0.63 |
16.7% overlap with Cerebellar Vermis | |||||
10.7% overlap with Left Calcarine Gyrus | |||||
82.0% overlap with Right Superior Parietal Lobule (BA 7) | 1890 | 33 | −55 | 57 | −0.58 |
9.3% overlap with Right Postcentral Gyrus | |||||
28.0% overlap with Left Middle Cingulate Gyrus (BA 23) | 1647 | 0 | −27 | 30 | −0.55 |
7.7% overlap with Right Middle Cingulate Gyrus | |||||
42.6% overlap with Right Caudate Nucleus | 999 | 16 | −10 | 24 | −0.59 |
13.0% overlap with Right Thalamus | |||||
61.4% overlap with Right Cerebellum | 945 | 22 | −54 | −21 | −0.55 |
23.9% overlap with Right Fusiform Gyrus | |||||
14.5% overlap with Right Cerebellum | |||||
32.4% overlap with Left Parahippocampal Area (BA 34) | 945 | −24 | 1 | −9 | −0.6 |
11.3% overlap with Left Putamen | |||||
3.8% overlap with Right Postcentral Gyrus (BA 2) | 864 | 31 | −23 | 37 | −0.55 |
1.5% overlap with Right SupraMarginal Gyrus | |||||
88.4% overlap with Left Superior Temporal Gyrus (BA 13) | 810 | −54 | −40 | 19 | −0.6 |
11.6% overlap with Left SupraMarginal Gyrus | |||||
21.4% overlap with Right Insula | 1809 | 22 | 6 | −10 | −0.63 |
13.7% overlap with Right Amygdala | |||||
5.4% overlap with Right Putamen | |||||
54.1% overlap with Right Angular Gyrus (BA 39) | 1809 | 42 | −70 | 29 | 0.56 |
31.0% overlap with Right Middle Occipital Gyrus | |||||
7.2% overlap with Right Middle Temporal Gyrus | |||||
67.8% overlap with Right Cerebellum | 1107 | 17 | −52 | −46 | 0.63 |
47.5% overlap with Right Middle Temporal Gyrus (BA 21) | 1026 | 45 | −2 | −15 | 0.62 |
17.0% overlap with Right Superior Temporal Gyrus | |||||
51.4% overlap with Right Hippocampus | 1026 | 24 | −16 | −15 | 0.57 |
37.5% overlap with Right ParaHippocampal Gyrus | |||||
92.9% overlap with Right Superior Temporal Gyrus (BA 42) | 864 | 57 | −29 | 13 | −0.63 |
5.8% overlap with Right SupraMarginal Gyrus | |||||
47.2% overlap with Left Superior Temporal Gyrus (BA 38) | 756 | −39 | 6 | −12 | −0.58 |
57.7% overlap with Right SupraMarginal Gyrus | 729 | 54 | −42 | 20 | 0.68 |
39.9% overlap with Right Superior Temporal Gyrus |
For the difference scores of
Anticipating intentions of an opponent during a fast interaction is a challenging problem. Our results reinforce and expand prior research by demonstrating that tennis experts are better at predicting where an expert server intends to serve (T or wide) when that expert server knows where he intends to serve
These findings support predictions by the simulation and embodied cognition theories by demonstrating that the observers are more efficient in predicting one's intentions when that someone is pre-cognizant of their intentions before initiating their action (IIS condition) than when they don't know in advance their action intention (NIIS condition). In other words, when the observers share a common mental representation of action with the server, observers can more accurately read the intentions of the server. As demonstrated in previous research, this facilitation effect in reading another's intentions is positively correlated with active practice (as measured with the number of hours playing tennis per week) rather than passive practice (as measured with the number of hours watching tennis per week). Further studies could be done to specifically test the effects of the different components of a tennis profile and habits of a player on their anticipatory behaviors and performance. For instance, based on simulation and embodied cognition theories, one may be interested in comparing the effect of active tennis practice (e.g., playing tennis every week) vs. passive tennis practice (e.g., observing tennis on TV every week) on the accuracy and speed of serve predictions.
Our neuroimaging results extend these behavioral findings by demonstrating that accurate predictions were characterized by activation within both the Action Observation Network (AON including the hMNS) and the social brain network (SN). More precisely, our fMRI analyses of the IIS—NISS contrast for
Furthermore, the specific pattern of activation for
In addition, IIS (compared to NIIS) was characterized by increased activity in dopaminergic-rich regions (bilateral thalamus, right putamen, right insula, and right caudate nucleus) involved in somatosensory integration, motivation, goal-directed actions as well as formation habits and procedural memory (Ashby and Crossley,
Finally, incorrect trials were associated with a different configuration of brain activation, which may provide clues as to when intention prediction goes wrong. Although overlapping areas of activations were observed within the brain areas involved in basic visual processing and spatial attention, no activation was observed during incorrect trials in brain areas involved in action prediction, embodied cognition, and procedural memory. Our study instead reveals that inaccurate predictions are related to activation in cortical areas known to be involved in low-level (bottom-up) computational processes associated with the sense of agency and self-other distinction as well as high-level processes such as theory of mind (Decety and Lamm,
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
This study was supported by a professorship grant from the Swiss National Science Foundation (FNS# PP00_1_128599/1 to SC). The authors thank Aaron B. Ball, Stephen Balogh and Ryan Paradise for their technical help, as well as Professor Brian Martens for his early comments on the stimuli.
The Supplementary Material for this article can be found online at: