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Related Experiment Video

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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Published on: March 2, 2015

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

Milena Rmus¹, Ti-Fen Pan¹, Liyu Xia²

¹Department of Psychology, University of California, Berkeley, Berkeley, California, United States of America.

Plos Computational Biology

|May 15, 2024

Summary

This study introduces artificial neural networks (ANNs) to fit cognitive models, bypassing complex likelihood calculations. This method enables quantitative analysis of previously intractable cognitive models, advancing computational cognitive science.

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Area of Science:

Cognitive Science
Computational Neuroscience
Artificial Intelligence

Background:

Computational cognitive models formalize cognitive processes and quantify individual differences via parameters.
Model comparison identifies theories best explaining empirical data, often relying on likelihood estimation.
Many complex cognitive models are under-explored due to computationally intractable likelihood calculations.

Purpose of the Study:

To develop a novel method for fitting cognitive models that bypasses computationally intensive likelihood estimation.
To enable quantitative investigation of cognitive models with intractable likelihoods, including those with inter-trial dependencies.
To facilitate both parameter estimation and model identification for a broader range of cognitive theories.

Main Methods:

Utilized artificial neural networks (ANNs) to directly map data to cognitive model identity and parameters.
Bypassed traditional likelihood estimation, addressing computational intractability.
Tested the ANN approach on cognitive models with strong inter-trial dependencies, such as reinforcement learning models.

Main Results:

Successfully performed parameter estimation and model identification using the ANN approach.
Demonstrated efficacy even for models intractable to traditional likelihood-based fitting methods.
Validated the ANN approach on challenging models like reinforcement learning, known for inter-trial dependencies.

Conclusions:

Artificial neural networks offer a viable and accessible tool for fitting complex cognitive models.
This simulation-based approach expands the scope of cognitive models amenable to quantitative analysis.
The method enhances the ability to test diverse cognitive theories and understand individual differences.