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

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Hebart,  Martin N.
Laboratory of Brain and Cognition, Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD, USA;
Max Planck Research Group Vision and Computational Cognition, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Citation

Hebart, M. N., Zheng, C. Y., Pereira, F., & Baker, C. I. (2020). Revealing the multidimensional mental representations of natural objects underlying human similarity judgements. Nature Human Behaviour, 4(11), 1173-1185. doi:10.1038/s41562-020-00951-3.


Cite as: http://hdl.handle.net/21.11116/0000-0007-60A5-2
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.