Modeling human memory with task-trained neural networks

Human memory is prone to multiple well-documented forgetting and confusion phenomena. We ask whether these effects also emerge from training general-purpose memory architectures, and what role architecture, training tasks and input statistics each play. We introduce a framework spanning classical cognitive models and modern machine learning architectures, built around a recurrent working memory and an associative episodic memory. Six representative models are trained on sequences with tunable input statistics (semantic structure, temporal structure, partial observability), using recognition, reconstruction and sequential recall tasks. They are tested on standard paradigms in human memory research to assess the qualitative emergence of nine phenomena. We find that some effects always emerge regardless of architecture or training regime (perceptual similarity, conjunction errors, fan effect, set size); semantic false memories emerge specifically from input statistics; and other phenomena emerge from either architecture, training tasks, or input statistics (contiguity, primacy, recency, repetition). Notably, the presence of these phenomena does not appear to depend strongly on performance: most of them persist even when accuracy is close to 1. Our results suggest that some human memory phenomena may be understood as adaptations to environmental constraints, and we provide concrete candidates for what such constraints may look like in a simplified setting. More broadly, our work shows how task-trained models can complement classical cognitive models, as tools to capture many phenomena at once and explore the conditions for their emergence.