Evidence map›Paper›PMID 42807699›Full record

ArticleOpen mind : discoveries in cognitive science2026

On Benchmarking Human-like Intelligence in Machines.

Lance Ying, Katherine M Collins, Lionel Wong, Ilia Sucholutsky, Ryan Liu, Adrian Weller, Tianmin Shu, Thomas L Griffiths, Joshua B Tenenbaum

Abstract read
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In one paragraph

Article in Open mind : discoveries in cognitive science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
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

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.

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

9 authors.

Lance YingDepartment of Psychology, Harvard University, Cambridge, MA, USA.
Katherine M CollinsDepartment of Engineering, University of Cambridge, Cambridge, UK.ORCID https://orcid.org/0000-0002-7032-716X
Lionel WongDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
Ilia SucholutskyDepartment of Computer Science, New York University, New York, NY, USA.ORCID https://orcid.org/0000-0003-4121-7479
Ryan LiuDepartment of Computer Science, Princeton University, Princeton, NJ, USA.
Adrian WellerDepartment of Engineering, University of Cambridge, Cambridge, UK.ORCID https://orcid.org/0000-0003-1915-7158
Tianmin ShuDepartment of Computer Science, Johns Hopkins University, Baltimore, MD, USA.ORCID https://orcid.org/0000-0001-5734-0653
Thomas L GriffithsDepartment of Computer Science, Princeton University, Princeton, NJ, USA.ORCID https://orcid.org/0000-0002-5138-7255
Joshua B TenenbaumDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID https://orcid.org/0000-0002-1925-2035

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent advances in Artificial Intelligence (AI) have yielded powerful computational models that, by learning from vast amounts of human-generated data, are increasingly posited as approximate models of human cognition. However, we argue that many current evaluation paradigms for AI are insufficient for assessing human-like cognitive capabilities in these models. We identify a set of key shortcomings: a lack of human-validated labels, inadequate representation of human response variability and uncertainty, and reliance on simplified and ecologically invalid tasks. We support our claims by conducting a human evaluation study on nine existing AI benchmarks, suggesting major limitations in task and label designs. To address these limitations, we propose five concrete recommendations for future AI evaluation efforts that will enable more rigorous and meaningful understanding of human-like cognitive capacities in AI models.

Indexed as

AI benchmarkingcognitive scienceevaluationhuman-like intelligencehuman variability

Identifiers

PMID42807699
PMCPMC13618755

What OpenQuestion holds

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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.