Abstract

Learning and decision making are interactive processes, yet cognitive modelling of error-driven learning and decision making have largely evolved separately. Recently, evidence accumulation models (EAMs) of decision making and reinforcement learning (RL) models of error-driven learning have been combined into joint RL-EAMs that can in principle address these interactions. However, we show that the most commonly used combination, based on the diffusion decision model (DDM) for binary choice, consistently fails to capture crucial aspects of response times observed during reinforcement learning. We propose a new RL-EAM based on an advantage racing diffusion (ARD) framework for choices among two or more options that not only addresses this problem but captures stimulus difficulty, speed-accuracy trade-off, and stimulus-response-mapping reversal effects. The RL-ARD avoids fundamental limitations imposed by the DDM on addressing effects of absolute values of choices, as well as extensions beyond binary choice, and provides a computationally tractable basis for wider applications.

Data availability

All data analysed in this study are available from https://osf.io/ygrve/

The following data sets were generated

Article and author information

Author details

  1. Steven Miletić

    Department of Psychology, University of Amsterdam, University of Amsterdam, Netherlands
    For correspondence
    s.miletic@uva.nl
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7399-2926
  2. Russell J Boag

    Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
    Competing interests
    No competing interests declared.
  3. Anne C Trutti

    Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
    Competing interests
    No competing interests declared.
  4. Niek Stevenson

    Department of Psychology, University of Amsterdam, University of Amsterdam, Netherlands
    Competing interests
    No competing interests declared.
  5. Birte U Forstmann

    Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
    Competing interests
    Birte U Forstmann, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1005-1675
  6. Andrew Heathcote

    Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
    Competing interests
    No competing interests declared.

Funding

Nederlandse Organisatie voor Wetenschappelijk Onderzoek (016.vici.185.052)

  • Birte U Forstmann

Australian Research Council (DP150100272)

  • Andrew Heathcote

Australian Research Council (DP160101891)

  • Andrew Heathcote

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

Reviewing Editor

  1. Valentin Wyart, École normale supérieure, PSL University, INSERM, France

Ethics

Human subjects: Informed consent was obtained in all experiments prior to the experiment onset. The local ethics board of the University of Amsterdam, Department of Psychology approved the study, with reference numbers 2018-BC-9620 (experiment 1), 2019-BC-10672 (experiment 2), 2019-BC-10250 (experiment 3), and 2020-BC-12788 (experiment 4).

Version history

  1. Received: September 15, 2020
  2. Accepted: January 26, 2021
  3. Accepted Manuscript published: January 27, 2021 (version 1)
  4. Version of Record published: February 12, 2021 (version 2)

Copyright

© 2021, Miletić 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.

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  1. Steven Miletić
  2. Russell J Boag
  3. Anne C Trutti
  4. Niek Stevenson
  5. Birte U Forstmann
  6. Andrew Heathcote
(2021)
A new model of decision processing in instrumental learning tasks
eLife 10:e63055.
https://doi.org/10.7554/eLife.63055

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https://doi.org/10.7554/eLife.63055

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