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Models that learn how humans learn: The case of decision-making and its disorders

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  • Amir Dezfouli
  • Kristi Griffiths
  • Fabio Ramos
  • Peter Dayan
  • Bernard W Balleine

Abstract

Popular computational models of decision-making make specific assumptions about learning processes that may cause them to underfit observed behaviours. Here we suggest an alternative method using recurrent neural networks (RNNs) to generate a flexible family of models that have sufficient capacity to represent the complex learning and decision- making strategies used by humans. In this approach, an RNN is trained to predict the next action that a subject will take in a decision-making task and, in this way, learns to imitate the processes underlying subjects’ choices and their learning abilities. We demonstrate the benefits of this approach using a new dataset drawn from patients with either unipolar (n = 34) or bipolar (n = 33) depression and matched healthy controls (n = 34) making decisions on a two-armed bandit task. The results indicate that this new approach is better than baseline reinforcement-learning methods in terms of overall performance and its capacity to predict subjects’ choices. We show that the model can be interpreted using off-policy simulations and thereby provides a novel clustering of subjects’ learning processes—something that often eludes traditional approaches to modelling and behavioural analysis.Author summary: Computational models of decision-making provide a quantitative characterisation of the learning and choice processes behind human actions. Designing a computational model is often based on manual engineering with an iterative process to examine the consistency between different aspects of the model and the empirical data. In practice, however, inconsistencies between the model and observed behaviours can remain hidden behind examined summary statistics. To address this limitation, we developed a recurrent neural network (RNNs) as a flexible type of model that can automatically characterize human decision-making processes without requiring tweaking and engineering. To show the benefits of this new approach, we collected data on a decision-making task conducted on subjects with either bipolar or unipolar depression, as well as healthy controls. The results showed that, indeed, important aspects of decision-making remained uncaptured by typical computational models and even their enhanced variants, but were captured by RNNs automatically. Further, we were able to show that the nature of such processes can be unveiled by simulating the model under various conditions. This new approach can be used, therefore, as a standalone model of decision-making or as a baseline model to evaluate how well other candidate models fit observed data.

Suggested Citation

  • Amir Dezfouli & Kristi Griffiths & Fabio Ramos & Peter Dayan & Bernard W Balleine, 2019. "Models that learn how humans learn: The case of decision-making and its disorders," PLOS Computational Biology, Public Library of Science, vol. 15(6), pages 1-33, June.
  • Handle: RePEc:plo:pcbi00:1006903
    DOI: 10.1371/journal.pcbi.1006903
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    References listed on IDEAS

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    3. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    4. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
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    Cited by:

    1. Shiva Farashahi & Alireza Soltani, 2021. "Computational mechanisms of distributed value representations and mixed learning strategies," Nature Communications, Nature, vol. 12(1), pages 1-18, December.
    2. Amir Dezfouli & Bernard W Balleine, 2019. "Learning the structure of the world: The adaptive nature of state-space and action representations in multi-stage decision-making," PLOS Computational Biology, Public Library of Science, vol. 15(9), pages 1-22, September.
    3. Amir Bagherpour, 2021. "How computer simulations enhance geopolitical decision‐making: A commentary on Lustick and Tetlock 2021," Futures & Foresight Science, John Wiley & Sons, vol. 3(2), June.

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