The Uncanny Valley Does Not Interfere with Level 1 Visual Perspective Taking - PubMed (original) (raw)
The Uncanny Valley Does Not Interfere with Level 1 Visual Perspective Taking
Karl F MacDorman et al. Comput Human Behav. 2013.
Abstract
When a computer-animated human character looks eerily realistic, viewers report a loss of empathy; they have difficulty taking the character's perspective. To explain this perspective-taking impairment, known as the uncanny valley, a novel theory is proposed: The more human or less eerie a character looks, the more it interferes with level 1 visual perspective taking when the character's perspective differs from that of the human observer (e.g., because the character competitively activates shared circuits in the observer's brain). The proposed theory is evaluated in three experiments involving a dot-counting task in which participants either assumed or ignored the perspective of characters varying in their human photorealism and eeriness. Although response times and error rates were lower when the number of dots faced by the observer and character were the same (congruent condition) than when they were different (incongruent condition), no consistent pattern emerged between the human photorealism or eeriness of the characters and participants' response times and error rates. Thus, the proposed theory is unsupported for level 1 visual perspective taking. As the effects of the uncanny valley on empathy have not previously been investigated systematically, these results provide evidence to eliminate one possible explanation.
Keywords: anthropomorphism; character animation; cognitive empathy; mirror neuron system; theory of mind.
Figures
Figure 1
A three-dimensional computer model (left) provides a more realistic representation of a human being (right) than, for example, a two-dimensional illustration; however, its risk of unintentionally resembling a death mask or corpse is much higher. In this respect, an intermediate level of human realism can often be evaluated more negatively than lower or higher levels. Although the absence of blemishes, wrinkles, and asymmetries is correlated with attractiveness in human beings (Fink, Grammer, Thornhill, 2001; Rhodes et al., 2001), as the figure shows, it is not enough to prevent a computer model from looking eerie.
Figure 2
The experimental paradigm of level 1 visual perspective taking is illustrated with four examples from Experiment 1 and 2 of this study. Samson and colleagues (2010) manipulated two independent variables: the instruction to take either one’s own perspective (self) or the character’s perspective (other) and the congruence of the two perspectives as determined by the character’s direction and the arrangement of dots on the walls. In addition, the experiments in this study manipulated the human photorealism of the character by using different kinds of characters in Experiment 1 _an_d 2 and by depicting the same human models in different ways in Experiment 3 (i.e., as a two-dimensional illustration, as a three-dimensional computer model, and as an actual photograph).
Figure 3
The characters from Experiments 1 and 2.
Figure 4
The mean ratings for animacy and eeriness in Experiments 1 and 2. Zombie, Creature, and Bear rated positively on eeriness.
Figure 5
Performance by character in Experiments 1–3. Although experimental controls on the effects of extraneous factors were the most stringent in Experiment 3, the experimental effects of the levels of human photorealism were the smallest. For this and all subsequent figures with error bars, the bars indicate the 95% confidence interval of the true mean.
Figure 6
Using linear regression, the effects of human photorealism in Experiment 1 were estimated on the two (individually adjusted) outcome variables, RT and PE, for each combination of congruence and perspective. The model predicts a positive slope for RT and PE in self–incongruent trials (Hypothesis 2a) and a negative slope in all other trials (Hypotheses 2b, 2c, and 2d). The level of human photorealism was not consistently a significant predictor of performance in the expected direction.
Figure 7
Using linear regression, the effects of eeriness in Experiment 1 were estimated on the two outcome variables, RT (individually adjusted) and PE, for each combination of congruence and perspective. The model predicts a negative slope for RT and PE in self–incongruent trials (Hypothesis 3a) and a positive slope in all other trials (Hypotheses 3b, 3c, and 3d). Within each of these four main conditions, the level of eeriness was at best a minor predictor of performance in the expected direction.
Figure 8
Using linear regression, the effects of human photorealism in Experiment 2 were estimated on the two outcome variables, RT (individually adjusted) and PE, for each combination of congruence and perspective. The model predicts a positive slope for RT and PE in self–incongruent trials (Hypothesis 2a) and a negative slope in all other trials (Hypotheses 2b, 2c, and 2d). Within each of these four main conditions, the level of human photorealism was at best a minor predictor of performance in the expected direction.
Figure 9
Using linear regression, the effects of eeriness in Experiment 2 were estimated on the two outcome variables, RT (individually adjusted) and PE, for each combination of congruence and perspective. The model predicts a negative slope for RT and PE in self–incongruent trials (Hypothesis 3a) and a positive slope in all other trials (Hypotheses 3b, 3c, and 3d). Within each of these four main conditions, the level of eeriness was not a significant predictor of performance in the expected direction.
Figure 10
The human characters used in Experiment 3 are three different men depicted at three levels of human photorealism: two-dimensional illustration, three-dimensional computer model, and actual photograph.
Figure 11
The mean ratings of animacy and eeriness in Experiment 3. All three computer models rated positively on eeriness.
Figure 12
Using linear regression, the effects of human photorealism in Experiment 3 were estimated on the two outcome variables, RT (individually adjusted) and PE, for each combination of congruence and perspective. The model predicts a positive slope for RT and PE in self–incongruent trials (Hypothesis 2a) and a negative slope in all other trials (Hypotheses 2b, 2c, and 2d). Within each of these four main conditions, the level of human photorealism was not a significant predictor of performance in the expected direction.
Figure 13
Using linear regression, the effects of eeriness in Experiment 3 were estimated on the two outcome variables, RT (individually adjusted) and PE, for each combination of congruence and perspective. The model predicts a negative slope for RT and PE in self–incongruent trials (Hypothesis 3a) and a positive slope in all other trials (Hypotheses 3b, 3c, and 3d). Within each of these four main conditions, the level of eeriness was not a significant predictor of performance in the expected direction.
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