Evidence map›Paper›PMID 38280196›Full record

ArticleCell reports2024

Decision-making dynamics are predicted by arousal and uninstructed movements.

Daniel Hulsey, Kevin Zumwalt, Luca Mazzucato, David A McCormick, Santiago Jaramillo

Abstract read
In one paragraph

Article in Cell reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing 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

36 citing papers in PubMed.

  1. Neural and autonomic predictors of future listening errors.bioRxiv : the preprint server for biology · 2026
    Article
  2. Cognitive functions of the GPe.Trends in neurosciences · 2026
    Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Daniel HulseyInstitute of Neuroscience, University of Oregon, Eugene, OR 97405, USA.
Kevin ZumwaltInstitute of Neuroscience, University of Oregon, Eugene, OR 97405, USA.
Luca MazzucatoInstitute of Neuroscience, University of Oregon, Eugene, OR 97405, USA; Department of Biology, University of Oregon, Eugene, OR 97405, USA; Departments of Physics and Mathematics, University of Oregon, Eugene, OR 97405, USA. Electronic address: lmazzuca@uoregon.edu.
David A McCormickInstitute of Neuroscience, University of Oregon, Eugene, OR 97405, USA; Department of Biology, University of Oregon, Eugene, OR 97405, USA. Electronic address: davidmc@uoregon.edu.
Santiago JaramilloInstitute of Neuroscience, University of Oregon, Eugene, OR 97405, USA; Department of Biology, University of Oregon, Eugene, OR 97405, USA. Electronic address: sjara@uoregon.edu.

Funding

Cortical Dynamics and Neural/Behavioral PerformanceR35NS097287 · NINDS · YALE UNIVERSITY · PI MCCORMICK, DAVID · 2017 to 2024
$6.1M
Brain States and Flexible BehaviorRF1NS118461 · NINDS · UNIVERSITY OF OREGON · PI JARAMILLO, SANTIAGO, MCCORMICK, DAVID · 2020 to 2020
$2.4M
CRCNS: A mechanistic theory of serotonergic modulation of cortical processingR01DA055439 · NIDA · UNIVERSITY OF OREGON · PI MAZZUCATO, LUCA, NIELL, CRISTOPHER M · 2021 to 2025
$1.6M
Mechanisms of Rapid Modulation of Auditory ResponsivenessR01DC015803 · NIDCD · YALE UNIVERSITY · PI MCCORMICK, DAVID · 2017 to 2021
$1.6M
Brain States and Flexible BehaviorR01NS118461 · NINDS · UNIVERSITY OF OREGON · PI MCCORMICK, DAVID, NIELL, CRISTOPHER M · 2023 to 2024
$1.4M
NIDA NIH HHS R01 DA055439NIDCD NIH HHS R01 DC015803NINDS NIH HHS R01 NS118461NINDS NIH HHS R35 NS097287NINDS NIH HHS RF1 NS118461
6 · The paper itself

Abstract

During sensory-guided behavior, an animal's decision-making dynamics unfold through sequences of distinct performance states, even while stimulus-reward contingencies remain static. Little is known about the factors that underlie these changes in task performance. We hypothesize that these decision-making dynamics can be predicted by externally observable measures, such as uninstructed movements and changes in arousal. Here, using computational modeling of visual and auditory task performance data from mice, we uncovered lawful relationships between transitions in strategic task performance states and an animal's arousal and uninstructed movements. Using hidden Markov models applied to behavioral choices during sensory discrimination tasks, we find that animals fluctuate between minutes-long optimal, sub-optimal, and disengaged performance states. Optimal state epochs are predicted by intermediate levels, and reduced variability, of pupil diameter and movement. Our results demonstrate that externally observable uninstructed behaviors can predict optimal performance states and suggest that mice regulate their arousal during optimal performance.

Indexed as

ArousalMovementAnimalsComputer SimulationMiceTask Performance and AnalysisarousalauditionCP: Neurosciencedecision-makingregulationstatevision

Identifiers

PMID38280196
PMCPMC11016285

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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.