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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.
Original language | English |
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Pages (from-to) | 399-419 |
Number of pages | 21 |
Journal | Computational Brain and Behavior |
Volume | 7 |
Issue number | 3 |
Early online date | 15 Aug 2024 |
DOIs | |
Publication status | Published - Sept 2024 |
MoE publication type | A1 Journal article-refereed |
Keywords
- Computational rationality
- Modeling workflow
- POMDPs
- Resource rationality
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Subjective Functions: Subjective Functions
Oulasvirta, A. (Principal investigator), Putkonen, A.-M. (Project Member), Li, F. (Project Member) & Luo, X. (Project Member)
01/09/2023 → 31/08/2027
Project: Academy of Finland: Other research funding
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Human Automata: Simulator-based Methods for Collaborative AI
Oulasvirta, A. (Principal investigator), Shiripour, M. (Project Member), Putkonen, A.-M. (Project Member), Rastogi, A. (Project Member), Hegemann, L. (Project Member), Iyer, A. (Project Member), Santala, S. (Project Member), Dayama, N. (Project Member), Laine, M. (Project Member), Halasinamara Chandramouli, S. (Project Member), Li, C. (Project Member), Zhu, Y. (Project Member), Liao, Y.-C. (Project Member), Kylmälä, J. (Project Member), Nioche, A. (Project Member) & Kompatscher, J. (Project Member)
01/01/2020 → 31/12/2023
Project: Academy of Finland: Other research funding
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-: Finnish Center for Artificial Intelligence
Kaski, S. (Principal investigator)
01/01/2019 → 31/12/2022
Project: Academy of Finland: Other research funding