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

ABSTRACT: 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

SUBMITTER: Rmus M 

PROVIDER: S-EPMC10521012 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature