Capturing dynamic performance in a cognitive model: Estimating ACT-R memory parameters with the linear ballistic accumulator
The parameters governing our behaviour are in constant flux. Accurately capturing these dynamics in cognitive models poses a challenge to modellers. Here, we demonstrate a mapping of ACT-R's declarative memory onto the linear ballistic accumulator, a mathematical model describing a competition between evidence accumulation processes. We show that this mapping provides a method for inferring individual ACT-R parameters without requiring the modeller to build and fit an entire ACT-R model. We conduct a parameter recovery study to confirm that the LBA can recover ACT-R parameters from simulated data. Then, as a proof of concept, we use the LBA to estimate ACT-R parameters from an empirical data set. The resulting parameter estimates provide a cognitively meaningful explanation for observed differences in behaviour over time and between individuals.
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