Artificial neural networks for model identification and parameter estimation in computational cognitive models.
| العنوان: | Artificial neural networks for model identification and parameter estimation in computational cognitive models. |
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| المؤلفون: | Rmus M; UC Berkeley., Pan TF; UC Berkeley., Xia L; UC Berkeley., Collins AGE; UC Berkeley. |
| المصدر: | BioRxiv : the preprint server for biology [bioRxiv] 2024 Apr 02. Date of Electronic Publication: 2024 Apr 02. |
| نوع المنشور: | Preprint |
| اللغة: | English |
| بيانات الدورية: | Country of Publication: United States NLM ID: 101680187 Publication Model: Electronic Cited Medium: Internet NLM ISO Abbreviation: bioRxiv Subsets: PubMed not MEDLINE |
| 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. |
| التعليقات: | Update in: PLoS Comput Biol. 2024 May 15;20(5):e1012119. (PMID: 38748770) |
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| معلومات مُعتمدة: | R21 MH132974 United States MH NIMH NIH HHS |
| تواريخ الأحداث: | Date Created: 20230928 Latest Revision: 20240528 |
| رمز التحديث: | 20240528 |
| مُعرف محوري في PubMed: | PMC10521012 |
| DOI: | 10.1101/2023.09.14.557793 |
| PMID: | 37767088 |
| قاعدة البيانات: | MEDLINE |
| DOI: | 10.1101/2023.09.14.557793 |
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