A Circular Diffusion Model of Continuous-Outcome Source Memory Retrieval

Autor: Zhou, Jason, Osth, Adam, Lilburn, Simon, Smith, Philip
Rok vydání: 2022
Předmět:
DOI: 10.17605/osf.io/p7sxc
Popis: 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 processes, but with trial-to-trial variability in the positive drift process. Comparison of model fits to joint response error and RT data suggest that a memory strength threshold under which no information is retrieved appears to underlie responding in a continuous report source memory task. Additionally, we also conditioned participants’ source responding on their confidence in an old/new recognition task, ruling out the possibility that participant guessing was only due to unrecognized items. Overall, our findings support an all-or-none or some-or none view of source memory retrieval and pose a challenge to continuous models of source memory.
Databáze: OpenAIRE