A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Chandramouli, Suyog; Shi, Danqing; Putkonen, Aini; De Peuter, Sebastiaan; Zhang, Shanshan; Jokinen, Jussi; Howes, Andrew; Oulasvirta, Antti

Lehti tai sarja: Computational Brain & Behavior

ISSN: 2522-0861

eISSN: 2522-087X

Julkaisuvuosi: 2024

Ilmestymispäivä: 15.08.2024

Volyymi: 7

Lehden numero: 3

Artikkelin sivunumerot: 399-419

Kustantaja: Springer Nature

Julkaisumaa: Sveitsi

Julkaisun kieli: englanti

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

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Osittain avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/96674

Rinnakkaistallenteen verkko-osoite (pre-print): https://doi.org/10.31234/osf.io/pcvbm

Tiivistelmä

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.

YSO-asiasanat: mallintaminen; kognitiiviset prosessit; päätöksenteko; rationaalisuus; käyttäytymisanalyysi; koneoppiminen; vahvistusoppiminen

Vapaat asiasanat: computational rationality; resource rationality; modeling workflow; POMDPs

Liittyvät organisaatiot

OKM-raportointi: Kyllä

VIRTA-lähetysvuosi: 2024

Alustava JUFO-taso: 1