A Circular Diffusion Model of Continuous-Outcome Source Memory Retrieval

Abstract

A circular analogue of the diffusion model adapted for continuous response tasks is applied to a continuous-outcome source memory task. In contrast to existing models of source retrieval that attribute all variability in responding to memory, the circular diffusion model decomposes noise into variability arising from memory and decision-making processes. We compare three models: 1) A single diffusion process with trial-to-trial variability in drift rate, 2) A mixture of two diffusion processes, one with positive drift that does not vary from trial-to-trial, and a second zero-drift process that represents discrete guessing, and 3) a hybrid model that also mixes positive and zero-drift process..