Autistic adults exhibit highly precise representations of others' emotions but a reduced influence of emotion representations on emotion recognition accuracy

Predictive coding for the actions and emotions of others and its deficits in autism spectrum disorders

Traditionally, the neural basis of social perception has been studied by showing participants brief examples of the actions or emotions of others presented in randomized order to prevent participants from anticipating what others do and feel. This approach is optimal to isolate the importance of information flow from lower to higher cortical areas. The degree to which feedback connections and Bayesian hierarchical predictive coding contribute to how mammals process more complex social stimuli has been less explored, and will be the focus of this review. We illustrate paradigms that start to capture how participants predict the actions and emotions of others under more ecological conditions, and discuss the brain activity measurement methods suitable to reveal the importance of feedback connections in these predictions. Together, these efforts draw a richer picture of social cognition in which predictive coding and feedback connections play significant roles. We further discuss how the notion of predicting coding is influencing how we think of autism spectrum disorder.

Individual differences in interoception and autistic traits share altered facial emotion perception, but not recognition per se

While alterations in both physiological responses to others' emotions as well as interoceptive abilities have been identified in autism, their relevance in altered emotion recognition is largely unknown. We here examined the role of interoceptive ability, facial mimicry, and autistic traits in facial emotion processing in non-autistic individuals. In an online Experiment 1, participants (N = 99) performed a facial emotion recognition task, including ratings of perceived emotional intensity and confidence in emotion recognition, and reported on trait interoceptive accuracy, interoceptive sensibility and autistic traits. In a follow-up lab Experiment 2 involving 100 participants, we replicated the online experiment and additionally investigated the relationship between facial mimicry (measured through electromyography), cardiac interoceptive accuracy (evaluated using a heartbeat discrimination task), and autistic traits in relation to emotion processing. Across experiments, neither interoception measures nor facial mimicry accounted for a reduced recognition of specific expressions with higher autistic traits. Higher trait interoceptive accuracy was rather associated with more confidence in correct recognition of some expressions, as well as with higher ratings of their perceived emotional intensity. Exploratory analyses indicated that those higher intensity ratings might result from a stronger integration of instant facial muscle activations, which seem to be less integrated in intensity ratings with higher autistic traits. Future studies should test whether facial muscle activity, and physiological signals in general, are correspondingly less predictive of perceiving emotionality in others in individuals on the autism spectrum, and whether training interoceptive abilities might facilitate the interpretation of emotional expressions.

Expression perceptive fields explain individual differences in the recognition of facial emotions

Humans can use the facial expressions of another to infer their emotional state, although it remains unknown how this process occurs. Here we suppose the presence of perceptive fields within expression space, analogous to feature-tuned receptive-fields of early visual cortex. We developed genetic algorithms to explore a multidimensional space of possible expressions and identify those that individuals associated with different emotions. We next defined perceptive fields as probabilistic maps within expression space, and found that they could predict the emotions that individuals infer from expressions presented in a separate task. We found profound individual variability in their size, location, and specificity, and that individuals with more similar perceptive fields had similar interpretations of the emotion communicated by an expression, providing possible channels for social communication. Modelling perceptive fields therefore provides a predictive framework in which to understand how individuals infer emotions from facial expressions.

Improved emotion differentiation under reduced acoustic variability of speech in autism

BACKGROUND

Socio-emotional impairments are among the diagnostic criteria for autism spectrum disorder (ASD), but the actual knowledge has substantiated both altered and intact emotional prosodies recognition. Here, a Bayesian framework of perception is considered suggesting that the oversampling of sensory evidence would impair perception within highly variable environments. However, reliable hierarchical structures for spectral and temporal cues would foster emotion discrimination by autistics.

METHODS

Event-related spectral perturbations (ERSP) extracted from electroencephalographic (EEG) data indexed the perception of anger, disgust, fear, happiness, neutral, and sadness prosodies while listening to speech uttered by (a) human or (b) synthesized voices characterized by reduced volatility and variability of acoustic environments. The assessment of mechanisms for perception was extended to the visual domain by analyzing the behavioral accuracy within a non-social task in which dynamics of precision weighting between bottom-up evidence and top-down inferences were emphasized. Eighty children (mean 9.7 years old; standard deviation 1.8) volunteered including 40 autistics. The symptomatology was assessed at the time of the study via the Autism Diagnostic Observation Schedule, Second Edition, and parents' responses on the Autism Spectrum Rating Scales. A mixed within-between analysis of variance was conducted to assess the effects of group (autism versus typical development), voice, emotions, and interaction between factors. A Bayesian analysis was implemented to quantify the evidence in favor of the null hypothesis in case of non-significance. Post hoc comparisons were corrected for multiple testing.

RESULTS

Autistic children presented impaired emotion differentiation while listening to speech uttered by human voices, which was improved when the acoustic volatility and variability of voices were reduced. Divergent neural patterns were observed from neurotypicals to autistics, emphasizing different mechanisms for perception. Accordingly, behavioral measurements on the visual task were consistent with the over-precision ascribed to the environmental variability (sensory processing) that weakened performance. Unlike autistic children, neurotypicals could differentiate emotions induced by all voices.

CONCLUSIONS

This study outlines behavioral and neurophysiological mechanisms that underpin responses to sensory variability. Neurobiological insights into the processing of emotional prosodies emphasized the potential of acoustically modified emotional prosodies to improve emotion differentiation by autistics.

TRIAL REGISTRATION

BioMed Central ISRCTN Registry, ISRCTN18117434. Registered on September 20, 2020.

The inside out model of emotion recognition: how the shape of one's internal emotional landscape influences the recognition of others' emotions

Some people are exceptional at reading emotional expressions, while others struggle. Here we ask whether the way we experience emotion "on the inside" influences the way we expect emotions to be expressed in the "outside world" and subsequently our ability to read others' emotional expressions. Across multiple experiments, incorporating discovery and replication samples, we develop EmoMap (N = 20; N = 271) and ExpressionMap (N = 98; replication N = 193) to map adults' experiences of emotions and visual representations of others' emotions. Some individuals have modular maps, wherein emotional experiences and visual representations are consistent and distinct-anger looks and feels different from happiness, which looks and feels different from sadness. In contrast, others have experiences and representations that are variable and overlapping-anger, happiness, and sadness look and feel similar and are easily confused for one another. Here we illustrate an association between these maps: those with consistent and distinct experiences of emotion also have consistent and distinct visual representations of emotion. Finally (N = 193), we construct the Inside Out Model of Emotion Recognition, which explains 60.8% of the variance in emotion recognition and illuminates multiple pathways to emotion recognition difficulties. These findings have important implications for understanding the emotion recognition difficulties documented in numerous clinical populations.