Signed and unsigned reward prediction errors dynamically enhance learning and memory.
Signed and unsigned reward prediction errors dynamically enhance learning and memory.
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作者：
Rouhani N;Niv Y
记忆有助于指导行为，但过去的哪些经历是优先考虑的呢？经典的学习模型认为，与不可预测的结果相关的事件，以及与可预测的结果相矛盾的事件，将更多的注意力和学习集中在这些事件上。在这里，我们测试了这些事件的强化学习和后续记忆，并将在奖励预测线索或奖励结果上经历的有符号和无符号奖励预测错误（rpe）视为这两个看似矛盾的信号的驱动因素。通过将强化学习模型拟合到行为中，我们发现两种rpe都通过调节动态变化的学习率来促进学习。我们进一步表征了这些RPE信号对记忆的影响，并表明有符号和无符号的RPE信号都能增强记忆，这与中脑多巴胺和海马体可塑性的蓝斑调节一致，从而与文献中单独的发现相一致。
Memory helps guide behavior, but which experiences from the past are prioritized? Classic models of learning posit that events associated with unpredictable outcomes as well as, paradoxically, predictable outcomes, deploy more attention and learning for those events. Here, we test reinforcement learning and subsequent memory for those events, and treat signed and unsigned reward prediction errors (RPEs), experienced at the reward-predictive cue or reward outcome, as drivers of these two seemingly contradictory signals. By fitting reinforcement learning models to behavior, we find that both RPEs contribute to learning by modulating a dynamically changing learning rate. We further characterize the effects of these RPE signals on memory and show that both signed and unsigned RPEs enhance memory, in line with midbrain dopamine and locus-coeruleus modulation of hippocampal plasticity, thereby reconciling separate findings in the literature.