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Wim Pouw

Researcher at Radboud University Nijmegen

Publications -  57
Citations -  850

Wim Pouw is an academic researcher from Radboud University Nijmegen. The author has contributed to research in topics: Gesture & Computer science. The author has an hindex of 11, co-authored 45 publications receiving 608 citations. Previous affiliations of Wim Pouw include Nijmegen Institute for Cognition and Information & Max Planck Society.

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An Embedded and Embodied Cognition Review of Instructional Manipulatives

TL;DR: The authors argued that perceptual and interactive richness may provide opportunities for alleviating cognitive load, and that transfer of learning is not reliant on decontextualized knowledge but may draw on previous sensorimotor experiences of the kind afforded by perceptual or interactive richness of manipulatives.
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Cold-blooded loneliness: Social exclusion leads to lower skin temperatures

TL;DR: The results strongly support the idea that more complex metaphorical understandings of social relations are scaffolded onto literal changes in bodily temperature: Being excluded in an online ball tossing game leads to lower finger temperatures, while the negative affect typically experienced after such social exclusion is alleviated after holding a cup of warm tea.
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Toward a more embedded/extended perspective on the cognitive function of gestures.

TL;DR: It is suggested that gestures are external physical tools of the cognitive system that replace and support otherwise solely internal cognitive processes, that is gestures provide the Cognitive system with a stable external physical and visual presence that can provide means to think with.
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Gesture-speech physics: The biomechanical basis for the emergence of gesture-speech synchrony.

TL;DR: It is concluded that gesture-speech synchrony has a biomechanical basis, which will have implications for the cognitive, ontogenetic, and phylogenetic understanding of multimodal language.
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The quantification of gesture-speech synchrony: A tutorial and validation of multimodal data acquisition using device-based and video-based motion tracking.

TL;DR: This article provides a tutorial on more efficient methods to quantify the temporal properties of gesture kinematics, and introduces and compares the performance of two video-based motion-tracking methods against a high-performance wired motion- tracking system.