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Estimating ACT-R declarative memory parameters using a drift diffusion model

Gillian Grennan
University of Washington ~ Psychology
Prof. Andrea Stocco
University of Washington ~ University of Washington

Accurately fitting cognitive models to empirical datasets requires a robust parameter estimation process which is often arduous and computationally expensive. A way to mitigate this challenge is to integrate participant-specific and efficient mathematical models such as a drift diffusion model (DDM) into the parameter estimation process of cognitive modeling. In this study, we exhibit a clear mapping of the parameters outputted by DDM onto the declarative memory parameters utilized in the cognitive architecture, ACT-R. We show a fairly consistent recovery of simulated ACT-R parameters using DDM and a successful application in using this method to optimize ACT-R simulated fit to an empirical dataset. Notably, we show that the DDM-derived estimated parameters are individualized to the original participant, providing a unique opportunity for parsing out individual differences in cognitive modeling. This method outlined here allows one to estimate ACT-R parameters without the need to manually build and run an ACT-R model while also allowing for neural contextualization of DDM parameters.



Drift Diffusion Model
Cognitive Architecture
Computational models
Brain Architecture
Individual Differences

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Cite this as:

Grennan, G., & Stocco, A. (2022, July). Estimating ACT-R declarative memory parameters using a drift diffusion model. Paper presented at Virtual MathPsych/ICCM 2022. Via