Evidence map›Paper›PMID 26236260›Full record

ArticleFrontiers in psychology2015

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

Marcus Cheetham, Lingdan Wu, Paul Pauli, Lutz Jancke

Abstract read
In one paragraph

Article in Frontiers in psychology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Marcus CheethamDepartment of Neuropsychology, University of Zurich Zurich, Switzerland ; Department of Psychology, Nungin University Seoul, South Korea.
Lingdan WuSwiss Centre for Affective Sciences, University of Geneva Geneva, Switzerland ; Department of Psychology, University of Wurzburg Wurzburg, Germany.
Paul PauliDepartment of Psychology, University of Wurzburg Wurzburg, Germany.
Lutz JanckeDepartment of Neuropsychology, University of Zurich Zurich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

arousalEEGEMGfamiliarityLPPuncanny valley hypothesisvalence

Identifiers

PMID26236260
PMCPMC4502535

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.