Evidence map›Paper›PMID 32563083›Full record

ReviewCognition2020

Reward prediction errors create event boundaries in memory.

Nina Rouhani, Kenneth A Norman, Yael Niv, Aaron M Bornstein

Abstract readReview
In one paragraph

Review in Cognition, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.

0numbers the graph read from it
0cells of the map it votes in
58citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

58 citing papers in PubMed.

  1. Effects of Initial Experiences on Risky Choice.Quarterly journal of experimental psychology (2006) · 2026
    Article
  2. Article
  3. Article
  4. Confidence in naturalistic decision making.Neuroscience of consciousness · 2026
    Article
  5. Article
  6. Neural Representation of Episodic Time.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Review
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. The Ubiquity of Time in Latent-cause Inference.Journal of cognitive neuroscience · 2024
    Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Nina RouhaniPrinceton Neuroscience Institute, Princeton University, United States of America; Department of Psychology, Princeton University, United States of America. Electronic address: ninarouhani@gmail.com.
Kenneth A NormanPrinceton Neuroscience Institute, Princeton University, United States of America; Department of Psychology, Princeton University, United States of America.
Yael NivPrinceton Neuroscience Institute, Princeton University, United States of America; Department of Psychology, Princeton University, United States of America.
Aaron M BornsteinDepartment of Cognitive Sciences and Center for the Neurobiology of Learning and Memory, University of California, Irvine, United States of America.

Funding

Neural and computational mechanisms of selective attention in experience-based deR01MH098861 · NIMH · PRINCETON UNIVERSITY · PI NIV, YAEL · 2012 to 2014
$1.1M
Quantifying the latent-cause inference process in humansR21MH120798 · NIMH · PRINCETON UNIVERSITY · PI NIV, YAEL · 2019 to 2020
$444k
NIMH NIH HHS R01 MH098861NIMH NIH HHS R21 MH120798
6 · The paper itself

Abstract

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.

Indexed as

MemoryRewardHumansLearningComputational modelContextDistance memoryEvent boundariesMemoryRecognition primingReinforcement learningReward prediction errorsSequence memory

Identifiers

PMID32563083
PMCPMC7483902

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.