Pupil dilation indexes automatic and dynamic inference about the precision of stimulus distributions

Silvestrin, Francesco, Penny, Will D. ORCID: https://orcid.org/0000-0001-9064-1191 and FitzGerald, Thomas H.B. ORCID: https://orcid.org/0000-0002-3855-1591 (2021) Pupil dilation indexes automatic and dynamic inference about the precision of stimulus distributions. Journal of Mathematical Psychology, 101. ISSN 0022-2496

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Abstract

Learning about the statistics of one's environment is a fundamental requirement of adaptive behaviour. In this experiment we probe whether pupil dilation in response to brief auditory stimuli reflects automatic 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 pitch in a sequence of tones. We provide clear evidence, both by comparing responses to perceptually identical probe tones in low and high precision blocks, and using a novel model-based analysis, that subjects did indeed track the precision of the stimulus distribution. This extends previous work looking at electrophysiological effects of precision (or, equivalently, variance) learning, and provides new evidence that the putatively noradrenergic processes underlying pupil dilation reflect rapidly updated information about distributions of sensory stimuli. In addition, our study represents a validation of our model-based approach to analysing pupillometry data, which we believe has considerable promise for future studies.

Item Type: Article
Uncontrolled Keywords: auditory oddball,precision,predictive coding,pupillometry,statistical learning,surprise,psychology(all),applied mathematics,3*,uoa4 ,/dk/atira/pure/subjectarea/asjc/3200
Faculty \ School: Faculty of Social Sciences > School of Psychology
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 18 Mar 2021 00:42
Last Modified: 17 Dec 2024 01:32
URI: https://ueaeprints.uea.ac.uk/id/eprint/79498
DOI: 10.1016/j.jmp.2021.102503

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