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ZENODO
Dataset . 2016
License: CC 0
Data sources: ZENODO
DRYAD
Dataset . 2016
License: CC 0
Data sources: Datacite
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Data from: Functionally relevant responses to human facial expressions of emotion in the domestic horse (Equus caballus)

Authors: Smith, Amy Victoria; Proops, Leanne; Grounds, Kate; Wathan, Jennifer; McComb, Karen;

Data from: Functionally relevant responses to human facial expressions of emotion in the domestic horse (Equus caballus)

Abstract

Whether non-human animals can recognize human signals, including emotions, has both scientific and applied importance, and is particularly relevant for domesticated species. This study presents the first evidence of horses' abilities to spontaneously discriminate between positive (happy) and negative (angry) human facial expressions in photographs. Our results showed that the angry faces induced responses indicative of a functional understanding of the stimuli: horses displayed a left-gaze bias (a lateralization generally associated with stimuli perceived as negative) and a quicker increase in heart rate (HR) towards these photographs. Such lateralized responses towards human emotion have previously only been documented in dogs, and effects of facial expressions on HR have not been shown in any heterospecific studies. Alongside the insights that these findings provide into interspecific communication, they raise interesting questions about the generality and adaptiveness of emotional expression and perception across species.

Smith et al. Raw Data

Keywords

Facial expression, lateralization, Emotion recognition, Equus caballus

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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