Computational models suggest that human memory judgments exhibit interference due to the use of distributed representations

1 Abstract

Episodic memory is a core function that allows us to remember the events of our lives. Given that many events in our life contain overlapping elements (e.g., similar people and places), it is critical to understand how well we can remember the specific events of our lives vs. how susceptible we are to interference between similar memories. Several prominent theories converged on the notion that pattern separation in the hippocampus causes it to play a greater role in processes such as recollection, associative memory, and memory for specific details, while distributed representations in the neocortex cause it to play a more prominent role in domain-specific memory. We propose that studying human memory performance on tasks with targets and similar lures provides a critical testbed for comparing the competing predictions of the role of hippocampal pattern separation vs. more distributed representations in supporting human episodic memory. We generated predictions from competing computational models and then tested these predictions in a large sample of human participants. We found that the comparison between simulated neural responses in an object-processing region of the brain (area IT) and human memory performance exhibited a linear relationship. We also observed strong effects of test format on performance and consistent relationships between test formats. Altogether, our results were better accounted for by distributed memory models than the pattern-separated representations of the hippocampus. Therefore, our results provide an important challenge to prominent theories of human memory and provide an important alternative mechanism for explaining human memory performance.