Computational reconstruction of mental representations using human behavior

Caplette, Laurent; Turk‐Browne, Nicholas B.

doi:10.31234/osf.io/7fdvw

Cited by 2 publications

(1 citation statement)

References 54 publications

(75 reference statements)

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“…Mental representations serve as a substrate for a variety of cognitive tasks such as decision-making, communication and memory (Anderson, 1990). Understanding the structure of those representation is a core problem in cognitive science and is the subject of a large corpus of work in the psychological literature (Shepard, 1980(Shepard, , 1987Ghirlanda & Enquist, 2003;Battleday, Peterson, & Griffiths, 2020;Peterson, Abbott, & Griffiths, 2018;Jha, Peterson, & Griffiths, 2020;Caplette & Turk-Browne, 2022;Hebart, Zheng, Pereira, & Baker, 2020).…”

Section: Introductionmentioning

confidence: 99%

Predicting Human Similarity Judgments Using Large Language Models

Marjieh¹,

Sucholutsky²,

Sumers³

et al. 2022

Preprint

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Similarity judgments provide a well-established method for accessing mental representations, with applications in psychology, neuroscience and machine learning. However, collecting similarity judgments can be prohibitively expensive for naturalistic datasets as the number of comparisons grows quadratically in the number of stimuli. One way to tackle this problem is to construct approximation procedures that rely on more accessible proxies for predicting similarity. Here we leverage recent advances in language models and online recruitment, proposing an efficient domain-general procedure for predicting human similarity judgments based on text descriptions. Intuitively, similar stimuli are likely to evoke similar descriptions, allowing us to use description similarity to predict pairwise similarity judgments. Crucially, the number of descriptions required grows only linearly with the number of stimuli, drastically reducing the amount of data required. We test this procedure on six datasets of naturalistic images and show that our models outperform previous approaches based on visual information.

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Section: Introductionmentioning

confidence: 99%

Predicting Human Similarity Judgments Using Large Language Models

Marjieh¹,

Sucholutsky²,

Sumers³

et al. 2022

Preprint

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Facecraft: Race Reification in Psychological Research With Faces

Martinez

2023

Perspect Psychol Sci

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Faces are socially important surfaces of the body on which various meanings are attached. The widespread physiognomic belief that faces inherently contain socially predictive value is why they make a generative stimulus for perception research. However, critical problems arise in studies that simultaneously investigate faces and race. Researchers studying race and racism inadvertently engage in various research practices that transform faces with specific phenotypes into straightforward representatives of their presumed race category, thereby taking race and its phenotypic associations for granted. I argue that research practices that map race categories onto faces using bioessentialist ideas of racial phenotypes constitute a form of racecraft ideology, the dubious reasoning of which presupposes the reality of race and mystifies the causal relation between race and racism. In considering how to study racism without reifying race in face studies, this article places these practices in context, describes how they reproduce racecraft ideology and impair theoretical inferences, and then suggests counterpractices for minimizing this problem.

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Computational reconstruction of mental representations using human behavior

Cited by 2 publications

References 54 publications

Predicting Human Similarity Judgments Using Large Language Models

Predicting Human Similarity Judgments Using Large Language Models

Facecraft: Race Reification in Psychological Research With Faces

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