Improved emotion differentiation under reduced acoustic variability of speech in autism

Abstract

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.

Keywords: Autism; Electroencephalography; Emotion; Naturalness; Pediatrics; Prosody; Sensory variability; Speech; Voice.

Fig. 1

Precision-weighting for perception inferences during emotional speech. A Within highly unstable sensory environments, higher precision may be ascribed to socio-emotional priors while a coarse decoding of the sensory evidence may optimize the perception (inferred estimate) of emotional prosodies. B Autistic mechanisms of perception may overestimate the environmental variability (incoming evidence), and fine-grained bottom-up inputs would be misinterpreted as predictions, leading to blurry prosodic contours differentiation (inferred estimates). C The reduction of voice’s variability creates more stable and unknown environments that would promote the precision towards the incoming evidence by neurotypicals without jeopardizing the differentiation of emotional prosodies. D We hypothesize that more reliable sensory environments may favor the differentiation of emotional prosodies by autistics

Fig. 2

Children’s demographic and clinical characteristics. Autistic symptomatology measured by the A Autism Spectrum Rating Scales (ASRS, parents’ version), and the B Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) at the time of the experiment; C gender and age; and D handedness. M1 module 1; M2 module 2; M3 module 3. **: p-value < 0.01; ***: p-value < 0.001; ASD autistic; TD typically developed. Note that ASD and TD children differed as regards gender and age: see Additional file 1 and section Sensitivity analysis: age- and gender-controlled sample

Fig. 3

Performance on MOT (m) of TD and ASD children while listening to human, level 1, and level 2 voices (emotional prosodies). Error bars represent the standard errors of means (sample standard deviation divided by the square root of the number of samples)

Fig. 4

Processing of level 2 voices by ASD. A T-values for pairwise comparisons through time and frequencies, averaged over channels of significance (indicated by the legend “Significance”). The “happiness-sadness” comparison appears twice: the first (4th row from the top, last column from the left) provides values averaged over Cz, Pz, CPz, and Poz, and values were averaged over Fz and AFz, within the second plot (5th row from the top, 1st column). Clusters mentioned in Table 5 are outlined by white dotted rectangles. B ERSP (dB) are presented through time and frequencies, averaged over Cz, Pz, CPz, POz and Fz, and AFz where relevant (happiness and sadness)

Fig. 5

Processing of human, level 1, and level 2 voices by TD. A ERSP (dB) are presented through time and frequencies, averaged over Cz, Pz, CPz, and POz. B ERSP (dB) are presented through time and frequencies, averaged over Fz and AFz