Differentiating between Models of Perceptual Decision Making Using Pupil Size Inferred Confidence

The Journal of Neuroscience : the Official Journal of the Society for Neuroscience
Katsuhisa KawaguchiHendrikje Nienborg

Abstract

During perceptual decisions, subjects often rely more strongly on early, rather than late, sensory evidence, even in tasks when both are equally informative about the correct decision. This early psychophysical weighting has been explained by an integration-to-bound decision process, in which the stimulus is ignored after the accumulated evidence reaches a certain bound, or confidence level. Here, we derive predictions about how the average temporal weighting of the evidence depends on a subject's decision confidence in this model. To test these predictions empirically, we devised a method to infer decision confidence from pupil size in 2 male monkeys performing a disparity discrimination task. Our animals' data confirmed the integration-to-bound predictions, with different internal decision bounds and different levels of correlation between pupil size and decision confidence accounting for differences between animals. However, the data were less compatible with two alternative accounts for early psychophysical weighting: attractor dynamics either within the decision area or due to feedback to sensory areas, or a feedforward account due to neuronal response adaptation. This approach also opens the door to using confidence more ...Continue Reading

Citations

Dec 20, 2018·Journal of Neurophysiology·Marie E BelletPhilipp Berens
Apr 7, 2020·Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences·Jens G KlinzingKarsten Rauss
Jul 5, 2019·Cold Spring Harbor Symposia on Quantitative Biology·Torben OttAdam Kepecs
Nov 14, 2020·Current Biology : CB·Fanny CazettesZachary F Mainen
Feb 26, 2021·Nature Communications·Genís Prat-OrtegaJaime de la Rocha
Jul 24, 2021·Nature Communications·Katrina R QuinnHendrikje Nienborg
Nov 30, 2021·PLoS Computational Biology·Richard D LangeRalf M Haefner

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