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Updated: Jan 15, 2026

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
Published on: June 3, 2013
The uncanny valley effect in embodied conversational agents: a critical systematic review of attractiveness,
Ștefania Cihodaru-Ștefanache1,2,3, Ioana R Podina1,3,4
1Laboratory of Cognitive Clinical Sciences, University of Bucharest, Bucharest, Romania.
Introduction:
The Uncanny Valley Effect (UVE) describes the discomfort users feel when interacting with Embodied Conversational Agents (ECAs) that display human-like features, often resulting in anxiety, disgust, and avoidance. This systematic review investigates how user characteristics and ECA design features influence UVE, aiming to provide insights for improving user engagement.
Methods:
Following PRISMA guidelines, we screened 21,897 papers from ACM Digital Library, IEEE Xplore, Scopus, ProQuest, and Web of Science, with 29 studies meeting the inclusion criteria. These studies focused on the roles of anthropomorphism, attractiveness, and uncanniness in user interactions with ECAs.
Results:
Using the Effective Public Health Practice Project (EPHPP) tool, most studies were rated as having weak to moderate methodological quality. We developed a Checklist for Avoiding the Uncanny Valley Effect in ECAs, offering critical recommendations across key dimensions such as physical appearance, non-verbal and verbal communication, and the incorporation of social and cultural norms. Additionally, our review underscores the need for methodological improvements.
Discussion:
Future studies must address confounding variables with greater precision, provide transparent reporting on participant withdrawal, and employ more robust, standardized measurement tools to generate reliable and actionable findings. Without these advancements, the field risks perpetuating inconclusive and contradictory insights, limiting the development of ECAs that effectively engage users while mitigating the UVE.
Systematic Review Registration:
https://www.crd.york.ac.uk/PROSPERO/view/CRD42023426584, identifier: CRD42023426584.
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