Gershman Samuel J, Monfils Marie-H, Norman Kenneth A, Niv Yael
Department of Psychology and Center for Brain Science, Harvard University, Cambridge, United States.
Department of Psychology, University of Texas, Austin, United States.
Elife. 2017 Mar 15;6:e23763. doi: 10.7554/eLife.23763.
Retrieving a memory can modify its influence on subsequent behavior. We develop a computational theory of memory modification, according to which modification of a memory trace occurs through classical associative learning, but which memory trace is eligible for modification depends on a structure learning mechanism that discovers the units of association by segmenting the stream of experience into statistically distinct clusters (latent causes). New memories are formed when the structure learning mechanism infers that a new latent cause underlies current sensory observations. By the same token, old memories are modified when old and new sensory observations are inferred to have been generated by the same latent cause. We derive this framework from probabilistic principles, and present a computational implementation. Simulations demonstrate that our model can reproduce the major experimental findings from studies of memory modification in the Pavlovian conditioning literature.
提取记忆会改变其对后续行为的影响。我们提出了一种记忆修改的计算理论，根据该理论，记忆痕迹的修改通过经典联想学习发生，但哪些记忆痕迹有资格被修改取决于一种结构学习机制，该机制通过将经验流分割成统计上不同的簇（潜在原因）来发现联想单元。当结构学习机制推断出一个新的潜在原因是当前感官观察的基础时，就会形成新的记忆。同样，当推断新旧感官观察是由相同的潜在原因产生时，旧记忆就会被修改。我们从概率原理推导出这个框架，并给出了一个计算实现。模拟结果表明，我们的模型可以重现巴甫洛夫条件反射文献中记忆修改研究的主要实验结果。
