Likelihood-based Parameter Estimation and Comparison of Dynamical Cognitive Models

TL;DR

This paper introduces a maximum-likelihood method for analyzing and comparing dynamical cognitive models using time-ordered data, demonstrated through a saccade generation model, improving parameter estimation and model differentiation.

Contribution

It presents a novel likelihood-based framework for parameter estimation and model comparison in dynamical cognitive models, including Bayesian inference and hierarchical modeling capabilities.

Findings

Likelihood approach outperforms non-dynamical models

Enables reliable parameter estimation with credible intervals

Differentiates between model variants with similar predictions

Abstract

Dynamical models of cognition play an increasingly important role in driving theoretical and experimental research in psychology. Therefore, parameter estimation, model analysis and comparison of dynamical models are of essential importance. Here we propose a maximum-likelihood approach for model analysis in a fully dynamical framework that includes time-ordered experimental data. Our methods can be applied to dynamical models for the prediction of discrete behavior (e.g., movement onsets), in particular, we use a dynamical model of saccade generation in scene viewing as a case study for our approach. For this model, the likelihood function can be computed directly by numerical simulation, which enables more efficient parameter estimation including Bayesian inference to obtain reliable estimates and corresponding credible intervals. Using hierarchical models inference is even possible for individual observers. Furthermore, our likelihood approach can be used to compare different models. In our example, the dynamical framework is shown to outperform non-dynamical statistical models. Additionally, the likelihood based evaluation differentiates model variants, which produced indistinguishable predictions on hitherto used statistics. Our results indicate that the likelihood approach is a promising framework for dynamical cognitive models.