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.

Metrics

  • 3,047
    Page views
  • 354
    Downloads
  • 22
    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. 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

Share this article

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

Further reading

    1. Neuroscience
    Kiwamu Kudo, Kamalini G Ranasinghe ... Srikantan S Nagarajan
    Research Article

    Alzheimer’s disease (AD) is characterized by the accumulation of amyloid-β and misfolded tau proteins causing synaptic dysfunction, and progressive neurodegeneration and cognitive decline. Altered neural oscillations have been consistently demonstrated in AD. However, the trajectories of abnormal neural oscillations in AD progression and their relationship to neurodegeneration and cognitive decline are unknown. Here, we deployed robust event-based sequencing models (EBMs) to investigate the trajectories of long-range and local neural synchrony across AD stages, estimated from resting-state magnetoencephalography. The increases in neural synchrony in the delta-theta band and the decreases in the alpha and beta bands showed progressive changes throughout the stages of the EBM. Decreases in alpha and beta band synchrony preceded both neurodegeneration and cognitive decline, indicating that frequency-specific neuronal synchrony abnormalities are early manifestations of AD pathophysiology. The long-range synchrony effects were greater than the local synchrony, indicating a greater sensitivity of connectivity metrics involving multiple regions of the brain. These results demonstrate the evolution of functional neuronal deficits along the sequence of AD progression.

    1. Medicine
    2. Neuroscience
    Luisa Fassi, Shachar Hochman ... Roi Cohen Kadosh
    Research Article

    In recent years, there has been debate about the effectiveness of treatments from different fields, such as neurostimulation, neurofeedback, brain training, and pharmacotherapy. This debate has been fuelled by contradictory and nuanced experimental findings. Notably, the effectiveness of a given treatment is commonly evaluated by comparing the effect of the active treatment versus the placebo on human health and/or behaviour. However, this approach neglects the individual’s subjective experience of the type of treatment she or he received in establishing treatment efficacy. Here, we show that individual differences in subjective treatment - the thought of receiving the active or placebo condition during an experiment - can explain variability in outcomes better than the actual treatment. We analysed four independent datasets (N = 387 participants), including clinical patients and healthy adults from different age groups who were exposed to different neurostimulation treatments (transcranial magnetic stimulation: Studies 1 and 2; transcranial direct current stimulation: Studies 3 and 4). Our findings show that the inclusion of subjective treatment can provide a better model fit either alone or in interaction with objective treatment (defined as the condition to which participants are assigned in the experiment). These results demonstrate the significant contribution of subjective experience in explaining the variability of clinical, cognitive, and behavioural outcomes. We advocate for existing and future studies in clinical and non-clinical research to start accounting for participants’ subjective beliefs and their interplay with objective treatment when assessing the efficacy of treatments. This approach will be crucial in providing a more accurate estimation of the treatment effect and its source, allowing the development of effective and reproducible interventions.