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Arousal, valence, and the uncanny valley: Psychophysiological and self-report findings.

Marcus eCheetham, Marcus eCheetham, Lingdan eWu and 3 more

Frontiers in Psychology · 2015

doi:10.3389/fpsyg.2015.00981 (opens in a new tab)

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Abstract

The main prediction of the Uncanny Valley Hypothesis (UVH) is that observation of humanlike characters that are difficult to distinguish from the human counterpart will evoke a state of negative affect. Well-established electrophysiological (late positive potential [LPP] and facial electromyography [EMG]) and self-report (Self-Assessment Manikin; SAM) indices of valence and arousal, i.e., the primary orthogonal dimensions of affective experience, were used to test this prediction by examining affective experience in response to categorically ambiguous compared with unambiguous avatar and human faces (N=30). LPP and EMG provided direct psychophysiological indices of affective state during passive observation and the SAM provided self-reported indices of affective state during explicit cognitive evaluation of static facial stimuli. The faces were drawn from well-controlled morph continua representing the UVH’ dimension of human likeness (DHL). The results provide no support for the notion that category ambiguity along the DHL is specifically associated with enhanced experience of negative affect. On the contrary, the LPP and SAM-based measures of arousal and valence indicated a general increase in negative affective state (i.e., enhanced arousal and negative valence) with greater morph distance from the human end of the DHL. A second sample (N=30) produced the same finding, using an ad hoc self-rating scale of feelings of familiarity, i.e., an oft-used measure of affective experience along the UVH' familiarity dimension. In conclusion, this multi-method approach using well-validated psychophysiological and self-rating indices of arousal and valence rejects - for passive observation and for explicit affective evaluation of static faces - the main prediction of the UVH.

Abstract licence: CC BY

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