Similarity as a Window on the Dimensions of Object Representation.

Bradley C Love, Brett D Roads

Abstract readComment

In one paragraph

Article in Trends in cognitive sciences, 2021. 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

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

1 citing paper in PubMed.

Estimating the perceived dimension of psychophysical stimuli using triplet accuracy and hypothesis testing.Journal of vision · 2022
Article

4 · The record

Corrections and comments

Comment on
Revealing the multidimensional mental representations of natural objects underlying human similarity judgements.2020

5 · Who and what money

Authors and funding

2 authors.

Bradley C LoveDepartment of Experimental Psychology, University College London, 26 Bedford Way, London, WC1H 0AP, UK. Electronic address: b.love@ucl.ac.uk.

Brett D RoadsDepartment of Experimental Psychology, University College London, 26 Bedford Way, London, WC1H 0AP, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hebart et al. recently analysed 1.5 million human similarity judgments and found that natural objects are described by a small set of interpretable dimensions. Such large-scale analyses offer new opportunities to characterise how people represent their knowledge, but also challenges, including scaling to even larger data sets and integrating accounts of semantic representation.

Indexed as

JudgmentSemanticsHumansKnowledgebig dataembedding spacesknowledge representationmultidimensional scalingsimilarity

Identifiers

PMID33358386

DOI10.1016/j.tics.2020.12.003

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.