Evidence map›Paper›PMID 36711889›Full record

ArticlebioRxiv : the preprint server for biology2023

Comparing rapid rule-learning strategies in humans and monkeys.

Vishwa Goudar, Jeong-Woo Kim, Yue Liu, Adam J O Dede, Michael J Jutras, Ivan Skelin, Michael Ruvalcaba, William Chang, Adrienne L Fairhall, Jack J Lin and 3 more

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. 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, 5 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors at 4 institutions in 1 country.

Vishwa GoudarCenter for Neural Science, New York University, NY, USA.
Jeong-Woo KimCenter for Neural Science, New York University, NY, USA.
Yue LiuCenter for Neural Science, New York University, NY, USA.
Adam J O DedeDepartment of Physiology and Biophysics, University of Washington, Seattle, WA, USA.
Michael J JutrasDepartment of Physiology and Biophysics, University of Washington, Seattle, WA, USA.
Ivan SkelinDepartment of Neurology, University of California, Davis, Davis, CA, USA.
Michael RuvalcabaHelen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA.
William ChangHelen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA.
Adrienne L FairhallDepartment of Physiology and Biophysics, University of Washington, Seattle, WA, USA.
Jack J LinDepartment of Neurology, University of California, Davis, Davis, CA, USA.
Robert T KnightDepartment of Psychology, University of California Berkeley, Berkeley, CA, USA.
Elizabeth A BuffaloDepartment of Physiology and Biophysics, University of Washington, Seattle, WA, USA.
Xiao-Jing WangCenter for Neural Science, New York University, NY, USA.
New York University · USUniversity of Washington · USUniversity of California, Berkeley · USUniversity of California, Davis · US

Funding

Neural circuit theory and trained recurrent network modeling of rapid learningU19NS107609 · NINDS · UNIVERSITY OF WASHINGTON · PI BUFFALO, ELIZABETH A · 2018 to 2022
$12.6M
Recurrent Neual Circuit Basis of Time Integration and Decision MakingR01MH062349 · NIMH · YALE UNIVERSITY · PI WANG, XIAO-JING · 2001 to 2022
$7.2M
NIMH NIH HHS R01 MH062349NINDS NIH HHS U19 NS107609
6 · The paper itself

Abstract

Inter-species comparisons are key to deriving an understanding of the behavioral and neural correlates of human cognition from animal models. We perform a detailed comparison of macaque monkey and human strategies on an analogue of the Wisconsin Card Sort Test, a widely studied and applied multi-attribute measure of cognitive function, wherein performance requires the inference of a changing rule given ambiguous feedback. We found that well-trained monkeys rapidly infer rules but are three times slower than humans. Model fits to their choices revealed hidden states akin to feature-based attention in both species, and decision processes that resembled a Win-stay lose-shift strategy with key differences. Monkeys and humans test multiple rule hypotheses over a series of rule-search trials and perform inference-like computations to exclude candidates. An attention-set based learning stage categorization revealed that perseveration, random exploration and poor sensitivity to negative feedback explain the under-performance in monkeys.

Identifiers

PMID36711889
PMCPMC9882042
OpenAlexW4315471038

What OpenQuestion holds

Textmetadata
LicenceCC BY-ND
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.