Evidence map›Paper›PMID 41618073›Full record

ArticleBehavior research methods2026

Drawings of THINGS: A large-scale drawing dataset of 1854 object concepts.

Kushin Mukherjee, Holly Huey, Laura M Stoinski, Martin N Hebart, Judith E Fan, Wilma A Bainbridge

Abstract read
In one paragraph

Article in Behavior research methods, 2026. 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
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0citing papers in PubMed
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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

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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.

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

6 authors.

Kushin MukherjeeDepartment of Psychology, University of Wisconsin-Madison, Madison, WI, USA. kushinm11@gmail.com.ORCID http://orcid.org/0000-0001-5013-6983
Holly HueyDepartment of Psychology, University of California, San Diego, CA, USA.
Laura M StoinskiMax Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
Martin N HebartMax Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
Judith E FanDepartment of Psychology, Stanford University, Stanford, CA, USA.
Wilma A BainbridgeDepartment of Psychology, University of Chicago, Chicago, IL, USA.

Funding

The neural and behavioral causes underlying differences between visual perception and memoryR01EY034432 · NEI · UNIVERSITY OF CHICAGO · PI Wilma A. Bainbridge · 2023 to 2026
$1.6M
NEI NIH HHS R01 EY034432
6 · The paper itself

Abstract

The development of large datasets of natural images has galvanized progress in psychology, neuroscience, and computer science. Notably, the THINGS database constitutes a collective effort towards understanding of human visual knowledge by accumulating rich data on a shared set of visual object concepts across several studies. In this paper, we introduce Drawing of THINGS ( DoT ), a novel dataset of 28,627 human drawings of 1854 diverse object concepts, sampled systematically from concrete picturable and nameable nouns in the American English language, mirroring the structure of the THINGS image database. In addition to data on drawings' stroke history, we further collected fine-grained recognition data for each drawing, along with metadata on participant demographics, drawing ability, and mental imagery. We characterize people's ability to communicate and recognize semantic information encoded in drawings and compare this ability to their ability to recognize real-world images of the same visual objects. We also explore the relationship between drawing understanding and the memorability and typicality of the objects contained in THINGS. In sum, we envision DoT as a powerful tool that builds on the THINGS database to advance understanding of how humans express knowledge about visual concepts.

Indexed as

ArtConcept FormationDatabases, FactualPattern Recognition, VisualFemaleHumansMaleRecognition, PsychologyBig dataDrawingMemoryObject recognitionTypicalityVisual concepts

Identifiers

PMID41618073
PMCPMC12858628

What OpenQuestion holds

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LicenceCC BY
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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.