Evidence map›Paper›PMID 41467250›Full record

ArticleTopics in cognitive science2026

AI-Enhanced Semantic Feature Norms for 786 Concepts.

Siddharth Suresh, Kushin Mukherjee, Tyler Giallanza, Xizheng Yu, Mia Patil, Jonathan D Cohen, Timothy T Rogers

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Topics in cognitive science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
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.

Siddharth SureshDepartment of Psychology, University of Wisconsin-Madison.ORCID 0009-0008-9876-8742
Kushin MukherjeeDepartment of Psychology, Stanford University.
Tyler GiallanzaDepartment of Psychology, Princeton University.
Xizheng YuDepartment of Computer Science, Brown University.
Mia PatilDepartment of Computer Sciences, University of Wisconsin-Madison.
Jonathan D CohenDepartment of Psychology, Princeton University.
Timothy T RogersDepartment of Psychology, University of Wisconsin-Madison.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Semantic feature norms have been foundational in the study of human conceptual knowledge, yet traditional methods face trade-offs between concept/feature coverage and verifiability of quality due to the labor-intensive nature of norming studies. Here, we introduce a novel approach that augments a dataset of human-generated feature norms with responses from large language models (LLMs) while verifying the quality of norms against reliable human judgments. We find that our AI-enhanced feature norm dataset, NOVA: Norms Optimized Via AI, shows much higher feature density and overlap among concepts while outperforming a comparable human-only norm dataset and word-embedding models in predicting people's semantic similarity judgments. Taken together, we demonstrate that human conceptual knowledge is richer than captured in previous norm datasets and show that, with proper validation, LLMs can serve as powerful tools for cognitive science research.

Indexed as

Artificial IntelligenceCognitive ScienceConcept FormationPsycholinguisticsSemanticsHumansFeature listingLarge language modelsSemantic knowledgeSimilarity judgments

Identifiers

PMID41467250
PMCPMC13102279

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.