Cited by 168Social Robot Interaction and HRIAI in Service InteractionsSpeech and dialogue systems

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When a Talking-Face Computer Agent is Half-Human and Half-Humanoid: Human Identity and Consistency Preference

Gong, Li, Nass, Clifford·2007·Human Communication Research

DOI 10.1111/j.1468-2958.2007.00295.x

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Abstract

Computer-generated anthropomorphic characters are a growing type of communicator that is deployed in digital communication environments. An essential theoretical question is how people identify humanlike but clearly artificial, hence humanoid, entities in comparison to natural human ones. This identity categorization inquiry was approached under the framework of consistency and tested through examining inconsistency effects from mismatching categories. Study 1 (N = 80), incorporating a self-disclosure task, tested participants’ responses to a talking-face agent, which varied in four combinations of human versus humanoid faces and voices. In line with the literature on inconsistency, the pairing of a human face with a humanoid voice or a humanoid face with a human voice led to longer processing time in making judgment of the agent and less trust than the pairing of a face and a voice from either the human or the humanoid category. Female users particularly showed negative attitudes toward inconsistently paired talking faces. Study 2 (N = 80), using a task that stressed comprehension demand, replicated the inconsistency effects on judging time and females’ negative attitudes but not for comprehension-related outcomes. Voice clarity overshadowed the consistency concern for comprehension-related responses. The overall inconsistency effects suggest that people treat humanoid entities in a different category from natural human ones.

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Nonparametric Statistics for the Behavioral Sciences
Guitton, H., & Siegel, S. (1958) · Revue économique · doi:10.2307/3498751 · Cited by 35,279
Advances In Experimental Social Psychology
(2022) · Advances in experimental social psychology · doi:10.1016/s0065-2601(22)x0002-6 · Cited by 22,559
Studies of interference in serial verbal reactions
Stroop, J. R. (1935) · Journal of Experimental Psychology · doi:10.1037/h0054651 · Cited by 18,622
The Social Identity Theory of Intergroup Behavior
Tajfel, H., & Turner, J. C. (2004) · Political Psychology · doi:10.4324/9780203505984-16 · Cited by 13,467
Psychology of intergroup relations
Austin, W. G., & Worchel, S. (1986) · Medical Entomology and Zoology · Cited by 10,626
Nonparametric statistics for the behavioral sciences
McGinn, L. C. (1957) · Journal of the Franklin Institute · doi:10.1016/0016-0032(57)90766-4 · Cited by 7,284
Hearing lips and seeing voices
MCGURK, H., & MACDONALD, J. (1976) · Nature · doi:10.1038/264746a0 · Cited by 6,183
Half a century of research on the Stroop effect: An integrative review.
MacLeod, C. M. (1991) · Psychological Bulletin · doi:10.1037/0033-2909.109.2.163 · Cited by 5,500
Communication and Persuasion
Petty, R. E., & Cacioppo, J. T. (1986) · doi:10.1007/978-1-4612-4964-1 · Cited by 5,219
Communication and Persuasion: Central and Peripheral Routes to Attitude Change
Marquart, F., & Naderer, B. (2015) · Springer eBooks · doi:10.1007/978-3-658-09923-7_20 · Cited by 5,011

Show all 75 references

Research works printed but not linked: 14

Berger C. R, 2005, Journal of Communication, 55, 415, 10.1111/j.1460-2466.2005.tb02680.x · DOI
Fiske S. T., 1991, Social cognition
10.1037//0033-2909.85.4.845 · DOI
Kiesler S., 1996, Journal of Personality & Social Psychology, 70, 47, 10.1037/0022-3514.70.1.47 · DOI
Lee E.-J., 2002, Human Communication Research, 28, 349
Lester J. C., 1997, Proceedings of the 8th World Conference on Artificial Intelligence in Education, 23
10.1037//0033-2909.109.2.163 · DOI · Paper page
Nass C., 1999, Proceedings of the International Auditory-Visual Speech Processing Conference, 1
Pomerantz J. R., 1986, Handbook of perception and human performance, vol. 2: Cognitive processes and performance, 36
Reeves B., 1993, New televisions: The effects of big pictures and big sound on viewer responses to the screen
Ritter M., 1999, Proceedings of the International Auditory-Visual Speech Processing Conference, 163
Slater M. D., 2002, Communication Theory, 1, 173
Slobin D. I, 1979, Psycholinguistics
W Tanaka . (2000 , October 15). Face to face: Talking images to add human touch to e-shops . , pp. 3F -4F .

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Authors: Gong, L., & Nass, C.
Affiliations: The Ohio State University (US)
Stanford University (US)
Venue: Human Communication Research
Publisher: Oxford University Press (OUP)
Published: 2007-04
Volume: 33
Issue: 2
Pages: 163-193
ISSN: 0360-3989 (ISSN-L)
1468-2958
DOI: 10.1111/j.1468-2958.2007.00295.x
OpenAlex: W2001429584
Cited by: 168
References: 75
Citations by year: 2026: 17 · 2025: 18 · 2024: 12 · 2023: 15 · 2022: 16 · 2021: 28 · 2020: 8 · 2019: 10 · 2018: 3 · 2017: 4 · 2016: 3 · 2015: 5 · 2014: 4 · 2013: 3
FWCI: 7.34
Type: journal article
Language: en
Open access: closed
Licence: doi.wiley.com/10.1002/tdm_license_1.1 (tdm, 3075 d)
Locations: Human Communication Research (publishedVersion)
Topics: Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
Keywords: anthropomorphic characters, consistency effect, self-disclosure, voice clarity, gender differences, digital communication environment

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When a Talking-Face Computer Agent is Half-Human and Half-Humanoid: Human Identity and Consistency Preference

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