Evidence map›Paper›PMID 39656196›Full record

ArticleeLife2024

Value construction through sequential sampling explains serial dependencies in decision making.

Ariel Zylberberg, Akram Bakkour, Daphna Shohamy, Michael N Shadlen

Abstract read
In one paragraph

Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Ariel ZylberbergMortimer B Zuckerman Mind Brain Behavior Institute, Columbia University, New York, United States.ORCID https://orcid.org/0000-0002-2572-4748
Akram BakkourMortimer B Zuckerman Mind Brain Behavior Institute, Columbia University, New York, United States.
Daphna Shohamy *Mortimer B Zuckerman Mind Brain Behavior Institute, Columbia University, New York, United States.
Michael N Shadlen *Mortimer B Zuckerman Mind Brain Behavior Institute, Columbia University, New York, United States.ORCID https://orcid.org/0000-0002-2002-2210

Funding

Computational and circuit mechanisms of decision makingR01NS113113 · NINDS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI SHADLEN, MICHAEL NEIL · 2019 to 2023
$2.2M
Differentiating reward seeking and loss avoidance with reference-dependent learning modelsR01MH121093 · NIMH · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI DAW, NATHANIEL DOUGLASS, SHOHAMY, DAPHNA · 2019 to 2022
$2.1M
Air Force Office of Scientific Research FA9550-22-1-0337National Science Foundation 1606916National Science Foundation 1822619NIH HHS MH121093NIH HHS R01NS113113NIMH NIH HHS R01 MH121093NINDS NIH HHS R01 NS113113
6 · The paper itself

Abstract

Deciding between a pair of familiar items is thought to rely on a comparison of their subjective values. When the values are similar, decisions take longer, and the choice may be inconsistent with stated value. These regularities are thought to be explained by the same mechanism of noisy evidence accumulation that leads to perceptual errors under conditions of low signal to noise. However, unlike perceptual decisions, subjective values may vary with internal states (e.g. desires, priorities) that change over time. This raises the possibility that the apparent stochasticity of choice reflects changes in value rather than mere noise. We hypothesized that these changes would manifest in serial dependencies across decision sequences. We analyzed data from a task in which participants chose between snack items. We developed an algorithm,

Indexed as

Decision MakingAdultAlgorithmsChoice BehaviorFemaleHumansMagnetic Resonance ImagingMalePrefrontal CortexReaction TimeYoung Adultchoice-induced preference changedecision makingdrift-diffusion modelfood-choice taskhumanneurosciencevalue-based decisionsvmPFC

Identifiers

PMID39656196
PMCPMC11630821

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.