Evidence map›Paper›PMID 28653668›Full record

ArticleNature communications2017

Reminders of past choices bias decisions for reward in humans.

Aaron M Bornstein, Mel W Khaw, Daphna Shohamy, Nathaniel D Daw

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 95 papers.

0numbers the graph read from it
0cells of the map it votes in
95citing papers in PubMed
11.9field-weighted citation impact, top 1% of its field
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

95 citing papers in PubMed, 253 citations in OpenAlex.

  1. Article
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  8. Data-driven equation discovery reveals nonlinear reinforcement learning in humans.Proceedings of the National Academy of Sciences of the United States of America · 2025
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  10. Differentiating Reinforcement Learning and Episodic Memory in Value-Based Decisions in Parkinson's Disease.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
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  11. Article
  12. Contributions of Attention to Learning in Multidimensional Reward Environments.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
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  13. Article
  14. Reinforcement-Learning-Informed Queries Guide Behavioral Change.Clinical psychological science : a journal of the Association for Psychological Science · 2024
    Article
  15. Article
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  18. The role of episodic memory sampling in evaluation.Psychonomic bulletin & review · 2024
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  19. How do humans learn about the reliability of automation?Cognitive research: principles and implications · 2024
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  20. Causal learning with delays up to 21 hours.Psychonomic bulletin & review · 2024
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35 more citing papers are in PubMed but not listed here.

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 at 3 institutions in 1 country.

Aaron M BornsteinNeuroscience Institute, Princeton University, Washington Road, Princeton, New Jersey 08544, USA.ORCID 0000-0001-6251-6000
Mel W KhawDepartment of Economics, Columbia University, New York, New York 10027, USA.
Daphna ShohamyDepartment of Psychology and Zuckerman Mind, Brain, Behavior Institute, New York, New York 10027, USA.
Nathaniel D DawNeuroscience Institute, Princeton University, Washington Road, Princeton, New Jersey 08544, USA.ORCID 0000-0001-5029-1430
Princeton University · USAllen Institute for Brain Science · USColumbia University · US

Funding

CRCNS: Computational and neural mechanisms of memory-guided decisionsR01DA038891 · NIDA · PRINCETON UNIVERSITY · PI DAW, NATHANIEL DOUGLASS, SHOHAMY, DAPHNA · 2014 to 2018
$1.7M
Goals vs. habits in the human brain: Cognitive and computational mechanismsR01NS078784 · NINDS · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI SHOHAMY, DAPHNA · 2011 to 2014
$1.5M
Computational mechanisms of goal-directed controlF31MH095501 · NIMH · NEW YORK UNIVERSITY · PI BORNSTEIN, AARON MICHAEL · 2011 to 2012
$70k
NIDA NIH HHS R01 DA038891NIMH NIH HHS F31 MH095501NINDS NIH HHS R01 NS078784
6 · The paper itself

Abstract

We provide evidence that decisions are made by consulting memories for individual past experiences, and that this process can be biased in favour of past choices using incidental reminders. First, in a standard rewarded choice task, we show that a model that estimates value at decision-time using individual samples of past outcomes fits choices and decision-related neural activity better than a canonical incremental learning model. In a second experiment, we bias this sampling process by incidentally reminding participants of individual past decisions. The next decision after a reminder shows a strong influence of the action taken and value received on the reminded trial. These results provide new empirical support for a decision architecture that relies on samples of individual past choice episodes rather than incrementally averaged rewards in evaluating options and has suggestive implications for the underlying cognitive and neural mechanisms.

Indexed as

Choice BehaviorRewardAdolescentAdultBrainDecision MakingFemaleHumansLearningMagnetic Resonance ImagingMaleYoung Adult

Identifiers

PMID28653668
PMCPMC5490260
OpenAlexW2731341786

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.