Repository logo
Log in(current)
Repository logoMIT Open ScholarshipDSpace@MIT
  1. Home
  2. MIT Open Access Articles
  3. MIT Open Access Articles
  4. Reinforcement biases subsequent perceptual decisions when confidence is low, a widespread behavioral phenomenon

Reinforcement biases subsequent perceptual decisions when confidence is low, a widespread behavioral phenomenon

Thumbnail Image
Download
Name

elife-49834-v3.pdf

Description
Published version
Size

1.11 MB

Format

Adobe PDF

Checksum (MD5)

bbbe01892350b861c4db90edc35ca860

sword-2021-08-03T12:54:22.original.xml (130 B)
Original SWORD entry document
Author(s)
Lak, Armin
•
Hueske, Emily
•
Hirokawa, Junya
•
Masset, Paul
•
Ott, Torben
•
Urai, Anne E
•
Donner, Tobias H
•
Carandini, Matteo
•
Tonegawa, Susumu
•
Uchida, Naoshige
more
Date Issued
2020
Journal
eLife
Publisher
eLife Sciences Publications, Ltd
Version
Final published version
Abstract
© 2020, eLife Sciences Publications Ltd. All rights reserved. Learning from successes and failures often improves the quality of subsequent decisions. Past outcomes, however, should not influence purely perceptual decisions after task acquisition is complete since these are designed so that only sensory evidence determines the correct choice. Yet, numerous studies report that outcomes can bias perceptual decisions, causing spurious changes in choice behavior without improving accuracy. Here we show that the effects of reward on perceptual decisions are principled: past rewards bias future choices specifically when previous choice was difficult and hence decision confidence was low. We identified this phenomenon in six datasets from four laboratories, across mice, rats, and humans, and sensory modalities from olfaction and audition to vision. We show that this choice-updating strategy can be explained by reinforcement learning models incorporating statistical decision confidence into their teaching signals. Thus, despite being suboptimal from the experimenter’s perspective, confidence-guided reinforcement learning optimizes behavior in uncertain, real-world situations.
MIT Department
Picower Institute for Learning and Memory
Massachusetts Institute of Technology. Department of Biology
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
McGovern Institute for Brain Research at MIT
Howard Hughes Medical Institute
Terms of Use
Creative Commons Attribution 4.0 International license
https://creativecommons.org/licenses/by/4.0/
Persistent DSpace Link
https://hdl.handle.net/1721.1/135923
DOI of Published Version
https://doi.org/10.7554/ELIFE.49834
Repository logo
PrivacyPermissionsAccessibilityContact us
Repository logo
Notify us about copyright concerns.