Evidence map›Paper›PMID 38569230›Full record

ReviewCurrent opinion in neurobiology2024

From innate to instructed: A new look at perceptual decision-making.

Lukas T Oesch, Michael B Ryan, Anne K Churchland

Abstract readReview
In one paragraph

Review in Current opinion in neurobiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Neural correlates of perceptual decision-making in the primary somatosensory cortex.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. 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

3 authors.

Lukas T OeschDepartment of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States.
Michael B RyanDepartment of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States. Electronic address: https://twitter.com/NeuroMikeRyan.
Anne K ChurchlandDepartment of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States. Electronic address: achurchland@mednet.ucla.edu.

Funding

State-dependent Decision-making in Brainwide Neural CircuitsU19NS123716 · NINDS · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI CHURCHLAND, ANNE KATHRYN · 2021 to 2025
$18.2M
The role of parietal cortex in multisensory decision-makingR01EY022979 · NEI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ANNE KATHRYN CHURCHLAND · 2013 to 2026
$5.7M
NEI NIH HHS R01 EY022979NINDS NIH HHS U19 NS123716
6 · The paper itself

Abstract

Understanding how subjects perceive sensory stimuli in their environment and use this information to guide appropriate actions is a major challenge in neuroscience. To study perceptual decision-making in animals, researchers use tasks that either probe spontaneous responses to stimuli (often described as "naturalistic") or train animals to associate stimuli with experimenter-defined responses. Spontaneous decisions rely on animals' pre-existing knowledge, while trained tasks offer greater versatility, albeit often at the cost of extensive training. Here, we review emerging approaches to investigate perceptual decision-making using both spontaneous and trained behaviors, highlighting their strengths and limitations. Additionally, we propose how trained decision-making tasks could be improved to achieve faster learning and a more generalizable understanding of task rules.

Indexed as

Decision MakingPerceptionAnimalsHumansLearning

Identifiers

PMID38569230
PMCPMC11162954

What OpenQuestion holds

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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.