Human-Centered Computing

Computer-modeled characters resembling real people sometimes elicit cold, eerie feelings. This effect, called the uncanny valley, has been attributed to uncertainty about whether the character is human or living or real. Uncertainty, however, neither explains why anthropomorphic characters lie in the uncanny valley nor their characteristic eeriness. We propose that realism inconsistency causes anthropomorphic characters to appear unfamiliar, despite their physical similarity to real people, owing to perceptual narrowing. We further propose that their unfamiliar, fake appearance elicit cold, eerie feelings, motivating threat avoidance. In our experiment, 365 participants categorized and rated objects, animals, and humans whose realism was manipulated along consistency-reduced and control transitions. These data were used to quantify a Bayesian model of categorical perception. In hypothesis testing, we found reducing realism consistency did not make objects appear less familiar, but only animals and humans, thereby eliciting cold, eerie feelings. Next, structural equation models elucidated the relation among realism inconsistency (measured objectively in a 2D Morlet wavelet domain inspired by the primary visual cortex), realism, familiarity, eeriness, and warmth. The fact that reducing realism consistency only elicited cold, eerie feelings toward anthropomorphic characters, and only when it lessened familiarity, indicates the role of perceptual narrowing in the uncanny valley.

It can be creepy to notice that something human-looking is not real. But can sensitivity to this phenomenon, known as the uncanny valley, be predicted from superficially unrelated traits? Based on results from at least 489 participants, this study examines the relation between nine theoretically motivated trait indices and uncanny valley sensitivity, operationalized as increased eerie ratings and decreased warmth ratings for androids presented in videos. Animal Reminder Sensitivity, Neuroticism, its Anxiety facet, and Religious Fundamentalism significantly predicted uncanny valley sensitivity. In addition, Concern over Mistakes and Personal Distress significantly predicted android eerie ratings but not warmth. The structural equation model indicated that Religious Fundamentalism operates indirectly, through robot-related attitudes, to heighten uncanny valley sensitivity, while Animal Reminder Sensitivity increases eerie ratings directly. These results suggest that the uncanny valley phenomenon may operate through both sociocultural constructions and biological adaptations for threat avoidance, such as the fear and disgust systems. Trait indices that predict uncanny valley sensitivity warrant investigation by experimental methods to explicate the processes underlying the uncanny valley phenomenon.

Just as physical appearance affects social influence in human communication, it may also affect the processing of advice conveyed through avatars, computer-animated characters, and other human-like interfaces. Although the most persuasive computer interfaces are often the most human-like, they have been predicted to incur the greatest risk of falling into the uncanny valley, the loss of empathy attributed to characters that appear eerily human. Previous studies compared interfaces on the left side of the uncanny valley, namely, those with low human likeness. To examine interfaces with higher human realism, a between-groups factorial experiment was conducted through the internet with 426 midwestern U.S. undergraduates. This experiment presented a hypothetical ethical dilemma followed by the advice of an authority figure. The authority was manipulated in three ways: depiction (digitally recorded or computer animated), motion quality (smooth or jerky), and advice (disclose or refrain from disclosing sensitive information). Of these, only the advice changed opinion about the ethical dilemma, even though the animated depiction was significantly eerier than the human depiction. These results indicate that compliance with an authority persists even when using an uncannily realistic computer-animated double.

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.

Prolegomena to Empirical Psychology and Rational Psychology. The former book was originally published in 1732 and the latter in 1734. Wolff's contribution was to relate the method of studying psychology to that of physics. He sought to establish laws of sensation, memory, emotion, understanding, and behavior. Johann Friederich Herbart further developed Wolff's measuring methods, and Wilhelm Wundt, who considered Wolff "the most influential psychological systematist among moderns," used Wolff's ideas to develop his psychophysics.

This paper explores memory-based approaches to the recognition of human behavior that rely on a database of previously categorized instances of sensory data. To overcome the curse of dimensionality, we examine two related methods that both rely on a hierarchical division of the sensory space using a decision tree. The first approach iteratively applies linear discriminant analysis to divide the sensory space in half in order to construct a binary tree for recognizing behaviors. We have verified the effectiveness of this approach for real-time behavior recognition using infrared sensors distributed in a desk environment and compared its results to those of Quinlan's C4.5. The second approach applies the well-known ID3 algorithm to the construction of a decision tree based on an information criterion. We use it to recognize browsing behavior at a video rental shop. Inferences are derived directly from the binarized pixel data of four wide-view cameras. Both systems offer behavior recognition rates in excess of 90%.

Developing a humanoid robot that can learn to perform complex tasks by itself has become a major goal of robotics research. This paper proposes a new algorithm for the automatic segmentation of humanoid motion data. We use a simplified soccer game, RoboCup, as a prototype task. Motion data from a 20 degree-of-freedom humanoid soccer playing robot are reduced to their intrinsic dimensionality by nonlinear principal component analysis. The proposed algorithm operates in of two phases. The first phase automatically segments the motion data in the reduced sensorimotor space by incrementally generating nonlinear principal component analysis with a circular constraint networks and assigning data points based on their temporal order to these networks in a conquer-and-divide fashion. Then, the second phase of the algorithm removes repeated patterns based on the distance between redundant motion patterns in the reduced sensorimotor space. The networks abstracted five motion patterns without any prior information about the number or type of motion patterns. The learned networks can be used to recognize and generate humanoid actions.

The uncanny valley hypothesis predicts that an entity appearing almost human risks eliciting cold, eerie feelings in viewers. Categorization-based stranger avoidance theory identifies the cause of this feeling as categorizing the entity into a novel category. This explanation is doubtful because stranger is not a novel category in adults; infants do not avoid strangers while the category stranger remains novel; infants old enough to fear strangers prefer photographs of strangers to those more closely resembling a familiar person ; and the uncanny valley's characteristic eeriness is seldom felt when meeting strangers. We repeated our original experiment with a more realistic 3D computer model and found no support for categorization-based stranger avoidance theory. By contrast, realism inconsistency theory explains cold, eerie feelings elicited by transitions between instances of two different, mutually exclusive categories, given that at least one category is anthropomorphic: Cold, eerie feelings are caused by prediction error from perceiving some features as features of the first category and other features as features of the second category. In principle, realism inconsistency theory can explain not only negative evaluations of transitions between real and computer modeled humans but also between different vertebrate species.

Just as physical appearance affects social influence in human communication, it may also affect the processing of advice conveyed through avatars, computer-animated characters, and other human-like interfaces. Although the most persuasive computer interfaces are often the most human-like, they have been predicted to incur the greatest risk of falling into the uncanny valley, the loss of empathy attributed to characters that appear eerily human. Previous studies compared interfaces on the left side of the uncanny valley, namely, those with low human likeness. To examine interfaces with higher human realism, a between-groups factorial experiment was conducted through the internet with 426 midwestern U.S. undergraduates. This experiment presented a hypothetical ethical dilemma followed by the advice of an authority figure. The authority was manipulated in three ways: depiction (digitally recorded or computer animated), motion quality (smooth or jerky), and advice (disclose or refrain from disclosing sensitive information). Of these, only the advice changed opinion about the ethical dilemma, even though the animated depiction was significantly eerier than the human depiction. These results indicate that compliance with an authority persists even when using an uncannily realistic computer-animated double.

It can be creepy to notice that something human-looking is not real. But can sensitivity to this phenomenon, known as the uncanny valley, be predicted from superficially unrelated traits? Based on results from at least 489 participants, this study examines the relation between nine theoretically motivated trait indices and uncanny valley sensitivity, operationalized as increased eerie ratings and decreased warmth ratings for androids presented in videos. Animal Reminder Sensitivity, Neuroticism, its Anxiety facet, and Religious Fundamentalism significantly predicted uncanny valley sensitivity. In addition, Concern over Mistakes and Personal Distress significantly predicted android eerie ratings but not warmth. The structural equation model indicated that Religious Fundamentalism operates indirectly, through robot-related attitudes, to heighten uncanny valley sensitivity, while Animal Reminder Sensitivity increases eerie ratings directly. These results suggest that the uncanny valley phenomenon may operate through both sociocultural constructions and biological adaptations for threat avoidance, such as the fear and disgust systems. Trait indices that predict uncanny valley sensitivity warrant investigation by experimental methods to explicate the processes underlying the uncanny valley phenomenon.

The speed, accuracy, and adaptability of human movement depends on the brain performing an inverse kinematics transformation*i.e., a transformation from visual to joint angle coordi-nates*based on learning from experience. In human visually guided motion control, it is important to learn a feedback controller for the hand position error. This paper proposes two novel models that learn coordinate transformations of the human visual feedback controller with time delay. The proposed models redress drawbacks in current models because they do not rely on complex signal switching, which does not seem neurophysiologically plausible. 2001 Published by Elsevier Science B.V.