دورية أكاديمية
أعرض في EDS

Artificial neural networks for model identification and parameter estimation in computational cognitive models.

التفاصيل البيبلوغرافية
العنوان: Artificial neural networks for model identification and parameter estimation in computational cognitive models.
المؤلفون: Rmus M; Department of Psychology, University of California, Berkeley, Berkeley, California, United States of America., Pan TF; Department of Psychology, University of California, Berkeley, Berkeley, California, United States of America., Xia L; Department of Mathematics, University of California, Berkeley, Berkeley, California, United States of America., Collins AGE; Department of Psychology, University of California, Berkeley, Berkeley, California, United States of America.; Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, United States of America.
المصدر: PLoS computational biology [PLoS Comput Biol] 2024 May 15; Vol. 20 (5), pp. e1012119. Date of Electronic Publication: 2024 May 15 (Print Publication: 2024).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE
أسماء مطبوعة: Original Publication: San Francisco, CA : Public Library of Science, [2005]-
مواضيع طبية MeSH: Neural Networks, Computer* , Cognition*/physiology , Computational Biology*/methods , Computer Simulation*, Humans ; Likelihood Functions ; Algorithms ; Models, Neurological
مستخلص: Computational cognitive models have been used extensively to formalize cognitive processes. Model parameters offer a simple way to quantify individual differences in how humans process information. Similarly, model comparison allows researchers to identify which theories, embedded in different models, provide the best accounts of the data. Cognitive modeling uses statistical tools to quantitatively relate models to data that often rely on computing/estimating the likelihood of the data under the model. However, this likelihood is computationally intractable for a substantial number of models. These relevant models may embody reasonable theories of cognition, but are often under-explored due to the limited range of tools available to relate them to data. We contribute to filling this gap in a simple way using artificial neural networks (ANNs) to map data directly onto model identity and parameters, bypassing the likelihood estimation. We test our instantiation of an ANN as a cognitive model fitting tool on classes of cognitive models with strong inter-trial dependencies (such as reinforcement learning models), which offer unique challenges to most methods. We show that we can adequately perform both parameter estimation and model identification using our ANN approach, including for models that cannot be fit using traditional likelihood-based methods. We further discuss our work in the context of the ongoing research leveraging simulation-based approaches to parameter estimation and model identification, and how these approaches broaden the class of cognitive models researchers can quantitatively investigate.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Rmus et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
التعليقات: Update of: bioRxiv. 2024 Apr 02;:. (PMID: 37767088)
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معلومات مُعتمدة: R21 MH132974 United States MH NIMH NIH HHS
تواريخ الأحداث: Date Created: 20240515 Date Completed: 20240528 Latest Revision: 20240530
رمز التحديث: 20240530
مُعرف محوري في PubMed: PMC11132492
DOI: 10.1371/journal.pcbi.1012119
PMID: 38748770
قاعدة البيانات: MEDLINE
الوصف
تدمد:1553-7358
DOI:10.1371/journal.pcbi.1012119