Reward prediction errors create event boundaries in memory.
Reward prediction errors create event boundaries in memory.
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奖励预测错误会在内存中创建事件边界。
DOI：
10.1016/j.cognition.2020.104269
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发表时间：
2020
期刊：
影响因子：
3.4
通讯作者：
Bornstein,AaronM
中科院分区：
文献类型：
--
作者：
Rouhani,Nina;Norman,KennethA;Niv,Yael;Bornstein,AaronM
我们会记得事情的变化。特别突出的是奖励发生变化的经验，引发奖励预测错误（RPE）。RPE如何影响我们对这些经历的记忆？一种想法是，这种信号直接增强了记忆的编码。另一个并不相互排斥的观点是，视网膜色素上皮（RPE）标志着环境中更深层次的变化，导致后续经历与之前经历的记忆分离，从而创造了一个新的潜在背景和一个更独立的记忆痕迹。我们在四个实验中测试了这一点，参与者学会了预测与一系列试验独特图像相关的奖励。高幅度的RPE表明奖励的潜在分布发生了变化。为了测试这些大的RPE是否创造了一个新的潜在背景，我们首先评估了包括高RPE事件或不包括高RPE事件的顺序对的识别启动（Exp. 1：n= 27 &实验2：n= 83）。我们发现了高RPE事件的识别启动的证据，表明高RPE事件与其在记忆中的前身相结合。鉴于高RPE事件本身优先被记住（鲁哈尼，诺曼和尼夫，2018），我们接下来测试了是否存在跨越高RPE事件的事件边界（即，排除高RPE事件本身; Exp. 3：n= 85）。在这里，连续对跨越高RPE不再显示识别启动，而对在相同的潜在奖励状态，提供了初步的证据RPE调制的事件边界。然后，我们通过要求参与者排序和估计两个事件之间的距离来调查RPE事件边界是否会破坏时间记忆，这两个事件之间要么包括高RPE事件，要么不包括（实验）。4）。我们发现（n= 49）和复制（n= 77）更差的序列记忆的事件在一个高的RPE。与我们的识别启动结果一致，我们没有发现高RPE事件及其前身之间的序列记忆受损，而是发现pairsacrossa高RPE事件的序列记忆更差。此外，编码时事件之间的距离更大，导致低RPE事件中事件的序列记忆更好，但不是高RPE事件，这表明在潜在奖励背景下事件的时间顺序是独立的机制。总而言之，这些发现表明，高RPE事件都更强烈地编码，与其前身显示出完整的联系，并作为中断事件顺序整合的事件边界。我们在上下文维护和检索模型（CMR; Polyn，Norman，& Kahana，2009）的变体中捕获了这些效果，修改后将RPE纳入编码过程。
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 pairsacrossa 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.