The Reuse-and-Append Memory Principle: Application to Latent Cause Inference
Abstract
The latent cause theory of memory modification provides a computational account of how the brain decides whether to update existing memories or form new ones, but leaves unspecified the neural mechanisms implementing this inference. We propose Reuse-and-Append Memory (RAM), a set of mechanistic principles that achieve the same goal through sparse neural coding, Hebbian learning, and pattern-matching dynamics. The core idea is that neurons encoding prior experiences are automatically reactivated by similar stimuli, while uncommitted neurons are recruited to encode genuinely novel aspects of each experience, including the passage of time. We present a computational model instantiating these principles and show that it reproduces acquisition, extinction, renewal, and spontaneous recovery in fear conditioning. By committing to a neural mechanism, we found that RAM separates what is typically modeled as a single prediction error driving memory updating or formation into two independent signals: an immediate novelty signal that emerges from coverage-based allocation of new neurons, and an outcome mismatch signal that later activates safety circuits when an expected outcome fails to arrive. RAM also reproduces the dependence of recovery on the timing of stimulus reminders (the Monfils-Schiller effect), but predicts it to be inherently fragile, consistent with the mixed empirical record. RAM suggests that the Monfils-Schiller effect, often attributed to reconsolidation, leaves the…
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