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Related Experiment Video

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New Variations for Strategy Set-shifting in the Rat

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Published on: January 23, 2017

Comparative Analysis of Behavioral Models for Adaptive Learning in Changing Environments.

Dimitrije Marković¹, Stefan J Kiebel¹

¹Department of Psychology, Technische Universität Dresden Dresden, Germany.

Frontiers in Computational Neuroscience

|May 6, 2016

Summary

Comparing probabilistic models for decision making under uncertainty is crucial. Bayesian methods accurately distinguish between Hierarchical Gaussian Filters and Change Point Models, outperforming maximum-likelihood approaches.

Keywords:

Bayesian inference Bayesian model comparison Hierarchical Gaussian Filters change point models changing environments decision making maximum-likelihood estimate

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Area of Science:

Cognitive neuroscience
Computational psychiatry
Decision science

Background:

Probabilistic models enhance understanding of decision making under uncertainty in behavioral and fMRI studies.
Lack of model comparison risks over-interpretation of single-model findings.

Purpose of the Study:

To compare the performance of Hierarchical Gaussian Filters and Change Point Models in explaining behavioral and neuroimaging data.
To evaluate Bayesian and maximum-likelihood methods for model comparison and parameter estimation.

Main Methods:

Analysis of two hierarchical probabilistic models: Hierarchical Gaussian Filters and Change Point Models.
Utilized simulated behavioral experiments with noisy and correlated data.
Compared Bayesian inference and model comparison against Maximum-Likelihood with Bayesian Information Criterion.

Main Results:

Accurate disambiguation between the two models was achieved using simulated data.
Precise inference of model parameters and hidden belief trajectories (expectations, uncertainties, prediction errors) was demonstrated.
Bayesian inference and model comparison showed advantages over Maximum-Likelihood schemes.

Conclusions:

Bayesian data analysis is highly relevant for model-based neuroimaging in decision making under uncertainty.
The study validates the use of Bayesian methods for comparing complex probabilistic models.
Accurate model comparison and parameter estimation are feasible even with imperfect data.