Dynamic noise estimation: A generalized method for modeling noise fluctuations in decision-making.

Jing-Jing Li, Chengchun Shi, Lexin Li, Anne G E Collins

Open access · greenAbstract readPreprint

In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

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

4 authors at 2 institutions in 2 countries.

Jing-Jing LiHelen Wills Neuroscience Institute, University of California, Berkeley, 175 Li Ka Shing Center, Berkeley, 94720, CA, United States.ORCID 0000-0001-5230-1049

Chengchun ShiDepartment of Statistics, London School of Economics and Political Science, 69 Aldwych, London, WC2B 4RR, United Kingdom.

Lexin LiHelen Wills Neuroscience Institute, University of California, Berkeley, 175 Li Ka Shing Center, Berkeley, 94720, CA, United States.

Anne G E CollinsHelen Wills Neuroscience Institute, University of California, Berkeley, 175 Li Ka Shing Center, Berkeley, 94720, CA, United States.ORCID 0000-0003-3751-3662

University of California, Berkeley · USLondon School of Economics and Political Science · GB

Funding

The neural computations supporting hierarchical reinforcement learningR01MH119383 · NIMH · UNIVERSITY OF CALIFORNIA BERKELEY · PI COLLINS, ANNE G.E. · 2019 to 2023

$1.9M

NIMH NIH HHS R01 MH119383

6 · The paper itself

Abstract

Computational cognitive modeling is an important tool for understanding the processes supporting human and animal decision-making. Choice data in decision-making tasks are inherently noisy, and separating noise from signal can improve the quality of computational modeling. Common approaches to model decision noise often assume constant levels of noise or exploration throughout learning (e.g., the

Indexed as

attentioncognitive modelingdecision-makingdecision noisehidden Markov modellapsesreinforcement learningtask engagement

Identifiers

PMID38328176

DOI10.1101/2023.06.19.545524

PMCPMC10849494

OpenAlexW4382042809

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.