Rouhani Nina, Norman Kenneth A, Niv Yael, Bornstein Aaron M
Princeton Neuroscience Institute, Princeton University, United States of America; Department of Psychology, Princeton University, United States of America.
Princeton Neuroscience Institute, Princeton University, United States of America; Department of Psychology, Princeton University, United States of America.
Cognition. 2020 Oct;203:104269. doi: 10.1016/j.cognition.2020.104269. Epub 2020 Jun 17.
We remember when things change. Particularly salient are experiences where there is a change in rewards, eliciting reward prediction errors (RPEs). How do RPEs influence our memory of those experiences? One idea is that this signal directly enhances the encoding of memory. Another, not mutually exclusive, idea is that the RPE signals a deeper change in the environment, leading to the mnemonic separation of subsequent experiences from what came before, thereby creating a new latent context and a more separate memory trace. We tested this in four experiments where participants learned to predict rewards associated with a series of trial-unique images. High-magnitude RPEs indicated a change in the underlying distribution of rewards. To test whether these large RPEs created a new latent context, we first assessed recognition priming for sequential pairs that included a high-RPE event or not (Exp. 1: n = 27 & Exp. 2: n = 83). We found evidence of recognition priming for the high-RPE event, indicating that the high-RPE event is bound to its predecessor in memory. Given that high-RPE events are themselves preferentially remembered (Rouhani, Norman, & Niv, 2018), we next tested whether there was an event boundary across a high-RPE event (i.e., excluding the high-RPE event itself; Exp. 3: n = 85). Here, sequential pairs across a high RPE no longer showed recognition priming whereas pairs within the same latent reward state did, providing initial evidence for an RPE-modulated event boundary. We then investigated whether RPE event boundaries disrupt temporal memory by asking participants to order and estimate the distance between two events that had either included a high-RPE event between them or not (Exp. 4). We found (n = 49) and replicated (n = 77) worse sequence memory for events across a high RPE. In line with our recognition priming results, we did not find sequence memory to be impaired between the high-RPE event and its predecessor, but instead found worse sequence memory for pairs across a high-RPE event. Moreover, greater distance between events at encoding led to better sequence memory for events across a low-RPE event, but not a high-RPE event, suggesting separate mechanisms for the temporal ordering of events within versus across a latent reward context. Altogether, these findings demonstrate that high-RPE events are both more strongly encoded, show intact links with their predecessor, and act as event boundaries that interrupt the sequential integration of events. We captured these effects in a variant of the Context Maintenance and Retrieval model (CMR; Polyn, Norman, & Kahana, 2009), modified to incorporate RPEs into the encoding process.
我们记得事情发生变化的时候。尤其显著的是奖励发生变化的经历，会引发奖励预测误差（RPEs）。奖励预测误差如何影响我们对这些经历的记忆呢？一种观点认为，这个信号直接增强了记忆的编码。另一种并非相互排斥的观点是，奖励预测误差标志着环境中更深层次的变化，导致后续经历与之前的经历在记忆上分离，从而创建一个新的潜在情境和一个更独立的记忆痕迹。我们在四个实验中对此进行了测试，实验中参与者学习预测与一系列试验中独有的图像相关的奖励。高强度的奖励预测误差表明奖励的潜在分布发生了变化。为了测试这些大的奖励预测误差是否创建了一个新的潜在情境，我们首先评估了对包含或不包含高奖励预测误差事件的连续图像对的识别启动效应（实验1：n = 27；实验2：n = 83）。我们发现了对高奖励预测误差事件的识别启动效应的证据，表明高奖励预测误差事件在记忆中与它的前一个事件相关联。鉴于高奖励预测误差事件本身更容易被记住（鲁哈尼、诺曼和尼夫，2018），接下来我们测试在高奖励预测误差事件处是否存在事件边界（即不包括高奖励预测误差事件本身；实验3：n = 85）。在这里，跨越高奖励预测误差的连续图像对不再显示识别启动效应，而处于相同潜在奖励状态内的图像对则显示，这为奖励预测误差调节的事件边界提供了初步证据。然后，我们通过要求参与者对两个事件进行排序并估计它们之间的距离来研究奖励预测误差事件边界是否会干扰时间记忆，这两个事件之间要么包含一个高奖励预测误差事件，要么不包含（实验4）。我们发现（n = 49）并重复验证（n = 77），对于跨越高奖励预测误差的事件，序列记忆更差。与我们的识别启动效应结果一致，我们没有发现高奖励预测误差事件与其前一个事件之间的序列记忆受损，而是发现跨越高奖励预测误差事件的图像对的序列记忆更差。此外，编码时事件之间的距离越大，对于跨越低奖励预测误差事件的事件，序列记忆越好，但对于跨越高奖励预测误差事件的事件则不然，这表明在潜在奖励情境内和跨潜在奖励情境的事件时间排序存在不同机制。总之，这些发现表明，高奖励预测误差事件编码更强，与它们的前一个事件有完整的联系，并且作为事件边界中断事件的顺序整合。我们在情境维持与检索模型（CMR；波利恩、诺曼和卡哈纳，2009）的一个变体中捕捉到了这些效应，该模型经过修改，将奖励预测误差纳入了编码过程。
