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Paper Detail

Paper: PS-1A.40
Session: Poster Session 1A
Location: H Lichthof
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: Pupil dilation indexes statistical learning about the uncertainty of stimulus distributions
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors: Francesco Silvestrin, Thomas FitzGerald, William Penny, University of East Anglia, United Kingdom
Abstract: Learning about the uncertainty of environmental stimuli is a fundamental requirement of adaptive behaviour. In this experiment we probe whether pupil dilation in response to brief auditory stimuli reflects statistical learning about the underlying stimulus distributions. Specifically, we consider whether pupil dilation reflects automatic (task-irrelevant) learning about the precision of Gaussian distributions of tones. By comparing responses to perceptually identical outlier and standard tones in low and high precision blocks, we provide clear evidence that subjects do indeed learn about precision, as reflected by increased responses to surprising (outlier) tones during high precision blocks. This extends previous work looking at electrophysiological effects of precision learning, and provides new evidence that the putatively noradrenergic processes underlying pupil dilation reflect learning about the uncertainty of stimulus distributions. In addition, we use our data to test a new convolution-based approach for analysing pupillometry data, which we believe has considerable promise for this and future studies.