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Investigating the Uncanny Valley Phenomenon Through the Temporal Dynamics of Neural Responses to Virtual Characters

Chiara Gorlini, Laurits Dixen, Paolo Burelli

TL;DR

The paper tackles the Uncanny Valley by combining online self-report validation with EEG-based neural measurements to uncover temporal dynamics underlying uncanny responses to virtual characters. In phase one, a questionnaire validates a three-factor UV structure (Realism, Eeriness, Warmth) and selects a stimulus set that spans unreal to realistic appearances. In phase two, a small-N EEG study tests ERP hypotheses, finding that Unrealistic stimuli provoke stronger early N170 responses while Semi-realistic stimuli elicit stronger late N400 responses, aligning with a dehumanization account where animacy is initially attributed and later corrected. This work provides temporal neural evidence linking subjective uncanny experiences to cognitive processing stages and offers practical insights for designing believable characters in games. The approach demonstrates how neurophysiological data can inform player experience models and character design decisions, particularly regarding realism levels that avoid the UV.

Abstract

The Uncanny Valley phenomenon refers to the feeling of unease that arises when interacting with characters that appear almost, but not quite, human-like. First theorised by Masahiro Mori in 1970, it has since been widely observed in different contexts from humanoid robots to video games, in which it can result in players feeling uncomfortable or disconnected from the game, leading to a lack of immersion and potentially reducing the overall enjoyment. The phenomenon has been observed and described mostly through behavioural studies based on self-reported scales of uncanny feeling: however, there is still no consensus on its cognitive and perceptual origins, which limits our understanding of its impact on player experience. In this paper, we present a study aimed at identifying the mechanisms that trigger the uncanny response by collecting and analysing both self-reported feedback and EEG data.

Investigating the Uncanny Valley Phenomenon Through the Temporal Dynamics of Neural Responses to Virtual Characters

TL;DR

The paper tackles the Uncanny Valley by combining online self-report validation with EEG-based neural measurements to uncover temporal dynamics underlying uncanny responses to virtual characters. In phase one, a questionnaire validates a three-factor UV structure (Realism, Eeriness, Warmth) and selects a stimulus set that spans unreal to realistic appearances. In phase two, a small-N EEG study tests ERP hypotheses, finding that Unrealistic stimuli provoke stronger early N170 responses while Semi-realistic stimuli elicit stronger late N400 responses, aligning with a dehumanization account where animacy is initially attributed and later corrected. This work provides temporal neural evidence linking subjective uncanny experiences to cognitive processing stages and offers practical insights for designing believable characters in games. The approach demonstrates how neurophysiological data can inform player experience models and character design decisions, particularly regarding realism levels that avoid the UV.

Abstract

The Uncanny Valley phenomenon refers to the feeling of unease that arises when interacting with characters that appear almost, but not quite, human-like. First theorised by Masahiro Mori in 1970, it has since been widely observed in different contexts from humanoid robots to video games, in which it can result in players feeling uncomfortable or disconnected from the game, leading to a lack of immersion and potentially reducing the overall enjoyment. The phenomenon has been observed and described mostly through behavioural studies based on self-reported scales of uncanny feeling: however, there is still no consensus on its cognitive and perceptual origins, which limits our understanding of its impact on player experience. In this paper, we present a study aimed at identifying the mechanisms that trigger the uncanny response by collecting and analysing both self-reported feedback and EEG data.
Paper Structure (16 sections, 4 figures, 2 tables)

This paper contains 16 sections, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Methods
  4. Questionnaire
  5. Stimulus set
  6. Structure, questions and hypotheses
  7. Analysis
  8. EEG Experiment
  9. Equipment
  10. Pre-processing
  11. Small N studies and within-participant design
  12. ERP analysis
  13. Results
  14. ERP analysis
  15. Discussion
  16. ...and 1 more sections

Figures (4)

  • Figure 1: Images used as stimuli in the EEG study. These images have been selected through a prior online questionnaire and they are organised into three categories: unrealistic, semi-realistic and realistic characters.
  • Figure 2: EEG montage displaying the placement of the 32 electrodes.
  • Figure 3: N170 time windows estimated for the five participants in the EEG experiment.
  • Figure 4: ERP waves in the N170 and N400 time windows for the three categories. Each row corresponds to one participant.