A neural network model of free recall learns multiple memory strategies

TL;DR - Recurrent neural networks trained for free recall independently learn several human-like memory strategies. The strongest models develop an index-based mechanism resembling the memory palace technique, extending beyond classical temporal context theories.

• The study optimizes recurrent neural networks specifically for free-recall tasks.
• Trained models discover diverse recall strategies rather than one fixed mechanism.
• Top-performing networks use index-based memory organization.
• The findings offer computational insight into how sophisticated mnemonic strategies can emerge through learning.