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Strategically managing learning during perceptual decision making

Masís, J; Chapman, T; Rhee, JY; Cox, DD; Saxe, AM; (2023) Strategically managing learning during perceptual decision making. eLife , 12 , Article e64978. 10.7554/eLife.64978. Green open access

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Abstract

Making optimal decisions in the face of noise requires balancing short-term speed and accuracy. But a theory of optimality should account for the fact that short-term speed can influence long-term accuracy through learning. Here, we demonstrate that long-term learning is an important dynamical dimension of the speed-accuracy trade-off. We study learning trajectories in rats and formally characterize these dynamics in a theory expressed as both a recurrent neural network and an analytical extension of the drift-diffusion model that learns over time. The model reveals that choosing suboptimal response times to learn faster sacrifices immediate reward, but can lead to greater total reward. We empirically verify predictions of the theory, including a relationship between stimulus exposure and learning speed, and a modulation of reaction time by future learning prospects. We find that rats' strategies approximately maximize total reward over the full learning epoch, suggesting cognitive control over the learning process.

Type: Article
Title: Strategically managing learning during perceptual decision making
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.7554/eLife.64978
Publisher version: https://doi.org/10.7554/eLife.64978
Language: English
Additional information: © 2023, Masís et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Keywords: Behavior, cognitive control, decision making, inter-temporal choice, learning, neural networks, neuroscience, rat, Animals, Rats, Decision Making, Learning, Reaction Time, Reward, Neural Networks, Computer
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit
URI: https://discovery.ucl.ac.uk/id/eprint/10166619
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