Evidence map›Paper›PMID 39422555›Full record

ArticleeLife2024

Direct observation of the neural computations underlying a single decision.

Natalie Steinemann, Gabriel M Stine, Eric Trautmann, Ariel Zylberberg, Daniel M Wolpert, 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 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing 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

41 citing papers in PubMed.

  1. Trial
  2. Article
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  7. Article
  8. Integration of Memory and Sensory Information in Skilled Sequence Production.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026
    Article
  9. Article
  10. Review
  11. Cortex-wide Dynamics of Internal Decisions About Behavioral Context.bioRxiv : the preprint server for biology · 2026
    Article
  12. Article
  13. Article
  14. Article
  15. Neural mechanisms of flexible perceptual inference.PLoS computational biology · 2025
    Article
  16. Article
  17. No Central Executive? Decision Formation Through Multi-Area Population Dynamics.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Review
  18. Brain-wide coordination of internal signals during decision-making.bioRxiv : the preprint server for biology · 2025
    Article
  19. Article
  20. Article
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

6 authors.

Natalie Steinemann *Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, United States.
Gabriel M Stine *Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, United States.ORCID https://orcid.org/0000-0003-4906-0461
Eric TrautmannZuckerman Mind Brain and Behavior Institute, Columbia University, New York, United States.
Ariel Zylberberg *Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, United States.ORCID https://orcid.org/0000-0002-2572-4748
Daniel M Wolpert *Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, United States.ORCID https://orcid.org/0000-0003-2011-2790
Michael N Shadlen *Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, United States.ORCID https://orcid.org/0000-0002-2002-2210

Funding

Vision Sciences Training GrantT32EY013933 · NEI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Carol A. Mason, CAROL M TROY · 2001 to 2026
$5.3M
Computational and circuit mechanisms of decision makingR01NS113113 · NINDS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI SHADLEN, MICHAEL NEIL · 2019 to 2023
$2.2M
Parieto-collicular interactions during perceptual decisionsF31EY032791 · NEI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI STINE, GABRIEL M · 2021 to 2021
$36k
Grossman Center Zuckerman InstituteNational Institutes of Health BRAIN Initiative R01NS113113NEI NIH HHS F31 EY032791NEI NIH HHS T32 EY013933NINDS NIH HHS R01 NS113113
6 · The paper itself

Abstract

Neurobiological investigations of perceptual decision-making have furnished the first glimpse of a flexible cognitive process at the level of single neurons. Neurons in the parietal and prefrontal cortex are thought to represent the accumulation of noisy evidence, acquired over time, leading to a decision. Neural recordings averaged over many decisions have provided support for the deterministic rise in activity to a termination bound. Critically, it is the unobserved stochastic component that is thought to confer variability in both choice and decision time. Here, we elucidate this drift-diffusion signal on individual decisions. We recorded simultaneously from hundreds of neurons in the lateral intraparietal cortex of monkeys while they made decisions about the direction of random dot motion. We show that a single scalar quantity, derived from the weighted sum of the population activity, represents a combination of deterministic drift and stochastic diffusion. Moreover, we provide direct support for the hypothesis that this drift-diffusion signal approximates the quantity responsible for the variability in choice and reaction times. The population-derived signals rely on a small subset of neurons with response fields that overlap the choice targets. These neurons represent the integral of noisy evidence. Another subset of direction-selective neurons with response fields that overlap the motion stimulus appear to represent the integrand. This parsimonious architecture would escape detection by state-space analyses, absent a clear hypothesis.

Indexed as

Decision MakingNeuronsParietal LobeAnimalsMacaca mulattaMaleModels, NeurologicalMotion PerceptionReaction Timedecision-makingdrift-diffusionneuropixelsneuroscienceparietal cortexpopulation codereaction timerhesus macaque

Identifiers

PMID39422555
PMCPMC11488853

What OpenQuestion holds

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Registered trials

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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.