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Emily S. Cross

Researcher at University of Glasgow

Publications -  126
Citations -  4403

Emily S. Cross is an academic researcher from University of Glasgow. The author has contributed to research in topics: Dance & Social robot. The author has an hindex of 30, co-authored 103 publications receiving 3565 citations. Previous affiliations of Emily S. Cross include Dartmouth College & University of California, Santa Barbara.

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Building a motor simulation de novo: observation of dance by dancers.

TL;DR: It is demonstrated that a complex motor resonance can be built de novo over 5 weeks of rehearsal and activity in premotor and parietal areas during action simulation is enhanced by the ability to execute a learned action irrespective of stimulus familiarity or semantic label.
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Sensitivity of the Action Observation Network to Physical and Observational Learning

TL;DR: Functional magnetic resonance imaging data demonstrate the emergence of action resonance processes in the human brain based on observational learning without physical practice and identify commonalities in the neural substrates for physical and observational learning.
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Justify your alpha

Daniel Lakens, +98 more
TL;DR: In response to recommendations to redefine statistical significance to P ≤ 0.005, it is proposed that researchers should transparently report and justify all choices they make when designing a study, including the alpha level.
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Neurocognitive control in dance perception and performance

TL;DR: The aim of this review is to present basic research on cognitive and neural processes implicated in the execution, expression, and observation of dance, and to bring into relief contemporary issues and open research questions.
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Robotic movement preferentially engages the action observation network

TL;DR: Test how this network responds to form and motion cues when observing natural human motion compared to rigid robotic‐like motion across two independent functional neuroimaging experiments suggests that the AON is sensitive to a broader range of action features beyond those that are simply familiar.