1. Neuroscience

Tracking subjects' strategies in behavioural choice experiments at trial resolution

  1. Silvia Maggi
  2. Rebecca M Hock
  3. Martin O'Neill
  4. Mark Buckley
  5. Paula M Moran
  6. Tobias Bast
  7. Musa Sami
  8. Mark D Humphries  Is a corresponding author
  1. University of Nottingham, United Kingdom
  2. Atlantic Technological University, Ireland
  3. University of Oxford, United Kingdom
https://doi.org/10.7554/eLife.86491
Share this article
Cite this article
  1. Silvia Maggi
  2. Rebecca M Hock
  3. Martin O'Neill
  4. Mark Buckley
  5. Paula M Moran
  6. Tobias Bast
  7. Musa Sami
  8. Mark D Humphries
(2024)
Tracking subjects' strategies in behavioural choice experiments at trial resolution
eLife 13:e86491.
https://doi.org/10.7554/eLife.86491
  2. Download BibTeX
  3. Download .RIS

Abstract

Investigating how, when, and what subjects learn during decision-making tasks requires tracking their choice strategies on a trial-by-trial basis. Here we present a simple but effective probabilistic approach to tracking choice strategies at trial resolution using Bayesian evidence accumulation. We show this approach identifies both successful learning and the exploratory strategies used in decision tasks performed by humans, non-human primates, rats, and synthetic agents. Both when subjects learn and when rules change the exploratory strategies of win-stay and lose-shift, often considered complementary, are consistently used independently. Indeed, we find the use of lose-shift is strong evidence that subjects have latently learnt the salient features of a new rewarded rule. Our approach can be extended to any discrete choice strategy, and its low computational cost is ideally suited for real-time analysis and closed-loop control.

Data availability

Source data from the rat Y-maze task data are available from crcns.org at http://dx.doi.org/10.6080/K0KH0KH5. Source data from the rat lever-press task (32 rats), the human gain/loss task (20 participants) and the primate stimulus-to-action task (one session) are available from the Nottingham Research Data Management Service at http://doi.org/10.17639/nott.7274.Processed data and analysis code to replicate all figures are available in our GitHub repository https://github.com/Humphries-Lab/Bayesian_strategy_analysis_Paper. Our copies of the source data for the Y-maze task, lever-press task, gain/loss task, and stimulus-to-action task are also freely available from the same repository.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Silvia Maggi

    School of Psychology, University of Nottingham, Nottingham, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6533-3509

  2. Rebecca M Hock

    School of Psychology, University of Nottingham, Nottingham, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0917-570X

  3. Martin O'Neill

    Deparyment of Health and Nutritional Sciences, Atlantic Technological University, Sligo, Ireland
    Competing interests
    The authors declare that no competing interests exist.

  4. Mark Buckley

    Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7455-8486

  5. Paula M Moran

    School of Psychology, University of Nottingham, Nottingham, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  6. Tobias Bast

    School of Psychology, University of Nottingham, Nottingham, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6163-3229

  7. Musa Sami

    Institute of Mental Health, University of Nottingham, Nottingham, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  8. Mark D Humphries

    School of Psychology, University of Nottingham, Nottingham, United Kingdom
    For correspondence
    mark.humphries@nottingham.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1906-2581

Funding

Medical Research Council (MR/J008648/1)

  • Mark D Humphries

Medical Research Council (MR/P005659/1)

  • Mark D Humphries

Medical Research Council (MR/S025944/1)

  • Mark D Humphries

Medical Research Council (MR/K005480/1)

  • Mark Buckley

Biotechnology and Biological Sciences Research Council (BB/T00598X/1)

  • Mark Buckley
  • Mark D Humphries

Biotechnology and Biological Sciences Research Council (BB/M008770/1)

  • Rebecca M Hock
  • Paula M Moran
  • Tobias Bast

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

Reviewing Editor

  1. Alicia Izquierdo, University of California, Los Angeles, United States

Ethics

Animal experimentation: Rat - lever-press task: All experimental procedures were conducted in accordance with the requirements of the United Kingdom (UK) Animals (Scientific Procedures) Act 1986, approved by the University of Nottingham's Animal Welfare and Ethical Review Board (AWERB) and run under the authority of Home Office project license 30/3357.Non-human primate task: All animal training and experimental procedures were performed in accordance with the guidelines of the UK Animals (Scientific Procedures) Act of 1986, licensed by the UK Home Office, and approved by Oxford University's Committee on Animal Care and Ethical Review.

Human subjects: The human gain/loss task study was approved by Research Ethics Committee (Stanmore London REC 17/LO/0577). All participants were read a participation information leaflet and undertook informed consent.

Version history

  1. Preprint posted: August 31, 2022 (view preprint)
  2. Received: January 29, 2023
  3. Accepted: February 23, 2024
  4. Accepted Manuscript published: March 1, 2024 (version 1)
  5. Version of Record published: March 22, 2024 (version 2)

Copyright

© 2024, Maggi 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

  • 1,125
    views
  • 175
    downloads
  • 1
    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. Silvia Maggi
  2. Rebecca M Hock
  3. Martin O'Neill
  4. Mark Buckley
  5. Paula M Moran
  6. Tobias Bast
  7. Musa Sami
  8. Mark D Humphries
(2024)
Tracking subjects' strategies in behavioural choice experiments at trial resolution
eLife 13:e86491.
https://doi.org/10.7554/eLife.86491

Share this article

https://doi.org/10.7554/eLife.86491

Categories and tags

Research organisms

Further reading

    1. Neuroscience

    A stochastic world model on gravity for stability inference

    Taicheng Huang, Jia Liu
    Research Article

    The fact that objects without proper support will fall to the ground is not only a natural phenomenon, but also common sense in mind. Previous studies suggest that humans may infer objects’ stability through a world model that performs mental simulations with a priori knowledge of gravity acting upon the objects. Here we measured participants’ sensitivity to gravity to investigate how the world model works. We found that the world model on gravity was not a faithful replica of the physical laws, but instead encoded gravity’s vertical direction as a Gaussian distribution. The world model with this stochastic feature fit nicely with participants’ subjective sense of objects’ stability and explained the illusion that taller objects are perceived as more likely to fall. Furthermore, a computational model with reinforcement learning revealed that the stochastic characteristic likely originated from experience-dependent comparisons between predictions formed by internal simulations and the realities observed in the external world, which illustrated the ecological advantage of stochastic representation in balancing accuracy and speed for efficient stability inference. The stochastic world model on gravity provides an example of how a priori knowledge of the physical world is implemented in mind that helps humans operate flexibly in open-ended environments.

    1. Neuroscience

    Drift of neural ensembles driven by slow fluctuations of intrinsic excitability

    Geoffroy Delamare, Yosif Zaki ... Claudia Clopath
    Short Report

    Representational drift refers to the dynamic nature of neural representations in the brain despite the behavior being seemingly stable. Although drift has been observed in many different brain regions, the mechanisms underlying it are not known. Since intrinsic neural excitability is suggested to play a key role in regulating memory allocation, fluctuations of excitability could bias the reactivation of previously stored memory ensembles and therefore act as a motor for drift. Here, we propose a rate-based plastic recurrent neural network with slow fluctuations of intrinsic excitability. We first show that subsequent reactivations of a neural ensemble can lead to drift of this ensemble. The model predicts that drift is induced by co-activation of previously active neurons along with neurons with high excitability which leads to remodeling of the recurrent weights. Consistent with previous experimental works, the drifting ensemble is informative about its temporal history. Crucially, we show that the gradual nature of the drift is necessary for decoding temporal information from the activity of the ensemble. Finally, we show that the memory is preserved and can be decoded by an output neuron having plastic synapses with the main region.

    1. Cell Biology
    2. Neuroscience

    Synapsin E-domain is essential for α-synuclein function

    Alexandra Stavsky, Leonardo A Parra-Rivas ... Daniel Gitler
    Short Report

    The cytosolic proteins synucleins and synapsins are thought to play cooperative roles in regulating synaptic vesicle (SV) recycling, but mechanistic insight is lacking. Here, we identify the synapsin E-domain as an essential functional binding-partner of α-synuclein (α-syn). Synapsin E-domain allows α-syn functionality, binds to α-syn, and is necessary and sufficient for enabling effects of α-syn at synapses of cultured mouse hippocampal neurons. Together with previous studies implicating the E-domain in clustering SVs, our experiments advocate a cooperative role for these two proteins in maintaining physiologic SV clusters.