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Chris Retzler, Udo Boehm, Jing Cai, Aimee Cochrane, Catherine Manning (2020). Prior information use and response caution in perceptual decision-making: No evidence for a relationship with autistic-like traits. Center for Open Science.

Date de publication
04/11/2020
Identifiant
10.31234/osf.io/vc49h
Auteurs
Chris Retzler, Udo Boehm, Jing Cai, Aimee Cochrane, Catherine Manning
Type de référence
preprint
Éditeur
Center for Open Science
Source de métadonnées
crossref

Résumé

Interpreting the world around us requires integrating incoming sensory signals with prior information. Autistic individuals have been proposed to rely less on prior information and make more cautious responses than non-autistic individuals. Here we investigated whether these purported features of autistic perception vary as a function of autistic-like traits in the general population. We used a diffusion model framework, whereby decisions are modelled as noisy evidence accumulation processes towards one of two bounds. Within this framework, prior information can bias the starting point of the evidence accumulation process. Our pre-registered hypotheses were that higher autistic-like traits would relate to reduced starting point bias caused by prior information and increased response caution (wider boundary separation). 222 participants discriminated the direction of coherent motion stimuli as quickly and accurately as possible. Stimuli were preceded with a neutral cue (square) or a directional cue (arrow). 80% of the directional cues validly predicted the upcoming motion direction. We modelled accuracy and response time data using a hierarchical Bayesian model in which starting point varied with cue condition. We found no evidence for our hypotheses, with starting point bias and response caution seemingly unrelated to AQ scores. Alongside future research applying this paradigm to autistic individuals, our findings will help refine theories regarding the role of prior information and altered decision-making strategies in autistic perception. Our study also has implications for models of bias in perceptual decision-making, as the most plausible model was one that incorporated bias in both decision-making and sensory processing.

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