Brain and Cognition 77 (2011) 372–381
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Biological motion task performance predicts superior temporal sulcus activity John D. Herrington a,⇑, Charlotte Nymberg b, Robert T. Schultz a,c a
Center for Autism Research, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, United States Institute of Psychiatry, Kings College London, United Kingdom c Department of Pediatrics, University of Pennsylvania, Philadelphia, PA 19104, United States b
a r t i c l e
i n f o
Article history: Accepted 12 September 2011 Available online 22 October 2011 Keywords: Biological motion fMRI Superior temporal sulcus Amygdala
a b s t r a c t Numerous studies implicate superior temporal sulcus (STS) in the perception of human movement. More recent theories hold that STS is also involved in the understanding of human movement. However, almost no studies to date have associated STS function with observable variability in action understanding. The present study directly associated STS activity with performance on a challenging task requiring the interpretation of human movement. During functional MRI scanning, fourteen adults were asked to identify the direction (left or right) in which either a point-light walking ﬁgure or spinning wheel were moving. The task was made challenging by perturbing the dot trajectories to a level (determined via pretesting) where each participant achieved 72% accuracy. The walking ﬁgure condition was associated with increased activity in a constellation of social information processing and biological motion areas, including STS, MT+/V5, right pars opercularis (inferior frontal gyrus), fusiform gyrus, and amygdala. Correctly answered walking ﬁgure trials were uniquely associated with increased activity in two right hemisphere STS clusters and right amygdala. Present ﬁndings provide some of the strongest evidence to date that STS plays a critical role in the successful interpretation of human movement. Ó 2011 Elsevier Inc. All rights reserved.
1. Introduction Humans process and interpret the movements of others with remarkable speed and reliability. The robustness of this processing suggests that they are maintained by a dedicated neurobiological system. Recent research indicates that portions of the temporal lobes – particularly superior temporal sulcus (STS) – are key constituents of this system. Human and non-human primate research indicates that different cells within STS are responsive to a variety of information about the human body, including whole-body motion, hand/arm gestures, lip reading, and eye gaze direction (for reviews see Allison, Puce, & McCarthy, 2000; Puce & Perrett, 2003). Although data relating STS to biological motion perception now have a considerable history, scientists have begun in recent years to make broader inferences about the precise functions of this area – functions related to social information processing. For example, STS is often included among a constellation of brain structures known as the mirror neuron system (MNS) that are thought to support action recognition, social understanding and imitation (see Iacoboni & Dapretto, 2006). Other research has demonstrated that STS responds to speciﬁc aspects of social gestures, including ‘‘the intentionality and appropriateness of biological motion’’ (Pelphrey, ⇑ Corresponding author. Address: Center for Autism Research, Children’s Hospital of Philadelphia, 3535 Market Street, Suite 860, Philadelphia, PA 19104, United States. Fax: +1 267 426 7590. E-mail address: [email protected]
il.com (J.D. Herrington). 0278-2626/$ - see front matter Ó 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.bandc.2011.09.001
Morris, & McCarthy, 2004, p. 1711; also see Jellema, Baker, Wicker, & Perrett, 2000; Pavlova, Sokolov, Birbaumer, & Krägeloh-Mann, 2008; Pelphrey & Morris, 2006; Wyk, Hudac, Carter, Sobel, & Pelphrey, 2009). When interpreting the meaning of observed STS activity, various overlapping constructs have been used, such as ‘‘action recognition’’ (Gallese, Fadiga, Fogassi, & Rizzolatti, 1996; Iacoboni, 2005; Molnar-Szakacs, Iacoboni, Koski, & Mazziotta, 2005), ‘‘action understanding’’ (Thioux, Gazzola, & Keysers, 2008), ‘‘intentional attunement’’ (Gallese, 2006), and ‘‘intentional stance’’ (Pelphrey et al., 2004). Although the functions ascribed are similar, there are important differences – in particular, whether they primarily encompass ‘‘goal-driven’’ movement (as in Gallese et al., 1996; Iacoboni, 2005), or are inclusive of any kind of movement that is amenable to understanding or interpretation (which ostensibly includes all movement; Pelphrey et al., 2004). This study focuses primarily on the role of STS in movement understanding. For the purposes of this study, we view movement perception as fundamentally a visual information process, movement understanding (or action understanding) as an information process dependent on intact movement perception but with some functional distinctions, and social understanding as a higher-order cognitive process that relies on intact movement perception when the social information being processed is visual. In principle, each of these component processes could be implemented by one or more portions of STS; the literature has made cases for each. However, this same literature consists primarily of paradigms where there the accomplishment of movement understanding is
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never actually measured. Rather, it is typically inferred by manipulations of the type of movement being perceived by research participants. These tasks generally follow one of two general forms. In the ﬁrst form, brain activation during human movement perception is compared to scrambled, occluded, or non-human movement stimuli (e.g., Grèzes et al., 2001; Saygin, Wilson, Hagler, Bates, & Sereno, 2004; Thompson, Clarke, Stewart, & Puce, 2005; Thompson, Hardee, Panayiotou, Crewther, & Puce, 2007; Vaina, Solomon, Chowdhury, Sinha, & Belliveau, 2001). These paradigms are generally ill equipped to differentiate human movement perception from movement understanding, as both are absent from the control condition. In the second form, brain activation is compared between conditions where participants perceive human movement that is clearly goal-directed (e.g., an arm swinging a hammer) versus human movement that has no clear goal (e.g., an arm and empty hand moving up and down with no obvious purpose; e.g., Binkofski et al., 1999; Downing, Jiang, Shuman, & Kanwisher, 2001; Pelphrey et al., 2003, 2004; Pelphrey & Morris, 2006; Wyk et al., 2009). Although this approach allows for inferences between STS and movement understanding, these inferences occur in the context of passive, implicit tasks that makes very few information processing demands. A much better case for STS involvement in movement understanding would come from tasks where processing is taxed in a manner that more closely resembles real-world performance – where individuals have to make rapid judgments about movements that are often ambiguous. In response to the lack of STS data using explicit movement judgments, the present study used a challenging paradigm where the success or failure of movement judgment can be examined on a trial-to-trial basis. This study uses a biological motion paradigm consisting of point-light displays (PL), where a human form is represented by an array of dots conﬁgured to represent joints and limbs (Johansson, 1973; see Fig. 1, left panel). Participants in this study were asked to indicate the direction (left or right) in which a PL walking ﬁgure appeared to be moving. On a participant-by-participant basis, these stimuli were scrambled such that accuracy was reduced to about 72% (where 50% is chance). The key prediction is that, if STS is critical to movement understanding, it will be signiﬁcantly more active during trials where participants were accurate in determining the walking direction (compared to incorrect trials). The PL literature includes many methods for degrading human form information: limitations on dot lifespan, scrambling of dot movement phases, inversion and occlusion of dots, etc. These
Fig. 1. Illustrations of point-light walker (left) and wheel (right) animations. Dotted lines illustrate the outlines of the ﬁgures (these did not appear in the actual experiment). Squares represent noise (these appeared as dots in the actual experiment, of the same size and shape as the walker/wheel dots). Actual dots were all white on a black background.
manipulations are typically selected to test speciﬁc psychophysical models of human movement perception (for a review of many variations on PL biological motion, see Blake & Shiffrar, 2007). The manipulation used here was selected primarily for a different reason: to make the task challenging and introduce response uncertainty. Although multiple dot manipulation approaches could have achieved this end (a number were piloted early in this study), we used a speciﬁc approach that preserved many aspects of PL human movement (namely, comparability with non-distorted PL stimuli in terms of overall luminance, pendular motion of individual dots, and average dot velocity), while disrupting the conﬁgural motion information that makes the PL human form so salient. By displacing and rotating each dot around its center of motion, it was possible to parametrically manipulate the difﬁculty of the left right judgment and measure concomitant variations in brain activity. Stimuli used in this study were also designed to address a key limitation of biological motion perception studies in general, and PL studies in particular. Almost no studies have used PL control stimuli that necessitate the application of any heuristic about the conﬁgural motion of objects.1 Most PL studies compare biological motion to scrambled dot motion (i.e., Grossman et al., 2000; Saygin, 2007; Saygin et al., 2004; Ulloa & Pineda, 2007; Vaina et al., 2001). However, scrambled motion differs from biological motion in more than just the percept of the person; the two conditions differ with respect to coherent, plausible, rule-based movement (Pelphrey et al., 2003). This study therefore used a rule-based control stimulus – a spinning wheel composed of the same number of dots and average dot velocity as the PL walker. For both the PL walker and wheel, participants had to choose whether the ﬁgure appeared to be moving to the left or right. This design can also test whether other areas of the social brain play a role in movement understanding. Of particular interest is the fusiform gyrus (FG), a structure playing a critical role in the perception of faces and human (or anthropomorphized) ﬁgures (Castelli, Frith, Happé, & Frith, 2002; Castelli, Happe, Frith, & Frith, 2000; Kanwisher, McDermott, & Chun, 1997; Pelphrey et al., 2003; Schultz, 2005; Schultz et al., 2003; Thompson et al., 2005). Portions of FG have proven responsive to a number of PL biological motion paradigms (Bonda, Petrides, & Evans, 1996; Herrington et al., 2007; Michels, Kleiser, de Lussanet, Seitz, & Lappe, 2009; Peelen, Wiggett, & Downing, 2006; Vaina et al., 2001). Although one prior study has established the coupling between FG activation and successful facial information processing (Grill-Spector, Knouf, & Kanwisher, 2004), none to date have examined this area vis-à-vis accuracy for a movement understanding task. In the present study we also examined exploratory hypotheses regarding the role of amygdala in biological motion processing. In an early PL imaging study, Bonda et al. (1996) observed increased amygdala activation while participants watched a moving PL human ﬁgure. However, as Bonda et al. (1996) pointed out, their amygdala ﬁndings may ultimately relate to affective qualities of the speciﬁc PL stimuli they used (ﬁgures that appeared to be dancing) rather than movement understanding per se. The present study attempts to shed light on this question by examining amygdala activity in response to a human form that is devoid of any salient affective content. 1 In this context, a heuristic refers to any interpretation about the movement of an object that is not entirely manifest as basic direction vectors (such as local or global motion in a random dot kinematogram), but instead requires the application of some learned rule about coherent motion to make a directional inference (as in a wheel spinning ‘‘on the spot’’). Although Pelphrey et al. (2003) attempted to separate human from meaningful non-human motion via the use of a clock with a moving pendulum, the movement of pendulum itself did not convey any semantically meaningful, rulebased information that one could easily interpret beyond general object identiﬁcation.
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In summary, this study tested three hypotheses. First, it was predicted that PL human movement activates a network of regions involved in human movement perception, including STS, IFG, and FG. Second, it was predicted that these areas are signiﬁcantly more active during trials where participants correctly perceive the direction of the PL walking ﬁgure. In a third exploratory hypothesis, it was predicted that amygdala will show increased activity during PL walker perception. 2. Method 2.1. Participants and selection procedures Sixteen participants responded to advertisements posted in the Yale University area. Data from two participants were dropped due to computer error, leaving 14 participants (8 female, mean age 26.8 years). All participants were right-handed, native speakers of English who denied a present psychiatric diagnosis or history of head injury or drug abuse. 2.2. Experimental task Participants completed two different PL human movement experiments as part of the same session; only data from the ﬁrst experiment are presented here. The task followed a two-by-two design with factors representing Stimulus Type (walker or wheel) and Distortion Level (no distortion or 72%-accuracy-level distortion, as determined by pretesting, described below). Stimuli consisted of 13 animated dots resembling either a walking person or a spinning wheel (see Fig. 1). Dot coordinates for the PL walker were taken from Vanrie and Verfaillie (2004, based on digitized movements of actual people) and rendered using Vision Egg (Straw, 2008). The overall percept was of a walker or wheel moving in a sea of random dots. Walker and wheel dots were centrally presented, as if on a treadmill. The task was to indicate as quickly as possible via button-press whether the person or wheel appeared to move to the left or right. A target accuracy of 72% was selected for the distorted conditions for two reasons. The ﬁrst was to assure that we could expect any given participant to provide, on average, at least 25 error trials during fMRI scanning (72% was selected instead of 75% to provide some margin for error). Because fMRI tasks vary in so many ways, there are few heuristics in the imaging literature regarding the minimum number of trials required for a given task. 25 trials, with approximately two fMRI samples per trial corresponding to the peak of a hemodynamic response, would yield at least 50 fMRI volumes of data for inaccurate trials; in prior studies from our group this has been sufﬁcient to yield condition-wise effects across a variety of experimental tasks (as was the case in the present one). The second reason for 72% was to strike a balance between a task that was too difﬁcult (forcing participants to guess at their responses) and too easy (where it may have been more likely that incorrect responses were due to ancillary attentional factors rather than actively ‘‘grappling’’ with the task but failing). One hundred trials were presented in an event-related manner from each cell of the two-by-two design. Trials consisted of a 250 ms ﬁxation cross followed by a 1 s stimulus presentation and 1.5 s response period (blank screen). Four hundred PL trials and 191 null trials (blank screen, varying in increments of TR between 2750 and 8250 ms as an implicit baseline) were split between two 14.1-min runs. Stimulus presentation orders were optimized using optseq2 (Dale, 1999). Two stimulus schedules (one for each run) were selected from 10,000 permutations. The ﬁnal two schedules were selected to maximize design efﬁciency, operationalized in terms of the variance reduction factor (VRF; Dale, 1999). The VRF
was 23.66 and 23.97 for the two individuals runs; when using the square root of N as an estimate of signal strength, the combined VRF of the two runs was 33.67. Stated in terms of percent signal change needed to observe effects in actual fMRI data (i.e., using smoothness/variability in fMRI time series data as noise estimates), efﬁciency estimates were approximately .71% for both runs combined (in other words, percent signal changes of .71% were sufﬁcient a priori for observing main effects of Distortion or Stimulus Type). For the Distorted Walker condition, motion trajectories for the 13 PL walker dots were rotated from 0% (no rotation) to 100% (orthogonal to standard path, i.e., 90°). Two additional modiﬁcations were made: (1) a vertical shift was added (0% = no shift; 100% = 100-pixel shift added or subtracted randomly from each dot), and (2) 13 noise dots were added with a one-to-one trajectory matching with the walker dots but located randomly on the screen and randomly rotated from 0° to 360°. For the Distorted Wheel condition, subsets of dots rotated in the opposite direction as the majority of dots (at 100% distortion, no overall direction was discernable). Noise dots were included as in the walker conditions. Stimuli subtended 13.6° horizontally and 17.2° vertically. MRI pilot testing of this paradigm was implemented with concurrent eye tracking. It was clear during piloting that participants rarely foveated anywhere but in the center of the image across all trials (consistent with the conﬁgural nature of the stimuli). As it was therefore unlikely that fMRI activation could be attributed to eye gaze patterns, eye tracking was discontinued. 2.3. Pretesting Each participant underwent 30 min of pretesting in order to determine the distortion level necessary for that speciﬁc participant to reach an accuracy of 72% for the distorted conditions. During pretesting, participants viewed 30 PL walker and 30 PL wheel trials at 60% (difﬁcult), 40%, 20% and 0% (easy) distortion levels. The 72%-correct distortion level was calculated via a linear interpolation between the distortion levels with average accuracies just above (‘‘lower bound’’) and below (‘‘upper bound’’) 72% for each participant. The ﬁnal fMRI distortion level was calculated using the following formula: ﬁnal distortion level = lower bound distortion level + ((.72 lower bound accuracy)/(upper bound accuracy lower bound accuracy)) (upper bound distortion level lower bound distortion level). Although 20% distortion increments were chosen for pretesting, the ﬁnal distortion level chosen for fMRI was allowed to vary continuously according to the above formula. Although more sophisticated signal detection algorithms were available to calculate individual distortion thresholds, this simple linear interpolation was sufﬁcient for study objectives (assuring that participants made errors during fMRI and to equating accuracy between people). 2.4. MRI data collection During each of two PL task runs, 308 fMRI images were acquired using a gradient-echo echo-planar sequence (TR = 2750 ms, TE = 25 ms, ﬂip angle 60°, FOV = 24 cm) on a 3T Siemens Trio. Forty contiguous axial slices (3.5 mm isotropic) were acquired parallel to the anterior/posterior commissures. Two structural MRI sequences were also acquired for standard space (MNI) registration. Following standard FSL registration procedures, a structural sequence (FLASH, .9 .9 3.5 mm, TR/TE = 300/2.46 ms, ﬂip angle = 60) acquired in the same plane as the fMRI data was registered to a structural sequence providing full head coverage (MPRAGE, 1 mm isotropic resolution, TR/TE = 2530/3.34 ms, ﬂip angle = 7°), which in turn was registered to the MNI template. The concatenation of
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these transformations was used to bring fMRI statistical maps into MNI space.
3. Results 3.1. Behavioral data
2.5. Behavioral performance analysis The task was designed with two behavioral objectives in mind: (1) that the distorted trials have signiﬁcantly more errors than non-distorted trials, and (2) that accuracy be matched between distorted PL walker and distorted PL wheel conditions. Shapiro– Wilk tests indicated that accuracy data deviated from a normal distribution for both the non-distorted walker and wheel conditions. For this reason, the non-parametric Kolmogorov–Smirnoff (KS) test (exact) was used for behavioral data analyses. 2.6. FMRI data reduction and analysis Functional image processing and statistical analyses were implemented using FSL (http://www.fmrib.ox.ac.uk/fsl). Each time series was motion-corrected, intensity-normalized, spatially ﬁltered (FWHM = 8 mm) and temporally ﬁltered (nonlinear highpass ﬁlter with a 1/50 Hz cutoff; Jenkinson, Bannister, Brady, & Smith, 2002; Jenkinson & Smith, 2001). Statistical maps were generated via multiple regression on each intracerebral voxel (Woolrich, Ripley, Brady, & Smith, 2001). Explanatory variables (EVs) for each trial type (distorted and non-distorted walker and wheel) were convolved with a gamma function. Motion parameters were included as nuisance EVs. The initial analysis plan was to examine the effect of Stimulus Type by including both Distortion Levels. However, despite pretest control of task difﬁculty, participants performed signiﬁcantly better in the scanner on the wheel condition than the walker condition (see Results section). In order to avoid task difﬁculty confounds, analysis of Stimulus Type focused on the non-distorted conditions only, where accuracy and response times did not differ. Lastly, statistical maps representing the main experimental factors were calculated for each participant by contrasting correctly and incorrectly answered distorted PL walker trials (henceforth called the Accuracy map). Note that because participants have fewer incorrect than correct trials, the GLM design applied to each ﬁrst-level time series is inherently unbalanced – a circumstance that FSL’s approach to GLM is speciﬁcally designed to accommodate (Woolrich, Behrens, Beckmann, Jenkinson, & Smith, 2004). A parallel analysis was carried out for the distorted wheel condition to establish the speciﬁcity of walker accuracy effects. 2.7. Group-level analyses and family-wise error correction All functional activation maps were transformed into MNI space using FLIRT (Jenkinson et al., 2002). Group-level analyses consisted of separate t-tests comparing the Stimulus Type and Accuracy effects to zero (including the random effect of participant; Beckmann, Jenkinson, & Smith, 2003). Post hoc analyses of hemispheric asymmetries were carried out by submitting individual subject Stimulus Type and Accuracy beta maps (dependent variable) to t-tests comparing each voxel to its contralateral homolog (i.e., treating midline as a zero point, moving sagittally in a positive direction and comparing each voxel to one the same distance from the origin but in the negative direction; see Herrington, Taylor, Grupe, Curby, & Schultz, 2011; Herrington et al., 2010). Family-wise error (FWE) was controlled via the simultaneous application of a per-voxel statistical threshold of p < .001 and cluster size thresholds determined from Monte Carlo simulations (Ward, 2006). All reported clusters were signiﬁcant at a FWEcorrected level of p < .05 (see Supplementary Materials for more detailed information about FWE correction methods).
See Table 1 for accuracy and response time data. Participants were signiﬁcantly less accurate for distorted than non-distorted trials across PL walker and wheel conditions (distorted/non-distorted accuracy = 77.6%/96.0%; KS D = .750, p < .001). Contrary to expectations from pretesting data, participants were more accurate for PL wheel than PL walker trials during fMRI at nearly a trend level (PL wheel/PL walker accuracy = 89.2%/84.3%; D = .214, p = .115), due to increased accuracy for the distorted PL wheel condition (81.6% and 73.4%, respectively; D = .393, p = .013). Accuracy for the non-distorted PL conditions did not signiﬁcantly differ. Although responses were faster for correctly answered non-distorted trials (across conditions), this effect was not signiﬁcant when submitted to KS testing (non-distorted/distorted RT = 926/ 1107 ms, D = .429, n.s.). RTs also did not differ by Stimulus Type (walker/wheel RT = 1111/1229 ms, D = .286, n.s.). The simple effect of Stimulus Type for non-distorted stimuli was not signiﬁcant (non-distorted walker/wheel RT = 960/977 ms, D = .213, n.s.). 3.2. Brain activity in response to human movement (the stimulus type map) The Stimulus Type map (i.e., PL walker versus wheel) yielded eight clusters of activity (see Table 2). As predicted, two of these clusters were localized to STS (in left and right hemispheres, respectively), though focused predominantly within the ‘‘extrastriate body area,’’ including MT+/V5, middle and inferior temporal gyri (see Fig. 2A, Dumoulin et al., 2000; Downing et al., 2001). The two clusters overlapped with STS clusters reported in multiple PL biological motion studies (e.g., Iacoboni et al., 2001; Saygin et al., 2004; Thompson et al., 2007). Also consistent with a priori hypotheses, a signiﬁcant cluster was identiﬁed in the medial section of right pars opercularis (IFG), closely matching the frontal MNS region reported by Iacoboni and others (Fig. 2A, Binkofski et al., 1999; Carr, Iacoboni, Dubeau, Mazziotta, & Lenzi, 2003; Johnson-Frey et al., 2003; Molnar-Szakacs et al., 2005). Activity in this region has been tied to the perception and imitation of simple movements and gestures, as well as static facial expressions (Carr et al., 2003; Dapretto et al., 2006; Iacoboni et al., 1999). Two clusters were identiﬁed in left and right FG (Fig. 3 and Table 2). These ﬁndings were consistent with multiple studies involving the perception of movement from ﬁgures interpreted as animate with human properties, such as agency (Castelli et al., 2000, 2002; Pelphrey et al., 2003; Schultz et al., 2003; Thompson et al., 2005).
Table 1 Behavioral performance data. Distortion level
Non-Distorted Distorted Distorted – incorrect trials
960 1121 1213
977 1083 1226
Note. RTs for the Non-Distorted and Distorted include correct trials only. Participants were signiﬁcantly less accurate for distorted than non-distorted trials across PL walker and wheel conditions (distorted/non-distorted accuracy = 77.6%/96.0%; KS D = .750, p < .001). Participants were more accurate for PL wheel than PL walker trials during fMRI at nearly a trend level (PL wheel/PL walker accuracy = 89.2%/ 84.3%; D = .214, p = .115), due to increased accuracy for the distorted PL wheel condition relative to distorted PL walker (81.6% and 73.4%, respectively; D = .393, p = .013). Response times did not differ between correctly answered distorted and non-distorted trials, or by Stimulus Type.
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Table 2 Summary of signiﬁcant activation clusters. Region
Cluster size (# voxels)
Human movement activation (compared to wheel condition) Amygdala/peri-amygdala 20, 7, 23 10 Peri-amygdala/hippocampus 18, 21, 18 113 Fusiform gyrus 46, 56, 19 72 31, 32, 21 312 Inferior frontal gyrus 38, 7, 27 40 Lingual gyrus 10, 45, 0 43 Superior temporal gyrus/MT+ 48, 64, 10 62 51, 63, 6 872
Peak z-value 3.720 5.176 4.568 5.677 4.176 3.849 6.500 7.775
Activation for correct walker trials (compared to incorrect walker trials) Peri-amygdala 15, 10, 25 71 3.825 Temporal pole 38, 11, 24 113 4.172 Superior temporal sulcus 66, 13, 6 66 3.801 48, 46, 13 39 3.263 Note. All clusters were signiﬁcant at p < .05 (corrected for multiple comparisons). MNI coordinates represent centers of intensity for each cluster.
Additional clusters of activation were identiﬁed in amygdala and adjacent medial temporal cortex (see Fig. 3). These ﬁndings are consistent with accounts of amygdala function that encompass a variety of socially relevant behavior that need not have obvious affective content. Although amygdala/peri-amygdala activation was observed in the right hemisphere only, no signiﬁcant lateralization of signal was observed in the hemispheric asymmetry map. 3.3. Brain activity corresponding to correct walker trials (the accuracy map) Trials with no behavioral responses were excluded from all fMRI and behavioral analysis. One of the 14 participants made only two errors for the distorted PL walker condition; fMRI data for this participant were therefore excluded from the Accuracy fMRI analysis. As predicted, accurate trials were associated with increased superior temporal sulcus (STS) activation (see Fig. 2B and C, and Table 2). Two right-hemisphere STS clusters were identiﬁed. The ﬁrst, more posterior cluster (Fig. 2C) overlapped with or was directly adjacent to clusters reported in numerous studies of biological motion perception, particularly PL biological motion (Carr et al., 2003; Michels et al., 2009; Pelphrey et al., 2004; Puce, Truett, Bentin, Gore, & McCarthy, 1998; Thompson et al., 2005, 2007). This cluster made direct contact with the superior, anterior portion of the STS cluster from the Stimulus Type map, but did not overlap with it. The second STS cluster (Fig 2B) was more anterior than the ﬁrst, corresponding closely with studies examining the whole body movement as well as arm, hand, and eye movement (Calvert, Campbell, & Brammer, 2000; Grèzes et al., 2001; Howard et al., 1996; Puce & Allison, 1999). This cluster showed increased activation for correct trials versus baseline (null trials), but was driven primarily by decreased activation for incorrect trials versus baseline (Fig. 4). This cluster also did not overlap with the STS cluster from the Stimulus Type map. Post hoc asymmetry testing indicated that the posterior STS cluster was signiﬁcantly right-lateralized (no other signiﬁcant lateralization was observed in a priori regions for the Accuracy map). Although response time did not signiﬁcantly differ between accurate and inaccurate trials, post hoc multiple regression analyses were carried out predicting activation for correct walker trials from response time on a per-voxel basis, and when using both of the above STS clusters as ROIs (i.e., averaging parameter estimate values within each ROI for each participant). Neither of these tests yielded signiﬁcant response time effects within STS.
No signiﬁcant clusters of activation were observed in the STS or amygdala when comparing correct to incorrect distorted wheel trials, indicating that the STS and amygdala clusters in the walker Accuracy map are speciﬁc to biological motion. Using the posterior STS, anterior STS and amygdala clusters as ROIs, peak z-values for correct versus incorrect wheel trials were, .623, .096, and 1.444, respectively (all n.s. when treated as inferential statistics). 4. Discussion 4.1. Superior temporal sulcus, action observation, and action judgment Data from this study are among the ﬁrst to link activity within temporal cortex to a behavioral measure of biological motion understanding. These data represent an important extension of prior STS research by establishing the sensitivity of this area to observable manifestations of movement understanding – not merely to passively perceived changes in the plausibility or intentionality of movement (as in most prior STS studies). These data therefore make it considerably more likely that STS implements the type of online interpretation of human movements and gestures required for social information processing. Data from the Accuracy contrast underscore the critical role of STS in decision-making about human movement. The accuracysensitive cluster within posterior STS overlaps with multiple reports of PL biological motion activation (Carr et al., 2003; Michels et al., 2009; Pelphrey et al., 2004; Thompson et al., 2005, 2007). Results were within 8 mm of those reported by Grossman, Blake, and Kim (2004) in the only paper known to the authors examining effects of task performance on STS function. Importantly, the stimuli comprising accurate and inaccurate trials in this task were created in an identical manner – their classiﬁcation was based entirely on judgments made by participants (i.e., accuracy). Therefore, these data suggest that STS plays a critical role in the understanding of human movement. The location of STS – a convergence zone for dorsal and ventral stream inputs, and parietal cortex – represents compelling evidence that the area may function as a ‘‘comparator,’’ matching perceptual inputs against known, internal representations of human movement (Boussaoud, Ungerleider, & Desimone, 1990; Felleman & Van Essen, 1991; Milner, 2006; Oram & Perrett, 1996). In a paper on MNS, Carr et al. (2003) hypothesized that STS received ‘‘efferent copies of motor plans’’ from parietal and frontal areas, and that these are compared via ‘‘a matching mechanism between the visual description of the observed action and the predicted sensory consequences’’ of that action (p. 5497; also see Iacoboni & Dapretto, 2006). Although this account appears compelling prima facie, it remains largely untested in humans. The present data underscore the need for future studies in this area – particularly those examining brain connectivity. The more anterior of the two accuracy-sensitive clusters was driven primarily by a decrease in activation relative to baseline (null trials). The relationship between this particular portion of STS and successful movement perception must therefore be treated cautiously. Because the two explanatory variables in this analysis represented an endogenous, participant-driven factor (task accuracy), this decreased activation may warrant a somewhat different interpretation than ‘‘deactivation’’ in conventional fMRI analyses. In particular, it is possible that activity within the anterior STS cluster may represent a precondition or preparatory state for successful movement judgment, active at a basal level during multiple cognitive processes (hence during null trials) but more essential during movement judgments (hence the behavioral performance errors when underactive). This particular pattern of activation clearly needs replication to make this interpretation with conﬁdence.
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Fig. 2. Brain activation during point-light biological motion perception. All STS clusters reported here are circled in green. Panel A: Activation for PL walker > PL wheel in the superior temporal sulcus/MT+/V5. The right frontal lobe of the right hemisphere was cut away to reveal the signiﬁcant cluster of activation within the medial portion of pars opercularis. Panel B: Greater activation for correct than incorrect PL walker trials in superior temporal sulcus (anterior cluster). Panel C: Greater activation for correct than incorrect PL walker trials in superior temporal sulcus (posterior cluster). All clusters of activity are signiﬁcant at p < .05 (corrected for multiple comparisons). See Table 1 for cluster centers of intensity.
Accuracy-based activation in posterior STS was signiﬁcantly right-lateralized. Although few studies have conducted formal analyses of hemispheric asymmetries for biological motion processes, there is some evidence pointing to a right-hemisphere specialization for these processes (for reviews see Decety & Lamm, 2007; Grosbras, Beaton, & Eickhoff, 2011). Much like the rightward lateralization of FG function during face perception (see Herrington et al., 2011), the present STS laterality ﬁnding is consistent with the overall right hemisphere dominance for tasks involving the integration of visuospatial input and identiﬁcation of visual structure (Corballis, 2003, 2002; Roser et al., 2005). The present ﬁndings suggest that human movement judgment stands alongside human movement perception in its association with the right-hemisphere. Interestingly, neither of the two STS clusters in the Accuracy map overlapped with the STS cluster that was sensitive to Stimulus Type (i.e., fully coherent walker versus wheel), though the more posterior cluster bordered it. Other than the assumption derived
from prior PL studies that activation would occur in posterior portions of STS, this study did not make speciﬁc a priori predictions regarding which sections of STS would relate most to task accuracy. There are in fact few theoretical treatments of the functional distinction between sub-regions of the STS, other than some selectivity for the visual processing of speciﬁc body areas (see Puce & Perrett, 2003). The present data suggest that movement perception and movement understanding are implemented by distinct areas of STS, with the former in the most posterior portions of STS (and adjacent extrastriate areas) and the latter more anterior. This is consistent with the observation that most PL biological motion studies report activation in very posterior STS using paradigms that generally make few explicit requirements regarding movement judgment. The activation of areas adjacent to STS for the walker versus wheel comparison is consistent with prior work on body movement perception. In particular, the ventral and posterior extent of
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present data suggest that STS implements higher order visual information processes that are more integral to human movement judgment, whereas other portions of STSC are responsive to the perceptual properties of human stimuli irrespective of how the stimuli are interpreted (for references in support of this perspective, see Allison et al., 2000; Pelphrey & Carter, 2008; Peelen & Downing, 2007). Replication of the present ﬁndings using other types of human movement tasks and stimuli – particularly whole-body videos – will be important in supporting this hypothesis. 4.2. Inferior frontal gyrus and action recognition
Fig. 3. Brain activation in fusiform gyrus, amygdala, and peri-amygdala for nondistorted PL walker versus wheel. FG clusters are labeled A, and amygdala/periamygdala clusters labeled B. Clusters of activity are signiﬁcant at p < .05 (corrected for multiple comparisons). See Table 1 for cluster centers of intensity.
As predicted, a medial section of right pars opercularis was signiﬁcantly more active during the perception the PL walker than the PL wheel. The observed area of activity corresponds closely with activation reported by Iacoboni and others in studies of MNS (Binkofski et al., 1999; Carr et al., 2003; Johnson-Frey et al., 2003; Molnar-Szakacs et al., 2005). IFG activity did not emerge in the Accuracy contrast, which may indicate that IFG activity plays a more peripheral role in action understanding than is generally assumed (see Heyes, Bird, Johnson, & Haggard, 2005; Hickok, 2009; Mahon & Caramazza, 2008). However, due to the limited number of error trials in the Accuracy analysis, present data may be insufﬁciently powered to sustain null-hypothesis interpretations. 4.3. Fusiform gyrus
Fig. 4. Peak parameter estimates for clusters with signiﬁcantly more activation for correctly answered walker trials. The anterior and posterier STS clusters are displayed in Fig. 2, panels B and C. Error bars represent the standard error of means.
the walker versus wheel STS clusters support the theory that STS operates in concert with extrastriate regions. This is consistent with Puce and Perrett’s (2003) notion of an ‘‘STS complex’’ (STSC) encompassing STS, MT+/V5, and the extrastriate body area (p. 438; also see Downing et al., 2001). However, it must be kept in mind that this complex encompasses a large area of cortex with likely functional heterogeneity that has yet to be fully elucidated. Indeed, the lack of accuracy effects in other portions of STSC speaks to some heterogeneity within this area (STS, but not MT+/ V5 or EBA, were signiﬁcantly more active for correct versus incorrect trials). Given that both MT+/V5 and EBA were responsive to the Stimulus Type manipulation (where the judgment task was the same for the walker and wheel conditions), and are responsive to a variety of tasks involving the passive perception of human movement (for review see Peelen & Downing, 2007), it is plausible that activity within these areas is necessary but not sufﬁcient for making judgments about human movement. In other words, the
Although FG is traditionally associated with face perception, a growing literature implicates portions of this area in broader aspects of ‘‘person perception’’ (for review see Schultz, 2005). The present ﬁnding of increased bilateral FG activity stands among numerous other studies using PL stimuli (Bonda et al., 1996; de Lussanet et al., 2008; Herrington et al., 2007; Michels et al., 2009; Peelen et al., 2006; Vaina et al., 2001). The absence of an association between FG and accuracy in this study was somewhat surprising, given prior data establishing the selectivity of this area for accurate versus inaccurate trials involving face stimuli (Grill-Spector et al., 2004). The overall pattern suggests two possibilities (not mutually exclusive). First, it is possible that FG function is more integral to the interpretation of facial rather than whole-body information, though it plays a critical role in the perception of both types of information. Second, it is possible that FG is more integral to any task where identity information is highly salient (as in subordinate-level discrimination; Grill-Spector et al., 2004) – a characteristic that may distinguish walking ﬁgures from spinning wheels (hence the FG effect for Stimulus Type), but not identically constructed walking ﬁgures from one another (as in accurate versus inaccurate trials). 4.4. Amygdala and ‘‘social surveillance’’ Current theories of human amygdala function frequently emphasize ‘‘emotional surveillance’’ – monitoring of the environment for emotionally relevant information (see Zald, 2003). However, numerous studies, extending back to the seminal work of Kluver and Bucy (1939), posit a broader role of the medial temporal lobes in social behavior that includes but is not limited to the core construct of emotion. In the present study, increased amygdala activation was observed for the PL walker compared to the PL wheel, despite the fact that the PL walker has very little obvious affective content. We therefore argue that the present data establish a role for amygdala in ‘‘social surveillance’’ that is magniﬁed considerably in the presence of emotionally salient information, but present in some nonemotional contexts.
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However, this account does not explain why amygdala activation was observed in this study, but remains uncommon in the PL biological motion literature. As most PL studies focus on visual cortex, it is possible that some data from amygdala go unreported in this literature. It is also possible that amygdala activity is present among some commonly used control stimuli, and therefore ‘‘subtracted out’’ when implementing statistical contrasts. This would seem mostly likely for stimuli such as inverted walkers, where a veridical human form is perturbed but still clearly identiﬁable, possibly evoking amygdala-mediated social information processes. This explanation is less satisfying for designs where walking ﬁgures are contrasted with stimuli where there is no human form information (as in random dot displays; for example, see Grèzes et al., 2001). There have also been studies that compare biological motion stimuli to very low-level control conditions where signiﬁcant amygdala activation is not reported (for example, Grossman & Blake, 2002, or Peuskens, Vanrie, Verfaillie, & Orban, 2005, where analyses compare PL stimuli to ﬁxation). Lastly, it is possible that it is related to the somewhat large number of trials that went into the present paradigm (100 for each condition), perhaps allowing for the identiﬁcation of relatively weak signals (i.e., amygdala) that otherwise go undetected. Ultimately, it is unclear why the present study succeeded in observing amygdala activation where so others have not. Clearly the present amygdala ﬁnding is in need of replication, particularly in the context of a design with a variety of control stimuli and a large number of trials to maximize statistical power. 4.5. Considerations for future research A central premise of this paradigm is that differences in accuracy for the PL task reﬂect differences in the functioning of systems involved in human movement interpretation. The presence of accuracy-driven STS activation in the predicted direction argues strongly against accurate trials representing chance events (i.e., ‘‘lucky guesses’’). However, it cannot be entirely ruled out that errors occurred due to earlier steps in visual information processing – i.e., that the percept of incorrect stimuli was somehow different than those answered correctly. Given that stimuli for correct and incorrect trials were created in an identical manner, brain activity differences were evidently driven by differences in endogenous cognitive events. It is nevertheless possible that, on a trial-by-trial basis, different aspects of the cascade from perception to judgment may be responsible for an incorrect response. The complex relationship between perception and understanding dovetails with a larger theoretical problem that complicates the interpretation of numerous movement understanding (action recognition) studies. The problem can be summarized with the following question: what constitutes movement understanding, and how can this understanding be measured? It is clear that, in forced-choice tasks such as the one used in this study, some understanding of human movement must have occurred for participants to arrive at a correct response above chance levels. However, in the case of incorrect responses, it is difﬁcult to make strong inferences about what precise cognitive operation failed, and whether such an operation could be legitimately labeled as ‘‘understanding.’’ Furthermore, it is possible that observed STS accuracy effects reﬂect a more peripheral aspect of motion understanding, such as conﬁdence in movement judgments (though we know of no studies to date that tie this function independently to STS). A further ambiguity in deﬁning movement ‘‘understanding’’ relates to the relevance of movement ‘‘goals.’’ In one of the few detailed considerations of this issue, Pelphrey et al. (2004) advocated a notion of social perception inherited from Dennett’s (1987) ‘‘intentional stance’’, focusing on the human propensity to create a fairly expansive prediction space when determining
another’s intentions. A key implication of this stance is that the term ‘‘goal-directed’’ may encompass a very large array of behaviors (and concomitant human movements/gestures) – much larger than was originally surmised in the seminal non-human primates studies of action perception (i.e., Gallese et al., 1996). It is nevertheless very difﬁcult to determine equivocally whether actions such as walking, looking, or grasping are inherently goal-directed. The presence of a goal target (such as an object being grasped) may help in this determination, but this seems neither necessary nor sufﬁcient. Ultimately, present STS data, along with those from numerous other studies using very basic human movement, would seem to indicate one of three possibilities: (1) although STS is critical to the interpretation of human movement, that movement need not convey a clear intention or goal to yield STS activity; (2) what registers as goal-directed behavior encompasses a very large array of actions (e.g., walking without a clear objective or destination), or (3) the movement information processing system (and perhaps social information processes more generally) can operate in ‘‘surveillance mode’’, and may be modulated by percepts of goal-directed motion, but does not strictly require them in order to engage. Another consideration for future studies is the speciﬁcity of accuracy ﬁndings for different biological motion tasks (whereas the present study focused on sensitivity, i.e., whether STSC shows any accuracy effects). A review of the biological motion literature indicates that different types of PL biological motion tasks (i.e., discriminating forward versus backward or upright versus inverted motion) yield somewhat different patterns of brain activation (for illustrations of task-based variability, see Grosbras et al., 2011; Puce & Perrett, 2003). There are few theoretical models explaining the associations between speciﬁc PL biological motion tasks and subregions of STS (theoretical models are more robust for MT+/V5 and FG). For that matter, there any number of biological motion paradigms using very different stimulus types (namely, human actors) that may necessitate different types of judgments, recruiting different brain regions. Prefrontal cortex activity, for example, appears in many studies involving mental state inferences based on movement perception (see Uddin, Iacoboni, Lange, & Keenan, 2007). However, STS activation is shared in common by many if not most biological motion studies. A variety of point-light biological motion perception studies activate posterior STS in particular (Grosbras et al., 2011). Furthermore, one of the advantages of designs examining accuracy effects is that the same task and stimulus type are used in both ‘‘active’’ (accurately answered trials) and ‘‘control’’ conditions (incorrect trials). For this reason, one would expect that task-speciﬁc effects might be subtracted out, with the remaining activation reﬂecting processes common to other stimulus types. Nevertheless, responding accurately to different types of tasks may necessitate systems implemented by other portions of STS than the one observed here, or different brain areas altogether (other portions of STSC in particular). The speciﬁcity of STS accuracy effects across types of biological motion judgment clearly warrants further investigation. Lastly, it is important to point out that future models of STS function will clearly need to consider connectivity between brain structures. Connectivity analyses (i.e., correlations between brain structures) were initially conducted with the present data, but ultimately discarded due to a lack of robust effects. However, connectivity analyses using electrophysiological measures (EEG and MEG) may ultimately prove more robust in testing hypotheses regarding STS connectivity. Connectivity analyses using in fMRI and M/EEG often rely on similar statistical approaches (namely, multiple regression/correlation between signals from different areas), but the temporal resolution of M/EEG allows for the use of these and other statistical approaches on a larger variety of signals within
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the spectral domain. Signals within speciﬁc frequency bands are thought to reﬂect the coordination (synchrony) of neuronal populations, often over very short time frames. This is particularly relevant here, as inputs to STS from dorsal and ventral streams travel fast – under 100 ms in non-human primates (Oram & Perrett, 1994, 1996). The evidence in humans suggests a cascade of activation for complex conﬁgural movement originating in occipital regions but, in the case of biological motion stimuli, further propagation to temporal areas – all within 200 ms (Pavlova, Lutzenberger, Sokolov, & Birbaumer, 2004). Data from MEG indicate that this cascade of activity is sensitive to selective attention towards human movement (Pavlova, Birbaumer, & Sokolov, 2006). Measures of localized spectral coherence have proven especially useful in characterizing posterior activation during biological motion perception. Future studies can extend on this methodology by examining spectral coherence between areas, testing for rapidly unfolding connectivity patterns that may prove critical to understanding the human movement processing system. Acknowledgments This work was supported by departmental funds from the Yale Child Study Center (to R. Schultz), and the National Institutes of Health Training Grant in the Developmental Neurobiology of Childhood Disorders (to J. Herrington) [Grant number T32 MH18268]. Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.bandc.2011.09.001. References Allison, T., Puce, A., & McCarthy, G. (2000). Social perception from visual cues: Role of the STS region. Trends in Cognitive Sciences, 4, 267–278. Beckmann, C. F., Jenkinson, M., & Smith, S. M. (2003). General multilevel linear modeling for group analysis in FMRI. Neuroimage, 20, 1052–1063. Binkofski, F., Buccino, G., Stephan, K. M., Rizzolatti, G., Seitz, R. J., & Freund, H. J. (1999). A parieto-premotor network for object manipulation: Evidence from neuroimaging. Experimental Brain Research, 128, 210–213. Blake, R., & Shiffrar, M. (2007). Perception of human motion. Annual Review of Psychology, 58, 47–73. Bonda, E., Petrides, M., & Evans, A. (1996). Speciﬁc involvement of human parietal systems and the amygdala in the perception of biological motion. Journal of Neuroscience, 16, 3737–3744. Boussaoud, D., Ungerleider, L. G., & Desimone, R. (1990). Pathways for motion analysis: Cortical connections of the medial superior temporal and fundus of the superior temporal visual areas in the macaque. Journal of Comparative Neurology, 296, 462–495. Calvert, G. A., Campbell, R., & Brammer, M. J. (2000). Evidence from functional magnetic resonance imaging of crossmodal binding in the human heteromodal cortex. Current Biology, 10, 649–657. Carr, L., Iacoboni, M., Dubeau, M., Mazziotta, J. C., & Lenzi, G. L. (2003). Neural mechanisms of empathy in humans: A relay from neural systems for imitation to limbic areas. Proceedings of the National Academy of Sciences of the United States of America, 100, 5497–5502. Castelli, F., Happe, F., Frith, U., & Frith, C. (2000). Movement and mind: A functional imaging study of perception and interpretation of complex intentional movement patterns. Neuroimage, 12, 314–325. Castelli, F., Frith, C., Happé, F., & Frith, U. (2002). Autism, Asperger syndrome and brain mechanisms for the attribution of mental states to animated shapes. Brain, 125, 1839–1849. Corballis, P. (2002). Hemispheric asymmetries for simple visual judgments in the split brain. Neuropsychologia, 40(4), 401–410. Corballis, P. M. (2003). Visuospatial processing and the right-hemisphere interpreter. Brain and Cognition, 53(2), 171–176. Dale, A. (1999). Optimal experimental design for event-related fMRI. Human Brain Mapping, 8, 109–114. Dapretto, M., Davies, M. S., Pfeifer, J. H., Scott, A. A., Sigman, M., Bookheimer, S. Y., et al. (2006). Understanding emotions in others: Mirror neuron dysfunction in children with autism spectrum disorders. Nature Neuroscience, 9, 28–30. Decety, J., & Lamm, C. (2007). The role of the right temporoparietal junction in social interaction: How low-level computational processes contribute to metacognition. The Neuroscientist, 13(6), 580–593. Dennett, D. C. (1987). The intentional stance. Massachusetts: MIT Press.
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