Abstract:
Previously acquired knowledge (e.g., prior) plays a crucial role in perceptual decision making. When estimating quantities, an integration of sensory estimates with acqui...Show MoreMetadata
Abstract:
Previously acquired knowledge (e.g., prior) plays a crucial role in perceptual decision making. When estimating quantities, an integration of sensory estimates with acquired internal representations of quantities of similar nature may occur, leading to a bias toward the mean of these quantities. In autism spectrum disorders (ASD), sensory and perceptual atypicalities are observed across behavioral tasks. While many of these atypicalities can be explained by an attenuated influence of previously acquired knowledge, a few studies have reported a stronger effect of prior knowledge on perceptual decision making. Given these mixed experimental results, in the present study, we use a recurrent neural network to simulate and investigate perceptual decisions in ASD in a time perception task. In particular, we examine the effects of reliance on priors on interval reproduction performance, and attempt to provide a model-based explanation for the heterogeneous ASD perceptual patterns through a model-aided approach. Overall, by modulating the reliance on priors, we are able to reproduce perceptual patterns ranging from more accurate perceptual decision making to an inability to flexibly integrate incoming sensory evidence, potentially establishing a mapping between reliance on acquired information and individual differences in ASD perception.
Date of Conference: 20-23 May 2024
Date Added to IEEE Xplore: 27 August 2024
ISBN Information: