2019 Conference on Cognitive Computational Neuroscience

13-16 September 2019    Berlin, Germany

Technical Program

Paper Detail

Paper: PS-1B.48
Session: Poster Session 1B
Location: H Fläche 1.OG
Session Time: Saturday, September 14, 16:30 - 19:30
Presentation Time:Saturday, September 14, 16:30 - 19:30
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Effects of Sensory Precision on Behavioral and Neuroimaging Perceptual Biases in Duration Estimation
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
DOI: https://doi.org/10.32470/CCN.2019.1280-0
Authors: Reny Baykova, Warrick Roseboom, University of Sussex, United Kingdom
Abstract: In Bayesian models of perception, the magnitude of perceptual biases towards prior expectations depends on the precision of incoming sensory information – the more precise the sensory likelihood, the weaker the bias towards the prior. Perceptual biases can be quantified behaviorally by regression to the mean effects, wherein reports are biased towards the mean of previously presented stimuli. As for many aspects of Bayesian perceptual accounts, the neural bases of regression to the mean remain unclear. Here, we investigate how sensory precision influences neural representations of duration using behavioral modelling and EEG decoding. Data simulated using a Bayesian ideal observer model shows that regression to the mean in a duration reproduction task is stronger with high, compared to low sensory precision, providing preliminary evidence that sensory precision affects regression to the mean in Bayesian observers. Using EEG, we are also investigating how sensory precision affects the accuracy of a multivariate classifier to decode stimulus context based on neural responses to the same physical stimulus. The results of these experiments will provide some of the first evidence explicitly linking these key behavioral and neural indices of Bayesian perceptual perception, providing deeper understanding of one of the most fundamental aspects of human perception.