Humans can learn individual episodes and generalizable rules and also successfully retain both kinds of acquired knowledge over time. In the cognitive science literature, (1) learning individual episodes and rules and (2) learning and remembering are often both conceptualized as competing processes that necessitate separate, complementary learning systems. Inspired by recent research in statistical learning, we challenge these trade-offs, hypothesizing that they arise from capacity limitations rather than from the inherent incompatibility of the underlying cognitive processes. Using an associative learning task, we show that one system with excess representational capacity can learn and remember both episodes and rules.

翻译：人类能够学习具体情景和可泛化的规则，并能长期成功保持这两种习得的知识。在认知科学文献中，(1) 学习具体情景与规则以及(2) 学习与记忆常被概念化为相互竞争的过程，需要独立的互补学习系统。受统计学习最新研究的启发，我们质疑这种权衡关系，提出其源于容量限制而非底层认知过程的内在不相容性。通过关联学习任务，我们证明具有超额表征容量的单一系统能够同时学习并记忆情景与规则。