Papers by Burcu Aysen Urgen
Recent work in cognitive science suggests that our expectations affect visual perception. With th... more Recent work in cognitive science suggests that our expectations affect visual perception. With the rise of artificial agents in human life in the last few decades, one important question is whether our expectations about non-human agents such as humanoid robots affect how we perceive them. In the present study, we addressed this question in an audiovisual context. Participants reported whether a voice embedded in a noise belonged to a human or a robot. Prior to this judgment, they were presented with a human or a robot image that served as a cue and allowed them to form an expectation about the category of the voice that would follow. This cue was either congruent or incongruent with the category of the voice. Our results show that participants were faster and more accurate when the auditory target was preceded by a congruent cue than an incongruent cue. This was true regardless of the human-likeness of the robot. Overall, these results suggest that our expectations affect how we perceive non-human agents and shed light on future work in robot design.
Mind perception is considered to be the ability to attribute mental states to non-human beings. A... more Mind perception is considered to be the ability to attribute mental states to non-human beings. As social robots increasingly become part of our lives, one important question for HRI is to what extent we attribute mental states to these agents and the conditions under which we do so. In the present study, we investigated the effect of appearance and the type of action a robot performs on mind perception. Participants rated videos of two robots in different appearances (one metallic, the other human-like), each of which performed four different actions (manipulating an object, verbal communication, non-verbal communication, and an action that depicts a biological need) on Agency and Experience dimensions. Our results show that the type of action that the robot performs affects the Agency scores. When the robot performs human-specific actions such as communicative actions or an action that depicts a biological need, it is rated to have more agency than when it performs a manipulative action. On the other hand, the appearance of the robot did not have any effect on the Agency or the Experience scores. Overall, our study suggests that the behavioral skills we build into social robots could be quite important in the extent we attribute mental states to them. CCS CONCEPTS • Human-centered computing • Human-computer interaction (HCI) • HCI design and evaluation methods • User studies
International Journal of Social Robotics, Apr 5, 2023
Recent work in cognitive science suggests that our expectations affect visual perception. With th... more Recent work in cognitive science suggests that our expectations affect visual perception. With the rise of artificial agents in human life in the last few decades, one important question is whether our expectations about non-human agents such as humanoid robots affect how we perceive them. In the present study, we addressed this question in an audiovisual context. Participants reported whether a voice embedded in a noise belonged to a human or a robot. Prior to this judgment, they were presented with a human or a robot image that served as a cue and allowed them to form an expectation about the category of the voice that would follow. This cue was either congruent or incongruent with the category of the voice. Our results show that participants were faster and more accurate when the auditory target was preceded by a congruent cue than an incongruent cue. This was true regardless of the human-likeness of the robot. Overall, these results suggest that our expectations affect how we perceive non-human agents and shed light on future work in robot design.
International Journal of Social Robotics
Recent work in cognitive science suggests that our expectations affect visual perception. With th... more Recent work in cognitive science suggests that our expectations affect visual perception. With the rise of artificial agents in human life in the last few decades, one important question is whether our expectations about non-human agents such as humanoid robots affect how we perceive them. In the present study, we addressed this question in an audio–visual context. Participants reported whether a voice embedded in a noise belonged to a human or a robot. Prior to this judgment, they were presented with a human or a robot image that served as a cue and allowed them to form an expectation about the category of the voice that would follow. This cue was either congruent or incongruent with the category of the voice. Our results show that participants were faster and more accurate when the auditory target was preceded by a congruent cue than an incongruent cue. This was true regardless of the human-likeness of the robot. Overall, these results suggest that our expectations affect how we p...
The present study aims to investigate how gender stereotypes affect people's gender attributi... more The present study aims to investigate how gender stereotypes affect people's gender attribution to social robots. To this end, we examined whether a robot can be attributed a gender depending on a performed action. The study consists of 3 stages. In the first stage, we determined masculine and feminine actions by a survey conducted with 54 participants. In the second stage, we selected a gender-neutral robot by having 76 participants rate several robot stimuli in the masculine-feminine spectrum. In the third stage, we created short animation videos in which the gender-neutral robot determined in stage two performed the masculine and feminine actions determined in stage one. We then asked 102 participants to evaluate the robot in the videos in the masculine-feminine spectrum. We asked them to rate the videos according to their own view (self-view) and how they thought the society would evaluate them (society-view). We also used the Socialization of Gender Norms Scale (SGNS) to id...
Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, 2021
Mind perception is considered to be the ability to attribute mental states to non-human beings. A... more Mind perception is considered to be the ability to attribute mental states to non-human beings. As social robots increasingly become part of our lives, one important question for HRI is to what extent we attribute mental states to these agents and the conditions under which we do so. In the present study, we investigated the effect of appearance and the type of action a robot performs on mind perception. Participants rated videos of two robots in different appearances (one metallic, the other human-like), each of which performed four different actions (manipulating an object, verbal communication, non-verbal communication, and an action that depicts a biological need) on Agency and Experience dimensions. Our results show that the type of action that the robot performs affects the Agency scores. When the robot performs human-specific actions such as communicative actions or an action that depicts a biological need, it is rated to have more agency than when it performs a manipulative action. On the other hand, the appearance of the robot did not have any effect on the Agency or the Experience scores. Overall, our study suggests that the behavioral skills we build into social robots could be quite important in the extent we attribute mental states to them. CCS CONCEPTS • Human-centered computing • Human-computer interaction (HCI) • HCI design and evaluation methods • User studies
Recent work in cognitive science suggests that our expectations affect visual perception. With th... more Recent work in cognitive science suggests that our expectations affect visual perception. With the rise of artificial agents in human life in the last few decades, one important question is whether our expectations about non-human agents such as humanoid robots affect how we perceive them. In the present study, we addressed this question in an audio-visual context. Participants reported whether a voice embedded in a noise belonged to a human or a robot. Prior to this judgment, they were presented with a human or a robot image that served as a cue and allowed them to form an expectation about the category of the voice that would follow. This cue was either congruent or incongruent with the category of the voice. Our results show that participants were faster and more accurate when the auditory target was preceded by a congruent cue than an incongruent cue. This was true regardless of the human-likeness of the robot. Overall, these results suggest that our expectations affect how we p...
Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, 2020
Robots increasingly become part of our lives. How we perceive and predict their behavior has been... more Robots increasingly become part of our lives. How we perceive and predict their behavior has been an important issue in HRI. To address this issue, we adapted a well-established prediction paradigm from cognitive science for HRI. Participants listened a greeting phrase that sounds either human-like or robotic. They indicated whether the voice belongs to a human or a robot as fast as possible with a key press. Each voice was preceded with a human or robot image (a human-like robot or a mechanical robot) to cue the participant about the upcoming voice. The image was either congruent or incongruent with the sound stimulus. Our findings show that people reacted faster to robotic sounds in congruent trials than incongruent trials, suggesting the role of predictive processes in robot perception. In sum, our study provides insights about how robots should be designed, and suggests that designing robots that do not violate our expectations may result in a more efficient interaction between humans and robots. CCS CONCEPTS • Human-centered computing • Human-computer interaction (HCI) • HCI design and evaluation methods • User studies
HRI '20: Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, 2020
Robots increasingly become part of our lives. How we perceive and predict their behavior has been... more Robots increasingly become part of our lives. How we perceive and predict their behavior has been an important issue in HRI. To address this issue, we adapted a well-established prediction paradigm from cognitive science for HRI. Participants listened a greeting phrase that sounds either human-like or robotic. They indicated whether the voice belongs to a human or a robot as fast as possible with a key press. Each voice was preceded with a human or robot image (a human-like robot or a mechanical robot) to cue the participant about the upcoming voice. The image was either congruent or incongruent with the sound stimulus. Our findings show that people reacted faster to robotic sounds in congruent trials than incongruent trials, suggesting the role of predictive processes in robot perception. In sum, our study provides insights about how robots should be designed, and suggests that designing robots that do not violate our expectations may result in a more efficient interaction between humans and robots. CCS CONCEPTS • Human-centered computing • Human-computer interaction (HCI) • HCI design and evaluation methods • User studies
Neuropsychologia, 2019
Visual processing of actions is supported by a network consisting of occipito-temporal, parietal,... more Visual processing of actions is supported by a network consisting of occipito-temporal, parietal, and premotor regions in the human brain, known as the Action Observation Network (AON). In the present study, we investigate what aspects of visually perceived actions are represented in this network using fMRI and computational modeling. Human subjects performed an action perception task during scanning. We characterized the different aspects of the stimuli starting from purely visual properties such as form and motion to higher-aspects such as intention using computer vision and categorical modeling. We then linked the models of the stimuli to the three nodes of the AON with representational similarity analysis. Our results show that different nodes of the network represent different aspects of actions. While occipito-temporal cortex performs visual analysis of actions by means of integrating form and motion information, parietal cortex builds on these visual representations and transforms them into more abstract and semantic representations coding target of the action, action type and intention. Taken together, these results shed light on the neuro-computational mechanisms that support visual perception of actions and provide support that AON is a hierarchical system in which increasing levels of the cortex code increasingly complex features.
A B S T R A C T Uncanny valley refers to humans' negative reaction to almost-but-not-quite-human ... more A B S T R A C T Uncanny valley refers to humans' negative reaction to almost-but-not-quite-human agents. Theoretical work proposes prediction violation as an explanation for uncanny valley but no empirical work has directly tested it. Here, we provide evidence that supports this theory using event-related brain potential recordings from the human scalp. Human subjects were presented images and videos of three agents as EEG was recorded: a real human, a mechanical robot, and a realistic robot in between. The real human and the mechanical robot had congruent appearance and motion whereas the realistic robot had incongruent appearance and motion. We hypothesize that the appearance of the agent would provide a context to predict her movement, and accordingly the perception of the realistic robot would elicit an N400 effect indicating the violation of predictions, whereas the human and the mechanical robot would not. Our data confirmed this hypothesis suggesting that uncanny valley could be explained by violation of one's predictions about human norms when encountered with realistic but artificial human forms. Importantly, our results implicate that the mechanisms underlying perception of other individuals in our environment are predictive in nature.
The uncanny valley hypothesis suggests that robots that are humanoid in appearance elicit positiv... more The uncanny valley hypothesis suggests that robots that are humanoid in appearance elicit positive and empathetic responses, but that there is a point where the robot design is very close to human, the robot becomes repulsive. A possible mechanism underlying this phenomenon is based on the predictive coding theory of neural computations. According to this framework, certain neural systems in the brain can ascribe humanness to a robot that is highly human-like in its appearance, and if the robot’s behavior does not match in realism to the appearance, there will be a processing conflict the neural network will need to resolve. Although this hypothesis is consistent with previous results in the field, empirical work directly testing it is lacking. Here we addressed this gap with a cognitive neuroscience study: We recorded electrical brain activity from the human brain using electroencephalography (EEG) as human subjects viewed images and videos of three agents: A female adult (human), a robot agent closely resembling her (android), and the same robot in a more mechanical appearance (robot). The human and robot had congruent appearance and movement (human with biological appearance and movement; robot with mechanical appearance and movement), and the android had incongruent appearance and movement (biological appearance but mechanical movement). We hypothesized that the android would violate the brain’s predictions since it has a biological appearance, but mechanical movement, whereas the other agents would not lead to such a conflict (robot looks mechanical and moves mechanically; human looks biological and moves biologically). We focused on the N400 ERP component derived from the EEG data. Since the N400 has a greater amplitude for anomalies and violations based on preceding context, we hypothesized the amplitude would be significantly greater for the android in the moving condition than the still condition, whereas the moving and still conditions of the robot and human stimuli would not differ. Our results confirmed out hypothesis, indicating that the uncanny valley might at least partially be due to violations of the brain’s internal predictions about almost-but-not-quite-human robots. Interdisciplinary studies like this one not only allows us to understand the neural basis of human social cognition but also informs robotics about what kind of robots we should design for successful human-robot interaction.
Understanding others' actions is essential for functioning in the physical and social world. In t... more Understanding others' actions is essential for functioning in the physical and social world. In the past two decades research has shown that action perception involves the motor system, supporting theories that we understand others' behavior via embodied motor simulation. Recently, empirical approach to action perception has been facilitated by using well-controlled artificial stimuli, such as robots. One broad question this approach can address is what aspects of similarity between the observer and the observed agent facilitate motor simulation. Since humans have evolved among other humans and animals, using artificial stimuli such as robots allows us to probe whether our social perceptual systems are specifically tuned to process other biological entities. In this study, we used humanoid robots with different degrees of human-likeness in appearance and motion along with electromyography (EMG) to measure muscle activity in participants' arms while they either observed or imitated videos of three agents produce actions with their right arm. The agents were a Human (biological appearance and motion), a Robot (mechanical appearance and motion), and an Android (biological appearance and mechanical motion). Right arm muscle activity increased when participants imitated all agents. Increased muscle activation was found also in the stationary arm both during imitation and observation. Furthermore, muscle activity was sensitive to motion dynamics: activity was significantly stronger for imitation of the human than both mechanical agents. There was also a relationship between the dynamics of the muscle activity and motion dynamics in stimuli. Overall our data indicate that motor simulation is not limited to observation and imitation of agents with a biological appearance, but is also found for robots. However we also found sensitivity to human motion in the EMG responses. Combining data from multiple methods allows us to obtain a more complete picture of action understanding and the underlying neural computations.
Journal of Neuroscience, 2015
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Papers by Burcu Aysen Urgen