Evidence map›Paper›PMID 31619495›Full record

ReviewThe Journal of neuroscience : the official journal of the Society for Neuroscience2019

Brain Mechanisms of Concept Learning.

Dagmar Zeithamova, Michael L Mack, Kurt Braunlich, Tyler Davis, Carol A Seger, Marlieke T R van Kesteren, Andreas Wutz

Abstract readReview
In one paragraph

Review in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing 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

30 citing papers in PubMed.

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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

7 authors.

Dagmar ZeithamovaDepartment of Psychology and Institute of Neuroscience, University of Oregon, Eugene, Oregon 97403, dasa@uoregon.edu mack@psych.utoronto.ca.ORCID 0000-0002-0310-5030
Michael L MackDepartment of Psychology, University of Toronto, Toronto, Ontario M5S 3G3, Canada, dasa@uoregon.edu mack@psych.utoronto.ca.ORCID 0000-0001-5155-7413
Kurt BraunlichDepartment of Psychology and Program in Molecular, Cellular, and Integrative Neurosciences, Colorado State University, Fort Collins, Colorado 80523.
Tyler DavisDepartment of Psychological Sciences, Texas Tech University, Lubbock, Texas 79403.
Carol A SegerCenter for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou 510631, China.
Marlieke T R van KesterenSection of Education Sciences and LEARN! Research Institute, Vrije Universiteit Amsterdam, Amsterdam 1081 BT, The Netherlands.ORCID 0000-0001-6765-7881
Andreas WutzThe Picower Institute for Learning & Memory and Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139.

Funding

Neural Basis of CategoriesR01MH065252 · NIMH · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI MILLER, EARL K · 2002 to 2016
$5.7M
Cortical Circuits for Attention and DecisionsR37MH087027 · NIMH · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI MILLER, EARL K · 2010 to 2019
$4.7M
Austrian Science Fund FWF M 2496NIMH NIH HHS R01 MH065252NIMH NIH HHS R37 MH087027
6 · The paper itself

Abstract

Concept learning, the ability to extract commonalities and highlight distinctions across a set of related experiences to build organized knowledge, is a critical aspect of cognition. Previous reviews have focused on concept learning research as a means for dissociating multiple brain systems. The current review surveys recent work that uses novel analytical approaches, including the combination of computational modeling with neural measures, focused on testing theories of specific computations and representations that contribute to concept learning. We discuss in detail the roles of the hippocampus, ventromedial prefrontal, lateral prefrontal, and lateral parietal cortices, and how their engagement is modulated by the coherence of experiences and the current learning goals. We conclude that the interaction of multiple brain systems relating to learning, memory, attention, perception, and reward support a flexible concept-learning mechanism that adapts to a range of category structures and incorporates motivational states, making concept learning a fruitful research domain for understanding the neural dynamics underlying complex behaviors.

Indexed as

AttentionBrainBrain MappingConcept FormationFunctional NeuroimagingHumansMagnetic Resonance ImagingModels, Neurologicalcategorizationcomputational modelingfMRIhippocampusparietal cortexprefrontal cortex

Identifiers

PMID31619495
PMCPMC6794919

What OpenQuestion holds

Textmetadata
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.