On the efficiency of neurally-informed cognitive models to identify latent cognitive states

Abstract

Psychological theory is advanced through empirical tests of predictions derived from quantitative cognitive models. As cognitive models are developed and extended they tend to increase in complexity-leading to more precise predictions-which places concomitant demands on the behavioral data used to discriminate between candidate theories. To aid discrimination between cognitive models and, more recently, to constrain parameter estimation, neural data have been used as an adjunct to behavioral data, or as a central stream of information, in the evaluation of cognitive models. Such a model-based neuroscience approach entails many advantages, including precise tests of hypotheses about brain-behavior relationships. There have, however, been few systematic investigations of the capacity for neural data to constrain the recovery of cognitive models. Through the lens of cognitive models of speeded decision-making, we investigated the efficiency of neural data to aid identification of latent cognitive states in models fit to behavioral data. We studied two theoretical frameworks that differed in their assumptions about the composition of the latent generating state. The first assumed that observed performance was generated from a mixture of discrete latent states. The second conceived of the latent state as dynamically varying along a continuous dimension. We used a simulation-based approach to compare recovery of latent data-generating states in neurallyinformed versus neurally-uninformed cognitive models. We found that neurally-informed cognitive models were more reliably recovered under a discrete state representation than a continuous dimension representation for medium effect sizes, although recovery was difficult for small sample sizes and moderate noise in neural data. Recovery improved for both representations when a larger effect size differentiated the latent states. We conclude that neural data aids the identification of latent states in cognitive models, but different frameworks for quantitatively informing cognitive models with neural information have different model recovery efficiencies. We provide full worked examples and freely-available code to implement the two theoretical frameworks.

Estimating across-trial variability parameters of the Diffusion Decision Model: Expert advice and recommendations

“…Finally, in recent years there has been increasing interest in the substantive interpretation of the across-trial variability parameters. For example, several authors have argued that variability in drift rate might be related to mind-wandering (McVay & Kane, 2012;Hawkins, Mittner, Forstmann, & Heathcote, 2017). In these cases, where the across-trial variability parameters themselves are of interest, researchers need to ensure that all possible precautions have been taken to optimize estimation of the across-trial variability parameters (i.e., removal or explicit modeling of outlier RTs, sufficient number of difficulty conditions and trials per participant) before proceeding to interpret their results.…”

A Joint Modelling Approach to Analyze Risky Decisions by Means of Diffusion Tensor Imaging and Behavioural Data

“…where s represents individuals, and f (1) s (resp. f (2) s ) reflects the FA values for the brain tract connections in ACC-Insula-IFG network (resp. dlPFC-Thalamus-Striatum network) for individual s.…”

“…In cognitive neuroscience, relations between neural and behavioural characteristics of individuals are usually analyzed using a two-step approach which first summarizes performances on a given experimental task, and then applies standard statistical analysis on the neural and behavioural measures. However, several studies have highlighted the limitations of this approach in investigating and selecting theories to explain the relation between neural functioning and cognition [1][2][3].…”

Model-based cognitive neuroscience

“…Hawkins, Mittner, Forstmann, and Heathcote (2017) illustrate how neural data can be used to test between cognitive models with different latent states. They focus on whether the underlying states driving performance in a speeded decision tasks are discrete or continuous.…”