| Autor: |
Alexander Diel, Michael Lewis |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Computers in Human Behavior Reports, Vol 14, Iss , Pp 100430- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2451-9588 |
| DOI: |
10.1016/j.chbr.2024.100430 |
| Popis: |
The uncanny valley describes the negative evaluation of near humanlike artificial entities. Previous research with synthetic and real voices failed to find an uncanny valley of voices. This may have been due to an incomplete selection of stimuli. In Experiment 1 (n = 50), synthetic, normal, and deviating voices (distorted and pathological) were rated on uncanniness and human likeness and categorized as human or non-human. Results showed a non-monotonic function when the uncanniness was plotted against human likeness indicative of an uncanny valley. However, the shape could be divided into two monotonic functions based on voice type (synthetic vs deviating). Categorization ambiguity could not predict voice uncanniness but moderated the effect of realism on uncanniness. Experiment 2 (n = 35) found that perceived organicness, animacy, and mind attribution of voices significantly moderated the effect of realism on uncanniness. Results indicate a vocal uncanny valley driven by deviations from typical human voices. While voices can fall into an uncanny valley, synthetic voices successfully escape it. Finally, the results support the account that uncanniness is caused by deviations from familiar categories, rather than categorical ambiguity or the misattribution of mind or animacy. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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