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

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 lack of tools required to relate them to data. We propose to fill this gap using artificial neural networks (ANNs) to map data directly onto model identity and parameters, bypassing the likelihood estimation. Our results show that we can adequately perform both parameter estimation and model identification using our new ANN approach, including for models that cannot be fit using traditional likelihood-based methods. Our new ANN approach will greatly broaden the class of cognitive models researchers can quantitatively consider.