Evidence map›Paper›PMID 33412101›Full record

ArticleNeuron2021

Extracting the dynamics of behavior in sensory decision-making experiments.

Nicholas A Roy, Ji Hyun Bak, International Brain Laboratory, Athena Akrami, Carlos D Brody, Jonathan W Pillow

Abstract read
In one paragraph

Article in Neuron, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers.

0numbers the graph read from it
0cells of the map it votes in
72citing 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

72 citing papers in PubMed.

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  15. Inferring learning rules during de novo task learning.bioRxiv : the preprint server for biology · 2025
    Article
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  20. Data-driven equation discovery reveals nonlinear reinforcement learning in humans.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article

12 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Nicholas A RoyPrinceton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA. Electronic address: nicholas.roy.42@gmail.com.
Ji Hyun BakKorea Institute for Advanced Study, Seoul 02455, South Korea; Redwood Center for Theoretical Neuroscience, University of California, Berkeley, Berkeley, CA 94720, USA.
International Brain Laboratory
Athena AkramiPrinceton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Sainsbury Wellcome Centre, University College London, London W1T 4JG, UK.
Carlos D BrodyPrinceton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Howard Hughes Medical Institute, Princeton University, Princeton, NJ 08544, USA.
Jonathan W PillowPrinceton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Department of Psychology, Princeton University, Princeton, NJ 08544, USA. Electronic address: pillow@princeton.edu.

Funding

Subcortical Neural Coding and DynamicsU19NS104648 · NINDS · PRINCETON UNIVERSITY · PI BRODY, CARLOS D · 2017 to 2021
$15.3M
Dissecting Sensorimotor Pathways Underlying Social Interactions: Models, Circuits, and BehaviorR01NS104899 · NINDS · PRINCETON UNIVERSITY · PI BIALEK, WILLIAM, MURTHY, MALA · 2018 to 2022
$2.4M
Real-time statistical algorithms for controlling neural dynamics and behaviorR01EB026946 · NIBIB · STATE UNIVERSITY NEW YORK STONY BROOK · PI PARK, IL MEMMING, PILLOW, JONATHAN WILLIAM · 2018 to 2020
$1.1M
Howard Hughes Medical InstituteNIBIB NIH HHS R01 EB026946NINDS NIH HHS R01 NS104899NINDS NIH HHS U19 NS104648Wellcome TrustWellcome Trust 209558Wellcome Trust 216324
6 · The paper itself

Abstract

Decision-making strategies evolve during training and can continue to vary even in well-trained animals. However, studies of sensory decision-making tend to characterize behavior in terms of a fixed psychometric function that is fit only after training is complete. Here, we present PsyTrack, a flexible method for inferring the trajectory of sensory decision-making strategies from choice data. We apply PsyTrack to training data from mice, rats, and human subjects learning to perform auditory and visual decision-making tasks. We show that it successfully captures trial-to-trial fluctuations in the weighting of sensory stimuli, bias, and task-irrelevant covariates such as choice and stimulus history. This analysis reveals dramatic differences in learning across mice and rapid adaptation to changes in task statistics. PsyTrack scales easily to large datasets and offers a powerful tool for quantifying time-varying behavior in a wide variety of animals and tasks.

Indexed as

Acoustic StimulationAdultAnimalsAuditory PerceptionDecision MakingFemaleHumansMaleMiceMice, Inbred C57BLPhotic StimulationPsychomotor PerformanceRatsRats, Long-EvansReaction TimeVisual Perceptionbehavioral dynamicslearningpsychophysicssensory decision making

Identifiers

PMID33412101
PMCPMC7897255

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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.