Identifying relationships between cognitive processes across tasks, contexts, and time

TL;DR

This paper introduces a statistically principled method to estimate correlations between latent cognitive processes across different tasks, contexts, and time, enhancing understanding of their generalizability and reliability.

Contribution

It develops a novel approach that simultaneously estimates individual and group-level parameters, allowing for more precise cross-task and cross-context comparisons of cognitive processes.

Findings

Method is practical with standard experimental designs.

Provides evidence of reliability and validity of cognitive model parameters.

Enhances understanding of cognitive process relatedness across conditions.

Abstract

It is commonly assumed that a specific testing occasion (task, design, procedure, etc.) provides insights that generalise beyond that occasion. This assumption is infrequently carefully tested in data. We develop a statistically principled method to directly estimate the correlation between latent components of cognitive processing across tasks, contexts, and time. This method simultaneously estimates individual-participant parameters of a cognitive model at each testing occasion, group-level parameters representing across-participant parameter averages and variances, and across-task correlations. The approach provides a natural way to "borrow" strength across testing occasions, which can increase the precision of parameter estimates across all testing occasions. Two example applications demonstrate that the method is practical in standard designs. The examples, and a simulation study, also provide evidence about the reliability and validity of parameter estimates from the linear ballistic accumulator model. We conclude by highlighting the potential of the parameter-correlation method to provide an "assumption-light" tool for estimating the relatedness of cognitive processes across tasks, contexts, and time.