Strategy-dependent effects of working-memory limitations on human perceptual decision-making

  1. Kyra Schapiro  Is a corresponding author
  2. Kresimir Josic
  3. Zachary P Kilpatrick
  4. Joshua I Gold
  1. University of Pennsylvania, United States
  2. University of Houston, United States
  3. University of Colorado Boulder, United States

Abstract

Deliberative decisions based on an accumulation of evidence over time depend on working memory, and working memory has limitations, but how these limitations affect deliberative decision-making is not understood. We used human psychophysics to assess the impact of working-memory limitations on the fidelity of a continuous decision variable. Participants decided the average location of multiple visual targets. This computed, continuous decision variable degraded with time and capacity in a manner that depended critically on the strategy used to form the decision variable. This dependence reflected whether the decision variable was computed either: 1) immediately upon observing the evidence, and thus stored as a single value in memory; or 2) at the time of the report, and thus stored as multiple values in memory. These results provide important constraints on how the brain computes and maintains temporally dynamic decision variables.

Data availability

All analysis code is available on GitHub (https://github.com/TheGoldLab/Memory_Diffusion_Task). Data used for figures will be made available on Dryad.

The following data sets were generated
    1. Schapiro K
    2. Josic K
    3. Gold J
    4. Kilpatrick Z
    (2022) Memory Diffusion Task Data
    Dryad Digital Repository, doi:10.5061/dryad.w3r2280rm.

Article and author information

Author details

  1. Kyra Schapiro

    Department of Neuroscience, University of Pennsylvania, Philadelphia, United States
    For correspondence
    kaschapiro@aol.com
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8308-0744
  2. Kresimir Josic

    Department of Mathematics, University of Houston, Houston, United States
    Competing interests
    No competing interests declared.
  3. Zachary P Kilpatrick

    Department of Applied Mathematics, University of Colorado Boulder, Boulder, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2835-9416
  4. Joshua I Gold

    Department of Neuroscience, University of Pennsylvania, Philadelphia, United States
    Competing interests
    Joshua I Gold, Senior editor, eLife.

Funding

National Institute of Mental Health (R01 MH115557)

  • Kresimir Josic
  • Zachary P Kilpatrick
  • Joshua I Gold

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: The task was created with PsychoPy3 and distributed to participants via Pavlovia.com, which allowed participants to perform the task on their home computers after providing informed consent. These protocols were reviewed by the University of Pennsylvania Institutional Review Board (IRB) and determined to meet eligibility criteria for IRB review exemption authorized by 45 CFR 46.104, category 2.

Reviewing Editor

  1. Tobias H Donner, University Medical Center Hamburg-Eppendorf, Germany

Version history

  1. Received: September 4, 2021
  2. Preprint posted: September 6, 2021 (view preprint)
  3. Accepted: March 10, 2022
  4. Accepted Manuscript published: March 15, 2022 (version 1)
  5. Accepted Manuscript updated: March 18, 2022 (version 2)
  6. Version of Record published: April 12, 2022 (version 3)

Copyright

© 2022, Schapiro et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,100
    Page views
  • 193
    Downloads
  • 1
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Kyra Schapiro
  2. Kresimir Josic
  3. Zachary P Kilpatrick
  4. Joshua I Gold
(2022)
Strategy-dependent effects of working-memory limitations on human perceptual decision-making
eLife 11:e73610.
https://doi.org/10.7554/eLife.73610

Further reading

    1. Computational and Systems Biology
    Arya Mani
    Insight

    A deep analysis of multiple genomic datasets reveals which genetic pathways associated with atherosclerosis and coronary artery disease are shared between mice and humans.

    1. Computational and Systems Biology
    Zeyneb Kurt, Jenny Cheng ... Xia Yang
    Research Article

    Mouse models have been used extensively to study human coronary artery disease (CAD) or atherosclerosis and to test therapeutic targets. However, whether mouse and human share similar genetic factors and pathogenic mechanisms of atherosclerosis has not been thoroughly investigated in a data-driven manner. We conducted a cross-species comparison study to better understand atherosclerosis pathogenesis between species by leveraging multiomics data. Specifically, we compared genetically driven and thus CAD-causal gene networks and pathways, by using human GWAS of CAD from the CARDIoGRAMplusC4D consortium and mouse GWAS of atherosclerosis from the Hybrid Mouse Diversity Panel (HMDP) followed by integration with functional multiomics human (STARNET and GTEx) and mouse (HMDP) databases. We found that mouse and human shared >75% of CAD causal pathways. Based on network topology, we then predicted key regulatory genes for both the shared pathways and species-specific pathways, which were further validated through the use of single cell data and the latest CAD GWAS. In sum, our results should serve as a much-needed guidance for which human CAD-causal pathways can or cannot be further evaluated for novel CAD therapies using mouse models.