Self-motion perception in autism is compromised by visual noise but integrated optimally across multiple senses

Abstract

Perceptual processing in autism spectrum disorder (ASD) is marked by superior low-level task performance and inferior complex-task performance. This observation has led to theories of defective integration in ASD of local parts into a global percept. Despite mixed experimental results, this notion maintains widespread influence and has also motivated recent theories of defective multisensory integration in ASD. Impaired ASD performance in tasks involving classic random dot visual motion stimuli, corrupted by noise as a means to manipulate task difficulty, is frequently interpreted to support this notion of global integration deficits. By manipulating task difficulty independently of visual stimulus noise, here we test the hypothesis that heightened sensitivity to noise, rather than integration deficits, may characterize ASD. We found that although perception of visual motion through a cloud of dots was unimpaired without noise, the addition of stimulus noise significantly affected adolescents with ASD, more than controls. Strikingly, individuals with ASD demonstrated intact multisensory (visual-vestibular) integration, even in the presence of noise. Additionally, when vestibular motion was paired with pure visual noise, individuals with ASD demonstrated a different strategy than controls, marked by reduced flexibility. This result could be simulated by using attenuated (less reliable) and inflexible (not experience-dependent) Bayesian priors in ASD. These findings question widespread theories of impaired global and multisensory integration in ASD. Rather, they implicate increased sensitivity to sensory noise and less use of prior knowledge in ASD, suggesting increased reliance on incoming sensory information.

Keywords: Bayesian; autism; coherence; multisensory integration; noise.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.

ASD example. Behavioral responses of an example participant with ASD are presented for three separate blocks, tested with 100% (A), 90% (B), and 50% (C) visual motion coherence. For each block, psychometric curves represent the ratio of rightward choices as a function of heading direction, based on visual-only (red), vestibular-only (blue), or combined visual–vestibular (green) cues. Data (circles) were fitted with cumulative Gaussian functions (solid lines).

Fig. 2.

Response to visual noise. (A) Filled and textured red bars represent the log-scale mean ± SEM visual psychometric thresholds for 100% and 90% motion coherence, respectively. These were similar for controls (left) but significantly different for participants with ASD (right). *P < 0.05. (B, Left) Red solid and dashed lines represent the log-scale mean visual thresholds, as a function of visual motion coherence for participants with ASD and controls, respectively. Error bars mark 1 SEM. (B, Right) Similarly, black solid and dashed lines represent the mean visual-to-vestibular threshold ratios for participants with ASD and controls, respectively. (C) Filled blue bars represent the log-scale mean ± SEM vestibular psychometric thresholds, which were similar for control and ASD participants. See also Figs. S1 and S2.

Fig. 3.

Multisensory integration. (A) Mean ± SEM (log-scale) psychometric thresholds are presented for visual (red), vestibular (blue), and combined visual–vestibular (green) cues as a function of visual coherence for controls (Upper) and participants with ASD (Lower). The light green dashed line represents predicted combined cue thresholds, based on the single (visual and vestibular) cues. Data for 0% coherence was pooled across all four blocks (the other data comprise one block per coherence). (B) A scatter plot of actual combined cue thresholds vs. predicted thresholds for controls (open circles) and participants with ASD (filled circles) is presented. Dashed and solid lines represent type-II regressions for controls and participants with ASD, respectively. Ideal performance (y = x) is marked by the diagonal dotted line.

Fig. 4.

Influence of complete visual noise. The noise effect (ratio of psychometric thresholds with/without the addition of 0% coherence visual noise) as a function of session repeats is presented for controls (open circles) and participants with ASD (filled circles). Linear regressions of the noise effect indicate habituation for controls (dashed line) but not for participants with ASD (solid line). The horizontal dotted line represents no noise effect (optimal noise filtering). See also Fig. S3.