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Poster Session A: Tuesday, August 12, 1:30 – 4:30 pm, de Brug & E‑Hall

Emergence of the Primacy Effect in Structured State-Space Models

Takashi Morita¹; ¹Chubu University

Presenter: Takashi Morita

Human and animal memory for sequentially presented items is well-documented to be more accurate for those at the beginning and end of a sequence, phenomena known as the *primacy* and *recency* effects, respectively. By contrast, artificial neural network (ANN) models are typically designed with a memory that decays monotonically over time. Accordingly, ANNs are expected to show the *recency* effect but not the *primacy* effect. Contrary to this theoretical expectation, however, the present study reveals a counterintuitive finding: a recently developed ANN architecture, called *structured state-space models*, exhibits the primacy effect when trained and evaluated on a synthetic task that mirrors psychological memory experiments. Given that this model was originally designed for recovering neuronal activity patterns observed in biological brains, this result provides a novel perspective on the psychological primacy effect while also posing a non-trivial puzzle for the current theories in machine learning.

Topic Area: Memory, Spatial Cognition & Skill Learning

Extended Abstract: Full Text PDF