December 12, 2023 REC GS.08
Drift-Diffusion Models (DDMs) are a widely used class of models that assume an accumulation of evidence during a quick decision. These models are often used as measurement models to assess individual differences in cognitive processes, such as an individual’s evidence accumulation rate and response caution. An additional underlying assumption of these models is that there is internal noise in the evidence accumulation process. In fact, individual differences in internal noise are often a more parsimonious explanation than individual differences in both response caution and evidence accumulation rate. However, fitting DDMs to experimental choice-response time data cannot yield estimates of an individual’s evidence accumulation rate, caution, and internal noise at the same time. This is due to an intrinsic joint-unidentifiability of these parameters when fitting DDMs to behavioral data. I introduce methods of estimating these parameters at the same time. Specifically, additional observed covariates, such as electroencephalographic (EEG) measures, can reasonably be assumed to be related to model parameters. I show parameter recovery for so-called neurocognitive models with true (simulated) connections to such additional covariates, as well as some recovery in misspecified models. I show this with both single-trial and participant-level covariates. The methods to estimate model parameters rely on Bayesian inference and often simulation-based Bayesian inference estimated with Artificial Neural Networks. I show why these methods are useful without making strong assumptions and how they can also explain noisy EEG measures. This work exemplifies estimation of unidentifiable cognitive model parameters with additional data. I discuss the future of these methods and why I think they are necessary for the fields of Cognitive Psychology, Cognitive Neuroscience, and Cognitive AI.