Normative decision rules in changing environments

  1. Nicholas W Barendregt  Is a corresponding author
  2. Joshua I Gold
  3. Krešimir Josić
  4. Zachary P Kilpatrick
  1. University of Colorado Boulder, United States
  2. University of Pennsylvania, United States
  3. University of Houston, United States

Abstract

Models based on normative principles have played a major role in our understanding of how the brain forms decisions. However, these models have typically been derived for simple, stable conditions, and their relevance to decisions formed under more naturalistic, dynamic conditions is unclear. We previously derived a normative decision model in which evidence accumulation is adapted to fluctuations in the evidence-generating process that occur during a single decision (Glaze et al., 2015), but the evolution of commitment rules (e.g., thresholds on the accumulated evidence) under dynamic conditions is not fully understood. Here we derive a normative model for decisions based on changing contexts, which we define as changes in evidence quality or reward, over the course of a single decision. In these cases, performance (reward rate) is maximized using decision thresholds that respond to and even anticipate these changes, in contrast to the static thresholds used in many decision models. We show that these adaptive thresholds exhibit several distinct temporal motifs that depend on the specific predicted and experienced context changes and that adaptive models perform robustly even when implemented imperfectly (noisily). We further show that decision models with adaptive thresholds outperform those with constant or urgency-gated thresholds in accounting for human response times on a task with time-varying evidence quality and average reward. These results further link normative and neural decision-making while expanding our view of both as dynamic, adaptive processes that update and use expectations to govern both deliberation and commitment.

Data availability

MATLAB code used to generate all results and figures is available at https://github.com/nwbarendregt/AdaptNormThresh.

Article and author information

Author details

  1. Nicholas W Barendregt

    Department of Applied Mathematics, University of Colorado Boulder, Boulder, United States
    For correspondence
    nicholas.barendregt@colorado.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3268-9426
  2. Joshua I Gold

    Department of Neuroscience, University of Pennsylvania, Philadelphia, United States
    Competing interests
    Joshua I Gold, Senior editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6018-0483
  3. Krešimir Josić

    Department of Mathematics, University of Houston, Houston, United States
    Competing interests
    No competing interests declared.
  4. 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

Funding

National Institutes of Health (R01-MH-115557)

  • Nicholas W Barendregt
  • Joshua I Gold
  • Krešimir Josić
  • Zachary P Kilpatrick

National Institutes of Health (R01-EB029847-01)

  • Nicholas W Barendregt
  • Zachary P Kilpatrick

National Science Foundation (NSF-DMS-1853630)

  • Nicholas W Barendregt
  • Zachary P Kilpatrick

National Science Foundation (NSF-DBI-1707400)

  • Krešimir Josić

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

Reviewing Editor

  1. Peter Latham, University College London, United Kingdom

Version history

  1. Preprint posted: April 29, 2022 (view preprint)
  2. Received: May 3, 2022
  3. Accepted: October 20, 2022
  4. Accepted Manuscript published: October 25, 2022 (version 1)
  5. Version of Record published: December 15, 2022 (version 2)

Copyright

© 2022, Barendregt 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

  • 1,017
    views
  • 165
    downloads
  • 3
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

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. Nicholas W Barendregt
  2. Joshua I Gold
  3. Krešimir Josić
  4. Zachary P Kilpatrick
(2022)
Normative decision rules in changing environments
eLife 11:e79824.
https://doi.org/10.7554/eLife.79824

Share this article

https://doi.org/10.7554/eLife.79824

Further reading

    1. Computational and Systems Biology
    Maksim Kleverov, Daria Zenkova ... Alexey A Sergushichev
    Research Article

    Transcriptomic profiling became a standard approach to quantify a cell state, which led to accumulation of huge amount of public gene expression datasets. However, both reuse of these datasets or analysis of newly generated ones requires significant technical expertise. Here we present Phantasus - a user-friendly web-application for interactive gene expression analysis which provides a streamlined access to more than 96000 public gene expression datasets, as well as allows analysis of user-uploaded datasets. Phantasus integrates an intuitive and highly interactive JavaScript-based heatmap interface with an ability to run sophisticated R-based analysis methods. Overall Phantasus allows users to go all the way from loading, normalizing and filtering data to doing differential gene expression and downstream analysis. Phantasus can be accessed on-line at https://alserglab.wustl.edu/phantasus or can be installed locally from Bioconductor (https://bioconductor.org/packages/phantasus). Phantasus source code is available at https://github.com/ctlab/phantasus under MIT license.

    1. Computational and Systems Biology
    2. Evolutionary Biology
    Ryan T Bell, Harutyun Sahakyan ... Eugene V Koonin
    Research Article

    A comprehensive census of McrBC systems, among the most common forms of prokaryotic Type IV restriction systems, followed by phylogenetic analysis, reveals their enormous abundance in diverse prokaryotes and a plethora of genomic associations. We focus on a previously uncharacterized branch, which we denote coiled-coil nuclease tandems (CoCoNuTs) for their salient features: the presence of extensive coiled-coil structures and tandem nucleases. The CoCoNuTs alone show extraordinary variety, with three distinct types and multiple subtypes. All CoCoNuTs contain domains predicted to interact with translation system components, such as OB-folds resembling the SmpB protein that binds bacterial transfer-messenger RNA (tmRNA), YTH-like domains that might recognize methylated tmRNA, tRNA, or rRNA, and RNA-binding Hsp70 chaperone homologs, along with RNases, such as HEPN domains, all suggesting that the CoCoNuTs target RNA. Many CoCoNuTs might additionally target DNA, via McrC nuclease homologs. Additional restriction systems, such as Type I RM, BREX, and Druantia Type III, are frequently encoded in the same predicted superoperons. In many of these superoperons, CoCoNuTs are likely regulated by cyclic nucleotides, possibly, RNA fragments with cyclic termini, that bind associated CARF (CRISPR-Associated Rossmann Fold) domains. We hypothesize that the CoCoNuTs, together with the ancillary restriction factors, employ an echeloned defense strategy analogous to that of Type III CRISPR-Cas systems, in which an immune response eliminating virus DNA and/or RNA is launched first, but then, if it fails, an abortive infection response leading to PCD/dormancy via host RNA cleavage takes over.