Perceiving and interpreting emotions from various social signals is critical for human social functioning, which enables us to infer the intentions of our conspecifics and further facilitates interpersonal interactions. Facial expressions present the most common non-verbal social communicative signals regarding others’ affective states and intentions (Frith, 2009). In addition to faces, the movement of biological organisms serves as another essential type of social signal carrying significant emotional information, and this information remained salient even from a far distance (de Gelder, 2006). The human visual system is highly sensitive to such signals that we can readily decipher emotions from biological motion (BM), even when it was portrayed by several point lights attached to the major joints (Johansson, 1973; Troje, 2008). Moreover, it has been found that happy point-light walkers were recognized faster and more accurately than sad, angry, or neutral walkers, demonstrating a happiness superiority (Lee and Kim, 2017; Spencer et al., 2016). While these studies provided some insights into the emotion processing mechanism of BM, they relied mainly on the explicit identification and active evaluation of BM emotions. Importantly, the encoding of emotional information also involves a rather automatic and implicit process that is independent of the participant’s explicit identifications (Critchley, 2000; Lange et al., 2003; Okon-Singer et al., 2013; Shafer et al., 2012). Notably, this implicit aspect of emotion processing could be even more effective in probing individual differences and social deficits (Kana et al., 2016; Keifer et al., 2020; Kovarski et al., 2019; Wong et al., 2008), as it requires the intuitive processing of emotions that could not be learned (Frith, 2004). For example, research has shown that individuals with autistic disorders showed altered neural activities during implicit but not explicit emotion processing of natural scenes (Kana et al., 2016). These observations highlighted the importance of using objective measurements to investigate the implicit and automatic aspect of BM emotion processing.

The pupil response hence serves as a promising approach for reliably unfolding the implicit emotion processing of BM as it adds no extra task requirements to the cognitive process (Burley et al., 2017). In particular, pupil size is related to the activity of the automatic nervous system mediated by the locus coeruleus norepinephrine, which not only responds to physical light, but also reflects the underlying cognitive state (Joshi and Gold, 2020). Moreover, it could spontaneously capture the current subjective state and is thus useful for revealing the time course of the related cognitive processing (de Gee et al., 2014; Graves et al., 2021; Kloosterman et al., 2015; Oliva and Anikin, 2018). Recently, this measurement has been introduced to the field of emotion perception, and emerging evidence has suggested that emotions conveyed by social signals (e.g., faces, voices) could modulate pupil responses. For instance, happy and angry dynamic faces elicited a pupil dilation effect as compared with sad and neutral faces (Burley and Daughters, 2020; Prunty et al., 2022). Noticeably, the point-light BM, as another critical type of social signal, also conveyed salient affective information. Besides, its emotion processing mechanism is closely connected with that of faces (Alaerts et al., 2011; Becker et al., 2011; Lee and Kim, 2017; Yuan et al., 2024). However, it remains unequivocal whether the pupil also responds to the emotional information carried by the minimalistic point-light walker display. Such physiological observation can faithfully uncover the implicit emotion processing of BM, and it also expands the existing line of inquiry on the emotion processing of social cues.

To fill this gap, the current study implemented the pupil recording technique together with the passive viewing paradigm to explore the automatic and implicit emotion processing of BM. We first investigated the pupil responses to point-light walkers with different emotions (i.e., happy, sad, and neutral). In addition to intact emotional BM sequences, we also tested scrambled emotional BM sequences, which lack the gestalt of a global figure but preserve the same local motion components as the intact walkers. Recent studies have shown that such local motion signals play an essential role in conveying biologically salient information such as animacy, and walking direction (Chang and Troje, 2008; Chang and Troje, 2009; Troje and Westhoff, 2006). This study went further to investigate whether this local BM signal could convey emotional information and elicit corresponding pupil responses. Moreover, given that emotion perception from social signals is generally impaired in individuals with autism (Harms et al., 2010; Hubert et al., 2007), we also took the individual autistic traits into consideration by measuring the autistic tendencies in normal populations with the autistic quotient (AQ) questionnaire (Baron-Cohen et al., 2001).

Life motion signals convey salient emotional information that is crucial for human survival and social interactions (Johansson, 1973; Troje, 2008). Here, we reported that such emotional clues carried by the point-light BM walker could exert a modulation effect on pupil responses. Specifically, the happy BM significantly dilated pupils as compared to the neutral BM, and the sad BM evoked smaller pupil responses than the neutral BM, showing distinct emotional modulations on pupil responses that depend on the emotional contents. Moreover, this emotional modulation of pupil responses could not be explained by the low-level differences, as viewing inverted BMs failed to induce such effects. Importantly, when the emotional BM was deprived of the local motion feature through the removal of accelerations, it failed to induce any modulation on pupil responses. Furthermore, the scrambled emotional BM with only the local motion feature retained could still produce a modulation effect on pupil size. Noticeably, this modulation is rapid but rather coarse: viewing the scrambled happy and sad BM would evoke greater pupil size than the scrambled neutral BM in a relatively early time window, while no significant difference was observed between the scrambled happy and sad BM. Taken together, these findings revealed multi-level processing of emotions in life motion signals: the global emotional BM-modulated pupil size in a fine-grained manner that discriminates each type of emotion, and the local emotional BM exerted a coarse but rapid modulation on pupil size.

The emotion processing of BM has been investigated in former studies using explicit behavioral detection and recognition paradigm. It has been reported that happy BM is more rapidly identified and detected as compared to sad and neutral BM (Actis-Grosso et al., 2015; Lee and Kim, 2017; Spencer et al., 2016), while other research observed no such superiority (Chouchourelou et al., 2006). Here, the present study adopted the novel and objective pupillometry index to investigate the emotion processing of BM from a physiological aspect. The pupil index, as a direct reflection of individual arousal level and cognitive state, served as a powerful tool for faithfully and automatically reflecting the implicit processing of emotions in BM. Previous studies have reported larger pupil responses toward emotional images as compared to neutral images, with no significant differences observed between the positive and negative conditions (Bradley and Lang, 2015; Bradley et al., 2008; Snowden et al., 2016). However, these studies mostly adopted complex emotional scene images that conveyed rather general emotional information. When it comes to the specific emotion cues (e.g., happy, sad) delivered by our conspecifics through biologically salient signals (e.g., faces, gestures, voices), the results became intermixed. Some studies reported pupil dilatory effects toward fearful, disgusted, angry, sad faces but not happy faces (Burley et al., 2017). On the contrary, other studies observed larger pupil responses for happy as compared to sad, fearful, and surprised faces (Aktar et al., 2018; Burley and Daughters, 2020; Jessen et al., 2016; Prunty et al., 2022). These conflicting results could be due to the low-level confounds of emotional faces (e.g., eye size) (Carsten et al., 2019; Harrison et al., 2006). Similar to faces, BM also conveyed salient clues concerning the emotional states of our interactive partners. However, they were highly simplified, and deprived of various irrelevant visual confounders (e.g., body shape). Here, we reported that the happy BM induced a stronger pupil response than the neutral and sad BM, lending support to the happy dilation effect observed with faces (Burley and Daughters, 2020; Prunty et al., 2022). Moreover, it helps ameliorate the concern regarding the low-level confounding factors by identifying similar pupil modulations in another type of social signal with distinctive perceptual features.

It has long been documented that pupil size reflects the degree of cognitive effort and attention input (Joshi and Gold, 2020; van der Wel and van Steenbergen, 2018), and indexes the noradrenalin activity in emotion processing structures like amygdala (Dal Monte et al., 2015; Harrison et al., 2006; Liddell et al., 2005). Thus, the happy dilation effect suggests that the happy BM may be more effective in capturing attention and evoking emotional arousal than the neutral and sad BM, which echoes with the happiness superiority reported in the explicit processing of BM (Lee and Kim, 2017; Spencer et al., 2016; Yuan et al., 2023). Instead, the sad constriction effect potentially indicates a disadvantage for sad BM in attracting visual attention and evoking emotional arousal than neutral BM. In line with this, it has been found that infants looked more at the happy BM walker when displayed in pair with the neutral walker, whereas they attended less to the sad walker as compared to the neutral one (Ogren et al., 2019). Besides, it has been revealed by neural studies that, the happy emotion evoked stronger activities in emotionally relevant brain regions including the amygdala, the extrastriate body area, and the fusiform body area, while the sad emotion failed to induce such effects (Peelen et al., 2007; Ross et al., 2019). Still, future studies are needed to provide further evidence regarding the happy advantage and the sad disadvantage in attracting visual attention. For example, in addition to pupil size, the microsaccades also provided valuable insights into attention processes (Baumeler et al., 2020; Engbert and Kliegl, 2003; Meyberg et al., 2017), and potentially involved shared neural circuits with pupil responses (Hafed et al., 2009; Hafed and Krauzlis, 2012; Wang et al., 2012). Future studies could combine the microsaccade index with pupil size to provide a more thorough understanding of BM emotion processing.

Notably, the happy over sad dilation effect was negatively correlated with autistic traits: individuals with greater autistic tendencies showed decreased sensitivities to emotions in BM. In fact, the perception of BM has long been considered an important hallmark of social cognition, and abundant studies reported that individuals with social cognitive deficits (e.g., autism spectrum disorder, ASD) were impaired in BM perception (Blake et al., 2003; Freitag et al., 2008; Klin et al., 2009; Nackaerts et al., 2012). More recently, it has been pointed out that the extraction of more complex social information (e.g., emotions, intentions) from BM, as compared to basic BM recognitions, could be more effective in detecting ASDs (Federici et al., 2020; Koldewyn et al., 2010; Parron et al., 2008; Todorova et al., 2019). Specifically, a meta-analysis found that the effect size expanded nearly twice when the task required emotion recognition as compared to simple perception/detection (Todorova et al., 2019). However, for the high-functioning ASD individuals, it has been reported that they showed comparable performance with the control group in explicitly labeling BM emotions, while their responses were rather delayed (Mazzoni et al., 2022). This suggests that ASD individuals could adopt compensatory strategies to complete the explicit BM labeling task, while their automatic responses remained impaired. Such an observation highlights the importance of using more objective measures that do not rely on subjective reports to investigate the intrinsic perception of emotions from BM and its relationship with ASD-related social deficits. The current study thus introduced pupil size measurement to this field, and we combined it with the passive viewing task to investigate the more automatic aspect of BM emotion processing. In addition to diagnostic ASDs, the non-clinical general population also manifested autistic tendencies that followed normal distribution and demonstrated substantial heritability (Hoekstra et al., 2007). Here, we focused on the autistic tendencies in the general population, and our results showed that the automatic emotion processing of BM stimuli was impaired in individuals with high autistic tendencies, lending support to previous studies (Hubert et al., 2007; Nackaerts et al., 2012; Parron et al., 2008). The more detailed test–retest examination further confirmed such a correlation and illustrated a general diminishment in BM emotion perception ability (happy and sad) for high AQ individuals. Given that pupil measurement does not require any explicit verbal reports, it is easily attainable in children and even infants. Thus, the pupil modulation effect obtained here may serve as a sensitive and reliable physiological marker for detecting early social cognitive disorders in both clinical and non-clinical populations.

Remarkably, our findings highlighted the central role of local motion feature in modulating pupil responses toward emotional information contained in BM. Previous studies have shown that human visual system is highly sensitive to the local BM stimulus, whose global configural information is completely deprived through spatially scrambling (Troje and Westhoff, 2006). For example, it has been demonstrated that scrambled BM could perform as intact BM in lengthening subjective temporal perception (Wang and Jiang, 2012). Besides, such local motion feature also served as a basic pre-attentive feature in visual search (Wang et al., 2010) and could further induce a significant reflexive attentional effect (Wang et al., 2014; Yu et al., 2020). Moreover, the deprivation of the local BM feature would greatly disrupt the perception of the contained biologically salient information, such as animacy and walking direction (Chang and Troje, 2008; Chang and Troje, 2009; Yu et al., 2020). Here, we extended this line of research to the emotional domain by demonstrating that the local BM component is also critical for the processing of emotional information: when the local BM feature is disrupted, the modulation of emotions on pupil size completely disappears. Furthermore, the emotional BM that retained only local motion features could still exert a salient modulation effect on pupil size. In particular, the happy and sad local BM induced a significant pupil dilation effect as compared to the neutral local BM stimuli. Intriguingly, this dilation effect is independent of the specific emotion category but reflects a general activation caused by the affective salient information. In addition, this non-selective pupil dilation effect in local BM appeared in a relatively early time window, indicating that the extraction of emotions from local BM is rapid. Taken together, these findings identified a coarse but rapid emotion processing mechanism in local BM that could promptly detect the emotional information therein without the aid of the global shape.

Moreover, our findings revealed the existence of distinct emotional modulations in intact and local BM, respectively, as evidenced by variations in pupil responses. In particular, the happy intact BM induced a significant pupil dilation effect as compared to the neutral one, whereas the sad intact BM led to pupil constriction. Interestingly, both the happy and sad local BM resulted in pupil dilation effects relative to the neutral local BM, and such effect occurred at a relatively early time point. This distinctive pupil modulation effects observed in intact and local BM could be elucidated by a multi-level emotion processing mechanism. Specifically, even though both the intact and local BM conveyed important life information, the local BM is deprived of the global configural information (Chang and Troje, 2008; Chang and Troje, 2009; Simion et al., 2008). Thus, its emotion processing potentially occurred at a more basic and preliminary level, responding to the general affective salient information without engaging in further detailed analysis. Importantly, similar dissociated emotion processing phenomenon has been observed in another important type of emotional signal with analogous function (i.e., facial expression). For example, happy and fearful faces induced differential amygdala activations when perceived consciously, yet they induced comparable amygdala activations when suppressed (Williams et al., 2004). Moreover, it has been argued that there exist two parallel pathways for facial expression processing: a slow route that conveys fine-grained facial features along cortical areas to the amygdala, and a rapid subcortical pathway that directly transfers emotional information to amygdala without detailed analysis, which is known as the ‘quick and dirty’ route (Garrido et al., 2012; Johnson, 2005; Méndez-Bértolo et al., 2016; Vuilleumier et al., 2003). It is probable that the emotion processing of the local and intact point-light BM potentially functions in a manner similar to that of the faces, with the former serving as a primary detection mechanism that automatically captures emotionally significant information conveyed by animate agents without detailed analysis, and the latter aiding in more specific emotion identification based on fine-grained analyses of their motion pattern and global shape. Compatible with this view, recent studies have reported that the emotion processing of face and BM was very similar. For example, they both showed a happiness superiority in visual detection (Becker et al., 2011; Lee and Kim, 2017; Nackaerts et al., 2012) and guiding social attention (Yuan et al., 2023). Moreover, their emotion processing involved potentially overlapping brain regions, such as amygdala, superior temporal sulcus (STS) (Alaerts et al., 2014; Engell and Haxby, 2007; Peelen et al., 2007; Pessoa and Adolphs, 2010). Overall, our findings, together with the former evidence, suggest Video 1 dissociated emotion processing for BM in the human brain akin to the dual-route model reported in faces, and further imply a general ‘emotional brain’ that can be shared by different types of social signals. Still, future studies are needed to implement neuroimaging techniques to directly identify the brain regions involved in the emotion processing of local and global BM signals.

Video 1

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To conclude, the current study clearly demonstrated that the emotional information conveyed by point-light BM-modulated pupil responses. The intact BM exerted a fine-grained emotional modulation on pupil sizes, while disrupting the contained local motion characteristic would deteriorate the observed modulations. Moreover, BM with only local motion features retained could exert a fast but rather coarse modulation on pupillometry. These findings together highlight the critical role of local motion feature in BM emotion processing, and further reveal the multi-level emotion processing of BM. Notably, the observed emotional modulation in intact BM is associated with individual autistic traits, suggesting the potential of utilizing the emotion-modulated pupil responses to facilitate the diagnosis of social cognitive disorders.

All data, materials, and analysis code used in the current study could be accessed at Science Data Bank (https://doi.org/10.57760/sciencedb.psych.00125).

1. 1. Yuan T
   2. Li Wang
   3. Jiang Y

   (2024) Science Data Bank

   Data from: Multi-level processing of emotions in life motion signals revealed through pupil responses.

   https://doi.org/10.57760/sciencedb.psych.00125

1. 1. Actis-Grosso R
   2. Bossi F
   3. Ricciardelli P

   (2015) Emotion recognition through static faces and moving bodies: a comparison between typically developed adults and individuals with high level of autistic traits

   Frontiers in Psychology 6:1570.

   https://doi.org/10.3389/fpsyg.2015.01570

   • PubMed
   • Google Scholar
2. 1. Aktar E
   2. Mandell DJ
   3. de Vente W
   4. Majdandžić M
   5. Oort FJ
   6. van Renswoude DR
   7. Raijmakers MEJ
   8. Bögels SM

   (2018) Parental negative emotions are related to behavioral and pupillary correlates of infants’ attention to facial expressions of emotion

   Infant Behavior & Development 53:101–111.

   https://doi.org/10.1016/j.infbeh.2018.07.004

   • PubMed
   • Google Scholar
3. 1. Alaerts K
   2. Nackaerts E
   3. Meyns P
   4. Swinnen SP
   5. Wenderoth N

   (2011) Action and emotion recognition from point light displays: an investigation of gender differences

   PLOS ONE 6:e20989.

   https://doi.org/10.1371/journal.pone.0020989

   • PubMed
   • Google Scholar
4. 1. Alaerts Kaat
   2. Woolley DG
   3. Steyaert J
   4. Di Martino A
   5. Swinnen SP
   6. Wenderoth N

   (2014) Underconnectivity of the superior temporal sulcus predicts emotion recognition deficits in autism

   Social Cognitive and Affective Neuroscience 9:1589–1600.

   https://doi.org/10.1093/scan/nst156

   • PubMed
   • Google Scholar
5. 1. Barliya A
   2. Omlor L
   3. Giese MA
   4. Berthoz A
   5. Flash T

   (2013) Expression of emotion in the kinematics of locomotion

   Experimental Brain Research 225:159–176.

   https://doi.org/10.1007/s00221-012-3357-4

   • PubMed
   • Google Scholar
6. 1. Baron-Cohen S
   2. Wheelwright S
   3. Skinner R
   4. Martin J
   5. Clubley E

   (2001) The autism-spectrum quotient (AQ): evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians

   Journal of Autism and Developmental Disorders 31:5–17.

   https://doi.org/10.1023/a:1005653411471

   • PubMed
   • Google Scholar
7. 1. Baumeler D
   2. Schönhammer JG
   3. Born S

   (2020) Microsaccade dynamics in the attentional repulsion effect

   Vision Research 170:46–52.

   https://doi.org/10.1016/j.visres.2020.03.009

   • PubMed
   • Google Scholar
8. 1. Becker DV
   2. Anderson US
   3. Mortensen CR
   4. Neufeld SL
   5. Neel R

   (2011) The face in the crowd effect unconfounded: happy faces, not angry faces, are more efficiently detected in single- and multiple-target visual search tasks

   Journal of Experimental Psychology. General 140:637–659.

   https://doi.org/10.1037/a0024060

   • PubMed
   • Google Scholar
9. 1. Blake R
   2. Turner LM
   3. Smoski MJ
   4. Pozdol SL
   5. Stone WL

   (2003) Visual recognition of biological motion is impaired in children with autism

   Psychological Science 14:151–157.

   https://doi.org/10.1111/1467-9280.01434

   • PubMed
   • Google Scholar
10. 1. Bradley MM
    2. Miccoli L
    3. Escrig MA
    4. Lang PJ

    (2008) The pupil as a measure of emotional arousal and autonomic activation

    Psychophysiology 45:602–607.

    https://doi.org/10.1111/j.1469-8986.2008.00654.x

    • PubMed
    • Google Scholar
11. 1. Bradley MM
    2. Lang PJ

    (2015) Memory, emotion, and pupil diameter: Repetition of natural scenes

    Psychophysiology 52:1186–1193.

    https://doi.org/10.1111/psyp.12442

    • PubMed
    • Google Scholar
12. 1. Brainard DH

    (1997)

    The Psychophysics Toolbox

    Spatial Vision 10:433–436.

    • PubMed
    • Google Scholar
13. 1. Burley DT
    2. Gray NS
    3. Snowden RJ

    (2017) As far as the eye can see: relationship between psychopathic traits and pupil response to affective stimuli

    PLOS ONE 12:e0167436.

    https://doi.org/10.1371/journal.pone.0167436

    • PubMed
    • Google Scholar
14. 1. Burley DT
    2. Daughters K

    (2020) The effect of oxytocin on pupil response to naturalistic dynamic facial expressions

    Hormones and Behavior 125:104837.

    https://doi.org/10.1016/j.yhbeh.2020.104837

    • PubMed
    • Google Scholar
15. 1. Carsten T
    2. Desmet C
    3. Krebs RM
    4. Brass M

    (2019) Pupillary contagion is independent of the emotional expression of the face

    Emotion 19:1343–1352.

    https://doi.org/10.1037/emo0000503

    • PubMed
    • Google Scholar
16. 1. Chang DHF
    2. Troje NF

    (2008) Perception of animacy and direction from local biological motion signals

    Journal of Vision 8:3.

    https://doi.org/10.1167/8.5.3

    • PubMed
    • Google Scholar
17. 1. Chang DHF
    2. Troje NF

    (2009) Acceleration carries the local inversion effect in biological motion perception

    Journal of Vision 9:19.

    https://doi.org/10.1167/9.1.19

    • PubMed
    • Google Scholar
18. 1. Chouchourelou A
    2. Matsuka T
    3. Harber K
    4. Shiffrar M

    (2006) The visual analysis of emotional actions

    Social Neuroscience 1:63–74.

    https://doi.org/10.1080/17470910600630599

    • PubMed
    • Google Scholar
19. 1. Critchley H

    (2000) Explicit and implicit neural mechanisms for processing of social information from facial expressions: a functional magnetic resonance imaging study

    Human Brain Mapping 9:93–105.

    https://doi.org/10.1002/(SICI)1097-0193(200002)9:2<93::AID-HBM4>3.0.CO;2-Z

    • Google Scholar
20. 1. Dal Monte O
    2. Costa VD
    3. Noble PL
    4. Murray EA
    5. Averbeck BB

    (2015) Amygdala lesions in rhesus macaques decrease attention to threat

    Nature Communications 6:10161.

    https://doi.org/10.1038/ncomms10161

    • Google Scholar
21. 1. de Gee JW
    2. Knapen T
    3. Donner TH

    (2014) Decision-related pupil dilation reflects upcoming choice and individual bias

    PNAS 111:E618–E625.

    https://doi.org/10.1073/pnas.1317557111

    • Google Scholar
22. 1. de Gelder B

    (2006) Towards the neurobiology of emotional body language

    Nature Reviews. Neuroscience 7:242–249.

    https://doi.org/10.1038/nrn1872

    • PubMed
    • Google Scholar
23. 1. Einhäuser W
    2. Stout J
    3. Koch C
    4. Carter O

    (2008) Pupil dilation reflects perceptual selection and predicts subsequent stability in perceptual rivalry

    PNAS 105:1704–1709.

    https://doi.org/10.1073/pnas.0707727105

    • Google Scholar
24. 1. Engbert R
    2. Kliegl R

    (2003) Microsaccades uncover the orientation of covert attention

    Vision Research 43:1035–1045.

    https://doi.org/10.1016/s0042-6989(03)00084-1

    • PubMed
    • Google Scholar
25. 1. Engell AD
    2. Haxby JV

    (2007) Facial expression and gaze-direction in human superior temporal sulcus

    Neuropsychologia 45:3234–3241.

    https://doi.org/10.1016/j.neuropsychologia.2007.06.022

    • PubMed
    • Google Scholar
26. 1. Faul F
    2. Erdfelder E
    3. Lang AG
    4. Buchner A

    (2007) G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences

    Behavior Research Methods 39:175–191.

    https://doi.org/10.3758/bf03193146

    • PubMed
    • Google Scholar
27. 1. Federici A
    2. Parma V
    3. Vicovaro M
    4. Radassao L
    5. Casartelli L
    6. Ronconi L

    (2020) Anomalous perception of biological motion in autism: a conceptual review and meta-analysis

    Scientific Reports 10:61252-3.

    https://doi.org/10.1038/s41598-020-61252-3

    • Google Scholar
28. 1. Freitag CM
    2. Konrad C
    3. Häberlen M
    4. Kleser C
    5. von Gontard A
    6. Reith W
    7. Troje NF
    8. Krick C

    (2008) Perception of biological motion in autism spectrum disorders

    Neuropsychologia 46:1480–1494.

    https://doi.org/10.1016/j.neuropsychologia.2007.12.025

    • PubMed
    • Google Scholar
29. 1. Frith U

    (2004) Emanuel Miller lecture: confusions and controversies about Asperger syndrome

    Journal of Child Psychology and Psychiatry, and Allied Disciplines 45:672–686.

    https://doi.org/10.1111/j.1469-7610.2004.00262.x

    • PubMed
    • Google Scholar
30. 1. Frith C

    (2009) Role of facial expressions in social interactions

    Philosophical Transactions of the Royal Society B 364:3453–3458.

    https://doi.org/10.1098/rstb.2009.0142

    • Google Scholar
31. 1. Garrido MI
    2. Barnes GR
    3. Sahani M
    4. Dolan RJ

    (2012) Functional evidence for a dual route to amygdala

    Current Biology 22:129–134.

    https://doi.org/10.1016/j.cub.2011.11.056

    • PubMed
    • Google Scholar
32. 1. Graves JE
    2. Egré P
    3. Pressnitzer D
    4. de Gardelle V

    (2021) An implicit representation of stimulus ambiguity in pupil size

    PNAS 118:e2107997118.

    https://doi.org/10.1073/pnas.2107997118

    • Google Scholar
33. 1. Hafed ZM
    2. Goffart L
    3. Krauzlis RJ

    (2009) A neural mechanism for microsaccade generation in the primate superior colliculus

    Science 323:940–943.

    https://doi.org/10.1126/science.1166112

    • PubMed
    • Google Scholar
34. 1. Hafed ZM
    2. Krauzlis RJ

    (2012) Similarity of superior colliculus involvement in microsaccade and saccade generation

    Journal of Neurophysiology 107:1904–1916.

    https://doi.org/10.1152/jn.01125.2011

    • Google Scholar
35. 1. Halovic S
    2. Kroos C

    (2018) Not all is noticed: Kinematic cues of emotion-specific gait

    Human Movement Science 57:478–488.

    https://doi.org/10.1016/j.humov.2017.11.008

    • Google Scholar
36. 1. Harms MB
    2. Martin A
    3. Wallace GL

    (2010) Facial emotion recognition in autism spectrum disorders: a review of behavioral and neuroimaging studies

    Neuropsychology Review 20:290–322.

    https://doi.org/10.1007/s11065-010-9138-6

    • PubMed
    • Google Scholar
37. 1. Harrison NA
    2. Singer T
    3. Rotshtein P
    4. Dolan RJ
    5. Critchley HD

    (2006) Pupillary contagion: central mechanisms engaged in sadness processing

    Social Cognitive and Affective Neuroscience 1:5–17.

    https://doi.org/10.1093/scan/nsl006

    • PubMed
    • Google Scholar
38. 1. Hoekstra RA
    2. Bartels M
    3. Verweij CJH
    4. Boomsma DI

    (2007) Heritability of autistic traits in the general population

    Archives of Pediatrics & Adolescent Medicine 161:372.

    https://doi.org/10.1001/archpedi.161.4.372

    • Google Scholar
39. 1. Hubert B
    2. Wicker B
    3. Moore DG
    4. Monfardini E
    5. Duverger H
    6. Da Fonséca D
    7. Deruelle C

    (2007) Brief report: recognition of emotional and non-emotional biological motion in individuals with autistic spectrum disorders

    Journal of Autism and Developmental Disorders 37:1386–1392.

    https://doi.org/10.1007/s10803-006-0275-y

    • PubMed
    • Google Scholar
40. 1. Jessen S
    2. Altvater-Mackensen N
    3. Grossmann T

    (2016) Pupillary responses reveal infants’ discrimination of facial emotions independent of conscious perception

    Cognition 150:163–169.

    https://doi.org/10.1016/j.cognition.2016.02.010

    • PubMed
    • Google Scholar
41. 1. Johansson G

    (1973) Visual perception of biological motion and a model for its analysis

    Perception & Psychophysics 14:201–211.

    https://doi.org/10.3758/BF03212378

    • Google Scholar
42. 1. Johnson MH

    (2005) Subcortical face processing

    Nature Reviews. Neuroscience 6:766–774.

    https://doi.org/10.1038/nrn1766

    • PubMed
    • Google Scholar
43. 1. Joshi S
    2. Gold JI

    (2020) Pupil size as a window on neural substrates of cognition

    Trends in Cognitive Sciences 24:466–480.

    https://doi.org/10.1016/j.tics.2020.03.005

    • Google Scholar
44. 1. Kana RK
    2. Patriquin MA
    3. Black BS
    4. Channell MM
    5. Wicker B

    (2016) Altered medial frontal and superior temporal response to implicit processing of emotions in autism

    Autism Research 9:55–66.

    https://doi.org/10.1002/aur.1496

    • PubMed
    • Google Scholar
45. 1. Kazak AE

    (2018) Editorial: journal article reporting standards

    The American Psychologist 73:1–2.

    https://doi.org/10.1037/amp0000263

    • PubMed
    • Google Scholar
46. 1. Keifer CM
    2. Mikami AY
    3. Morris JP
    4. Libsack EJ
    5. Lerner MD

    (2020) Prediction of social behavior in autism spectrum disorders: Explicit versus implicit social cognition

    Autism 24:1758–1772.

    https://doi.org/10.1177/1362361320922058

    • PubMed
    • Google Scholar
47. 1. Klin A
    2. Lin DJ
    3. Gorrindo P
    4. Ramsay G
    5. Jones W

    (2009) Two-year-olds with autism orient to non-social contingencies rather than biological motion

    Nature 459:257–261.

    https://doi.org/10.1038/nature07868

    • PubMed
    • Google Scholar
48. 1. Kloosterman NA
    2. Meindertsma T
    3. van Loon AM
    4. Lamme VAF
    5. Bonneh YS
    6. Donner TH

    (2015) Pupil size tracks perceptual content and surprise

    The European Journal of Neuroscience 41:1068–1078.

    https://doi.org/10.1111/ejn.12859

    • PubMed
    • Google Scholar
49. 1. Koldewyn K
    2. Whitney D
    3. Rivera SM

    (2010) The psychophysics of visual motion and global form processing in autism

    Brain 133:599–610.

    https://doi.org/10.1093/brain/awp272

    • PubMed
    • Google Scholar
50. 1. Kovarski K
    2. Mennella R
    3. Wong SM
    4. Dunkley BT
    5. Taylor MJ
    6. Batty M

    (2019) Enhanced early visual responses during implicit emotional faces processing in autism spectrum disorder

    Journal of Autism and Developmental Disorders 49:871–886.

    https://doi.org/10.1007/s10803-018-3787-3

    • Google Scholar
51. 1. Lange K
    2. Williams LM
    3. Young AW
    4. Bullmore ET
    5. Brammer MJ
    6. Williams SCR
    7. Gray JA
    8. Phillips ML

    (2003) Task instructions modulate neural responses to fearful facial expressions

    Biological Psychiatry 53:226–232.

    https://doi.org/10.1016/s0006-3223(02)01455-5

    • PubMed
    • Google Scholar
52. 1. Lee H
    2. Kim J

    (2017) Facilitating effects of emotion on the perception of biological motion: evidence for a happiness superiority effect

    Perception 46:679–697.

    https://doi.org/10.1177/0301006616681809

    • PubMed
    • Google Scholar
53. 1. Liddell BJ
    2. Brown KJ
    3. Kemp AH
    4. Barton MJ
    5. Das P
    6. Peduto A
    7. Gordon E
    8. Williams LM

    (2005) A direct brainstem-amygdala-cortical “alarm” system for subliminal signals of fear

    NeuroImage 24:235–243.

    https://doi.org/10.1016/j.neuroimage.2004.08.016

    • PubMed
    • Google Scholar
54. 1. Mazzoni N
    2. Ricciardelli P
    3. Actis-Grosso R
    4. Venuti P

    (2022) Difficulties in recognising dynamic but not static emotional body movements in autism spectrum disorder

    Journal of Autism and Developmental Disorders 52:1092–1105.

    https://doi.org/10.1007/s10803-021-05015-7

    • PubMed
    • Google Scholar
55. 1. Méndez-Bértolo C
    2. Moratti S
    3. Toledano R
    4. Lopez-Sosa F
    5. Martínez-Alvarez R
    6. Mah YH
    7. Vuilleumier P
    8. Gil-Nagel A
    9. Strange BA

    (2016) A fast pathway for fear in human amygdala

    Nature Neuroscience 19:1041–1049.

    https://doi.org/10.1038/nn.4324

    • PubMed
    • Google Scholar
56. 1. Meyberg S
    2. Sinn P
    3. Engbert R
    4. Sommer W

    (2017) Revising the link between microsaccades and the spatial cueing of voluntary attention

    Vision Research 133:47–60.

    https://doi.org/10.1016/j.visres.2017.01.001

    • PubMed
    • Google Scholar
57. 1. Nackaerts E
    2. Wagemans J
    3. Helsen W
    4. Swinnen SP
    5. Wenderoth N
    6. Alaerts K

    (2012) Recognizing biological motion and emotions from point-light displays in autism spectrum disorders

    PLOS ONE 7:e44473.

    https://doi.org/10.1371/journal.pone.0044473

    • PubMed
    • Google Scholar
58. 1. Nakakoga S
    2. Higashi H
    3. Muramatsu J
    4. Nakauchi S
    5. Minami T

    (2020) Asymmetrical characteristics of emotional responses to pictures and sounds: Evidence from pupillometry

    PLOS ONE 15:e0230775.

    https://doi.org/10.1371/journal.pone.0230775

    • PubMed
    • Google Scholar
59. 1. Ogren M
    2. Kaplan B
    3. Peng Y
    4. Johnson KL
    5. Johnson SP

    (2019) Motion or emotion: infants discriminate emotional biological motion based on low-level visual information

    Infant Behavior & Development 57:101324.

    https://doi.org/10.1016/j.infbeh.2019.04.006

    • PubMed
    • Google Scholar
60. 1. Okon-Singer H
    2. Lichtenstein-Vidne L
    3. Cohen N

    (2013) Dynamic modulation of emotional processing

    Biological Psychology 92:480–491.

    https://doi.org/10.1016/j.biopsycho.2012.05.010

    • PubMed
    • Google Scholar
61. 1. Oliva M
    2. Anikin A

    (2018) Pupil dilation reflects the time course of emotion recognition in human vocalizations

    Scientific Reports 8:4871.

    https://doi.org/10.1038/s41598-018-23265-x

    • PubMed
    • Google Scholar
62. 1. Parron C
    2. Da Fonseca D
    3. Santos A
    4. Moore DG
    5. Monfardini E
    6. Deruelle C

    (2008) Recognition of biological motion in children with autistic spectrum disorders

    Autism 12:261–274.

    https://doi.org/10.1177/1362361307089520

    • PubMed
    • Google Scholar
63. 1. Peelen MV
    2. Atkinson AP
    3. Andersson F
    4. Vuilleumier P

    (2007) Emotional modulation of body-selective visual areas

    Social Cognitive and Affective Neuroscience 2:274–283.

    https://doi.org/10.1093/scan/nsm023

    • PubMed
    • Google Scholar
64. Book

    1. Pelli D

    (1997) The videotoolbox software for visual psychophysics: transforming numbers into movies

    Spatial Vision.

    https://doi.org/10.1163/156856897X00366

    • Google Scholar
65. 1. Pessoa L
    2. Adolphs R

    (2010) Emotion processing and the amygdala: from a “low road” to “many roads” of evaluating biological significance

    Nature Reviews. Neuroscience 11:773–783.

    https://doi.org/10.1038/nrn2920

    • PubMed
    • Google Scholar
66. 1. Pomè A
    2. Binda P
    3. Cicchini GM
    4. Burr DC

    (2020) Pupillometry correlates of visual priming, and their dependency on autistic traits

    Journal of Vision 20:3.

    https://doi.org/10.1167/jovi.20.3.3

    • PubMed
    • Google Scholar
67. 1. Prunty JE
    2. Keemink JR
    3. Kelly DJ

    (2022) Infants show pupil dilatory responses to happy and angry facial expressions

    Developmental Science 25:e13182.

    https://doi.org/10.1111/desc.13182

    • PubMed
    • Google Scholar
68. 1. Roether CL
    2. Omlor L
    3. Christensen A
    4. Giese MA

    (2009) Critical features for the perception of emotion from gait

    Journal of Vision 9:15.

    https://doi.org/10.1167/9.6.15

    • PubMed
    • Google Scholar
69. 1. Ross P
    2. de Gelder B
    3. Crabbe F
    4. Grosbras M-H

    (2019) Emotion modulation of the body-selective areas in the developing brain

    Developmental Cognitive Neuroscience 38:100660.

    https://doi.org/10.1016/j.dcn.2019.100660

    • Google Scholar
70. 1. Shafer AT
    2. Matveychuk D
    3. Penney T
    4. O’Hare AJ
    5. Stokes J
    6. Dolcos F

    (2012) Processing of emotional distraction is both automatic and modulated by attention: evidence from an event-related fMRI investigation

    Journal of Cognitive Neuroscience 24:1233–1252.

    https://doi.org/10.1162/jocn_a_00206

    • PubMed
    • Google Scholar
71. 1. Simion F
    2. Regolin L
    3. Bulf H

    (2008) A predisposition for biological motion in the newborn baby

    PNAS 105:809–813.

    https://doi.org/10.1073/pnas.0707021105

    • Google Scholar
72. 1. Snowden RJ
    2. O’Farrell KR
    3. Burley D
    4. Erichsen JT
    5. Newton NV
    6. Gray NS

    (2016) The pupil’s response to affective pictures: Role of image duration, habituation, and viewing mode

    Psychophysiology 53:1217–1223.

    https://doi.org/10.1111/psyp.12668

    • PubMed
    • Google Scholar
73. 1. Spencer JMY
    2. Sekuler AB
    3. Bennett PJ
    4. Giese MA
    5. Pilz KS

    (2016) Effects of aging on identifying emotions conveyed by point-light walkers

    Psychology and Aging 31:126–138.

    https://doi.org/10.1037/a0040009

    • PubMed
    • Google Scholar
74. 1. Todorova GK
    2. Hatton REM
    3. Pollick FE

    (2019) Biological motion perception in autism spectrum disorder: a meta-analysis

    Molecular Autism 10:49.

    https://doi.org/10.1186/s13229-019-0299-8

    • PubMed
    • Google Scholar
75. 1. Troje NF
    2. Westhoff C

    (2006) The inversion effect in biological motion perception: evidence for a “life detector”?

    Current Biology 16:821–824.

    https://doi.org/10.1016/j.cub.2006.03.022

    • PubMed
    • Google Scholar
76. Book

    1. Troje NF

    (2008)

    Retrieving information from human movement patterns

    Oxford University Press.

    • Google Scholar
77. 1. Turi M
    2. Burr DC
    3. Binda P

    (2018) Pupillometry reveals perceptual differences that are tightly linked to autistic traits in typical adults

    eLife 7:e32399.

    https://doi.org/10.7554/eLife.32399

    • PubMed
    • Google Scholar
78. 1. van der Wel P
    2. van Steenbergen H

    (2018) Pupil dilation as an index of effort in cognitive control tasks: A review

    Psychonomic Bulletin & Review 25:2005–2015.

    https://doi.org/10.3758/s13423-018-1432-y

    • Google Scholar
79. 1. Vuilleumier P
    2. Armony JL
    3. Driver J
    4. Dolan RJ

    (2003) Distinct spatial frequency sensitivities for processing faces and emotional expressions

    Nature Neuroscience 6:624–631.

    https://doi.org/10.1038/nn1057

    • PubMed
    • Google Scholar
80. 1. Wang L
    2. Zhang K
    3. He S
    4. Jiang Y

    (2010) Searching for life motion signals

    Psychological Science 21:1083–1089.

    https://doi.org/10.1177/0956797610376072

    • Google Scholar
81. 1. Wang CA
    2. Boehnke SE
    3. White BJ
    4. Munoz DP

    (2012) Microstimulation of the monkey superior colliculus induces pupil dilation without evoking saccades

    The Journal of Neuroscience 32:3629–3636.

    https://doi.org/10.1523/JNEUROSCI.5512-11.2012

    • PubMed
    • Google Scholar
82. 1. Wang L
    2. Jiang Y

    (2012) Life motion signals lengthen perceived temporal duration

    PNAS 109:E673–E677.

    https://doi.org/10.1073/pnas.1115515109

    • PubMed
    • Google Scholar
83. 1. Wang L
    2. Yang X
    3. Shi J
    4. Jiang Y

    (2014) The feet have it: local biological motion cues trigger reflexive attentional orienting in the brain

    NeuroImage 84:217–224.

    https://doi.org/10.1016/j.neuroimage.2013.08.041

    • PubMed
    • Google Scholar
84. 1. Williams MA
    2. Morris AP
    3. McGlone F
    4. Abbott DF
    5. Mattingley JB

    (2004) Amygdala responses to fearful and happy facial expressions under conditions of binocular suppression

    The Journal of Neuroscience 24:2898–2904.

    https://doi.org/10.1523/JNEUROSCI.4977-03.2004

    • PubMed
    • Google Scholar
85. 1. Wong TKW
    2. Fung PCW
    3. Chua SE
    4. McAlonan GM

    (2008) Abnormal spatiotemporal processing of emotional facial expressions in childhood autism: dipole source analysis of event-related potentials

    The European Journal of Neuroscience 28:407–416.

    https://doi.org/10.1111/j.1460-9568.2008.06328.x

    • PubMed
    • Google Scholar
86. 1. Yu Y
    2. Ji H
    3. Wang L
    4. Jiang Y

    (2020) Cross-modal social attention triggered by biological motion cues

    Journal of Vision 20:21.

    https://doi.org/10.1167/jov.20.10.21

    • PubMed
    • Google Scholar
87. 1. Yuan T
    2. Ji H
    3. Wang L
    4. Jiang Y

    (2023) Happy is stronger than sad: Emotional information modulates social attention

    Emotion 23:1061–1074.

    https://doi.org/10.1037/emo0001145

    • PubMed
    • Google Scholar
88. 1. Yuan T
    2. Wang L
    3. Jiang Y

    (2024) Cross-channel adaptation reveals shared emotion representation from face and biological motion

    Emotion 1:emo0001409.

    https://doi.org/10.1037/emo0001409

    • PubMed
    • Google Scholar

Author details

1. Tian Yuan

   1. State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
   2. Department of Psychology, University of Chinese Academy of Sciences, Beijing, China

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8570-1484

2. Li Wang

   1. State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
   2. Department of Psychology, University of Chinese Academy of Sciences, Beijing, China

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Methodology, Writing - review and editing

   For correspondence

   wangli@psych.ac.cn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2204-5192

3. Yi Jiang

   1. State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
   2. Department of Psychology, University of Chinese Academy of Sciences, Beijing, China

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5746-7301

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