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Barbara Tversky

Researcher at Columbia University

Publications -  221
Citations -  19121

Barbara Tversky is an academic researcher from Columbia University. The author has contributed to research in topics: Cognition & Perspective (graphical). The author has an hindex of 66, co-authored 215 publications receiving 17974 citations. Previous affiliations of Barbara Tversky include Ames Research Center & Hitachi.

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Journal ArticleDOI

RETRACTED: Communicative gestures facilitate problem solving for both communicators and recipients

TL;DR: This paper investigated the roles of gesture and speech in explanations, both for communicators and recipients, and found that communicators using gestures alone learned assembly better, making fewer assembly errors than those communicating via speech with gestures.
Proceedings Article

The Spatial Nature of Thought: Understanding Systems Design Through Diagrams

TL;DR: The analysis of designs in topological and Euclidean space required the creation of computational tools that show promise as decision aids for designers, by separating the intertwined qualities of topologically and Euclidesan space, and by making visible the conceptual similarity of design alternatives.
Journal ArticleDOI

When Far Becomes Near.

TL;DR: Using a spatial perspective task in which participants were asked to identify objects at specific locations, it was found that self-perspective judgments were faster for objects presented to the right, rather than the left, and for objects present closer to the participants’ own bodies.
Book ChapterDOI

Creativity: Depth and Breadth

TL;DR: This article investigated divergent and convergent thinking in a task in which participants are asked to provide many interpretations of ambiguous suggestive sketches and found that switching attention among the sketches encouraged divergent thinking whereas focused repeated attention to interpreting a single sketch encouraged convergence.
Journal ArticleDOI

Visualizing space, time, and agents: production, performance, and preference

TL;DR: Users’ production, preference, and performance aligned to favor matrix representations with time as rows or columns and space and agents as entries, but performance and preference were greater forMatrices with discrete dots representing cell entries than for matrices with lines, but lines connecting cells may provide an advantage when evaluating temporal sequence.