Validating model-based Bayesian integration using prior–cost metamers
Name
e2021531118.full.pdf
Description
Published version
Size
4.31 MB
Format
Adobe PDF
Checksum (MD5)
08f0f6c3cbdc654fd2bf25a4e1d3cf21
Author(s)
Sohn, Hansem
Jazayeri, Mehrdad
Date Issued
2021
Journal
Proceedings of the National Academy of Sciences of the United States of America
Publisher
Proceedings of the National Academy of Sciences
Citation
Sohn, Hansem and Jazayeri, Mehrdad. 2021. "Validating model-based Bayesian integration using prior–cost metamers." Proceedings of the National Academy of Sciences of the United States of America, 118 (25).
Version
Final published version
Abstract
There are two competing views on how humans make decisions under uncertainty. Bayesian decision theory posits that humans optimize their behavior by establishing and integrating internal models of past sensory experiences (priors) and decision outcomes (cost functions). An alternative hypothesis posits that decisions are optimized through trial and error without explicit internal models for priors and cost functions. To distinguish between these possibilities, we introduce a paradigm that probes the sensitivity of humans to transitions between prior–cost pairs that demand the same optimal policy (metamers) but distinct internal models. We demonstrate the utility of our approach in two experiments that were classically explained by Bayesian theory. Our approach validates the Bayesian learning strategy in an interval timing task but not in a visuomotor rotation task. More generally, our work provides a domain-general approach for testing the circumstances under which humans explicitly implement model-based Bayesian computations.
MIT Department
McGovern Institute for Brain Research at MIT
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1073/PNAS.2021531118
