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Elife. 2019 May 1;8. pii: e43942. doi: 10.7554/eLife.43942.

Unimodal statistical learning produces multimodal object-like representations.

Author information

Department of Cognitive Science, Central European University, Budapest, Hungary.
Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, United States.
Contributed equally


The concept of objects is fundamental to cognition and is defined by a consistent set of sensory properties and physical affordances. Although it is unknown how the abstract concept of an object emerges, most accounts assume that visual or haptic boundaries are crucial in this process. Here, we tested an alternative hypothesis that boundaries are not essential but simply reflect a more fundamental principle: consistent visual or haptic statistical properties. Using a novel visuo-haptic statistical learning paradigm, we familiarised participants with objects defined solely by across-scene statistics provided either visually or through physical interactions. We then tested them on both a visual familiarity and a haptic pulling task, thus measuring both within-modality learning and across-modality generalisation. Participants showed strong within-modality learning and 'zero-shot' across-modality generalisation which were highly correlated. Our results demonstrate that humans can segment scenes into objects, without any explicit boundary cues, using purely statistical information.


haptic statistical learning; human; neuroscience; object representations; statistical learning; visual statistical learning; zero-shot generalization

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