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Memory models have typically characterized retrieval in free recall as multi-alternative decision making. However, the majority of these models have only been applied to mean response times (RTs), and have not accounted for the complete RT distributions. We show that RT distributions carry diagnostic information about how items enter into competition for recall, and how that competition impacts on the dynamics of recall. We jointly fit RT distributions and serial position functions of free recall initiation with both a racing diffusion model and the linear ballistic accumulator (LBA: Brown & Heathcote, 2008) model in a hierarchical Bayesian framework while factorially varying different assumptions of how primacy and recency are generated. Recency was either a power law or an exponential function. Primacy was treated either as a strength boost to the early list items so that both primacy and recency items jointly compete to be retrieved, a rehearsal process whereby the first item is sometimes rehearsed to the end of the list to make it functionally recent, or due to reinstatement of the start of the list. While serial position curves do not distinguish between these accounts, they make distinct predictions about how RT distributions vary across serial positions. Results from a number of datasets strongly favor the reinstatement account of primacy with an exponential recency function. These results suggest that models of free recall can be more constrained by considering complete RT distributions.
Model code along with the datasets can be found below in rtPFR.zip
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