A1 Journal article (refereed)
A Workflow for Building Computationally Rational Models of Human Behavior (2024)

Chandramouli, S., Shi, D., Putkonen, A., De Peuter, S., Zhang, S., Jokinen, J., Howes, A., & Oulasvirta, A. (2024). A Workflow for Building Computationally Rational Models of Human Behavior. Computational Brain & Behavior, 7(3), 399-419. https://doi.org/10.1007/s42113-024-00208-6

JYU authors or editors

Publication details

All authors or editors: Chandramouli, Suyog; Shi, Danqing; Putkonen, Aini; De Peuter, Sebastiaan; Zhang, Shanshan; Jokinen, Jussi; Howes, Andrew; Oulasvirta, Antti

Journal or series: Computational Brain & Behavior

ISSN: 2522-0861

eISSN: 2522-087X

Publication year: 2024

Publication date: 15/08/2024

Volume: 7

Issue number: 3

Pages range: 399-419

Publisher: Springer Nature

Publication country: Switzerland

Publication language: English

DOI: https://doi.org/10.1007/s42113-024-00208-6

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/96674

Web address of parallel published publication (pre-print): https://doi.org/10.31234/osf.io/pcvbm

Abstract

Computational rationality explains human behavior as arising due to the maximization of expected utility under the constraints imposed by the environment and limited cognitive resources. This simple assumption, when instantiated via partially observable Markov decision processes (POMDPs), gives rise to a powerful approach for modeling human adaptive behavior, within which a variety of internal models of cognition can be embedded. In particular, such an instantiation enables the use of methods from reinforcement learning (RL) to approximate the optimal policy solution to the sequential decision-making problems posed to the cognitive system in any given setting; this stands in contrast to requiring ad hoc hand-crafted rules for capturing adaptive behavior in more traditional cognitive architectures. However, despite their successes and promise for modeling human adaptive behavior across everyday tasks, computationally rational models that use RL are not easy to build. Being a hybrid of theoretical cognitive models and machine learning (ML) necessitates that model building take into account appropriate practices from both cognitive science and ML. The design of psychological assumptions and machine learning decisions concerning reward specification, policy optimization, parameter inference, and model selection are all tangled processes rife with pitfalls that can hinder the development of valid and effective models. Drawing from a decade of work on this approach, a workflow is outlined for tackling this challenge and is accompanied by a detailed discussion of the pros and cons at key decision points.

Keywords: modelling (representation); cognitive processes; decision making; rationality; behaviour analysis; machine learning; reinforcement learning

Free keywords: computational rationality; resource rationality; modeling workflow; POMDPs

Contributing organizations

Ministry reporting: Yes

VIRTA submission year: 2024

Preliminary JUFO rating: 1