Sparsity and memory constraints interact with training sequence to bias learning of associative maps.

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not specified in abstract

• Primary Outcome: Blocked learning promotes integration when memory capacity is low, while interleaved learning promotes integration when memory capacity is high

• Nnt Or Sensitivity: Integration especially likely with sparse and distributed neural codes

• Confidence Interval: Not provided

Clinical Application

Requires empirical validation before clinical implementation; computational framework provides foundation for intervention design Relevant to Australian neuropsychology and cognitive rehabilitation services; could inform memory training protocols in aged care and neurological rehabilitation settings Individuals with varying memory capacities across the lifespan, particularly those with memory impairments

Abstract

Cognitive maps support inference and planning by representing associations between experiences encoded in memory. These map-like representations are thought to carry information not only about directly observed links but also about longer paths. The ability to make judgments based on multi-step associations varies with one's experience in an environment and with changes in memory abilities across the lifespan. However, it remains unclear exactly how representations of associative structure are influenced by learning curricula and memory constraints. Prior studies have suggested a tradeoff: memory representations can either be more integrated to improve inference, or more separated to recall distinct direct associations. Whether overlapping associations are experienced nearby in time (interleaved) or spaced apart (blocked) can bias memory representations toward integration or separation. However, key recent findings about how blocked versus interleaved experience bias integration or separation have been inconsistent. Here, we introduce a computational framework that helps reconcile these apparent discrepancies. Using neural network simulations of three separate memory-guided inference tasks, we show that variations in memory capacity and the sparsity of neural codes interact with learning sequence to shape network representations. Specifically, blocked learning promotes integration when memory capacity is low, while interleaved learning promotes integration when memory capacity is high. Integration is especially likely to result from representations formed when neural codes are both sparse and distributed. These results offer a principled computational account of how flexible, map-like representations can arise from experience and suggest avenues for individualized memory interventions to improve inference, generalization, and planning.

References

1. 1.Noh, S. M., Zhou, D., Cooper, K. W., Guo, S., Dinh, E. T., & Bornstein, A. M. (2021). Sparsity and memory constraints interact with training sequence to bias learning of associative maps. Journal of Neuroscience, 41(19), 4234-4247. https://doi.org/10.1523/JNEUROSCI.2875-20.2021