Revealing the multidimensional mental representations of natural objects underlying human similarity judgements

Martin N. Hebart (), Charles Y. Zheng, Francisco Pereira and Chris I. Baker
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Martin N. Hebart: National Institutes of Health
Charles Y. Zheng: National Institutes of Health
Francisco Pereira: National Institutes of Health
Chris I. Baker: National Institutes of Health

Nature Human Behaviour, 2020, vol. 4, issue 11, 1173-1185

Abstract: Abstract Objects can be characterized according to a vast number of possible criteria (such as animacy, shape, colour and function), but some dimensions are more useful than others for making sense of the objects around us. To identify these core dimensions of object representations, we developed a data-driven computational model of similarity judgements for real-world images of 1,854 objects. The model captured most explainable variance in similarity judgements and produced 49 highly reproducible and meaningful object dimensions that reflect various conceptual and perceptual properties of those objects. These dimensions predicted external categorization behaviour and reflected typicality judgements of those categories. Furthermore, humans can accurately rate objects along these dimensions, highlighting their interpretability and opening up a way to generate similarity estimates from object dimensions alone. Collectively, these results demonstrate that human similarity judgements can be captured by a fairly low-dimensional, interpretable embedding that generalizes to external behaviour.

Date: 2020
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DOI: 10.1038/s41562-020-00951-3

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