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      Robots As Intentional Agents: Using Neuroscientific Methods to Make Robots Appear More Social

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          Robots are increasingly envisaged as our future cohabitants. However, while considerable progress has been made in recent years in terms of their technological realization, the ability of robots to interact with humans in an intuitive and social way is still quite limited. An important challenge for social robotics is to determine how to design robots that can perceive the user’s needs, feelings, and intentions, and adapt to users over a broad range of cognitive abilities. It is conceivable that if robots were able to adequately demonstrate these skills, humans would eventually accept them as social companions. We argue that the best way to achieve this is using a systematic experimental approach based on behavioral and physiological neuroscience methods such as motion/eye-tracking, electroencephalography, or functional near-infrared spectroscopy embedded in interactive human–robot paradigms. This approach requires understanding how humans interact with each other, how they perform tasks together and how they develop feelings of social connection over time, and using these insights to formulate design principles that make social robots attuned to the workings of the human brain. In this review, we put forward the argument that the likelihood of artificial agents being perceived as social companions can be increased by designing them in a way that they are perceived as intentional agents that activate areas in the human brain involved in social-cognitive processing. We first review literature related to social-cognitive processes and mechanisms involved in human–human interactions, and highlight the importance of perceiving others as intentional agents to activate these social brain areas. We then discuss how attribution of intentionality can positively affect human–robot interaction by (a) fostering feelings of social connection, empathy and prosociality, and by (b) enhancing performance on joint human–robot tasks. Lastly, we describe circumstances under which attribution of intentionality to robot agents might be disadvantageous, and discuss challenges associated with designing social robots that are inspired by neuroscientific principles.

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          Empathy for pain involves the affective but not sensory components of pain.

          Our ability to have an experience of another's pain is characteristic of empathy. Using functional imaging, we assessed brain activity while volunteers experienced a painful stimulus and compared it to that elicited when they observed a signal indicating that their loved one--present in the same room--was receiving a similar pain stimulus. Bilateral anterior insula (AI), rostral anterior cingulate cortex (ACC), brainstem, and cerebellum were activated when subjects received pain and also by a signal that a loved one experienced pain. AI and ACC activation correlated with individual empathy scores. Activity in the posterior insula/secondary somatosensory cortex, the sensorimotor cortex (SI/MI), and the caudal ACC was specific to receiving pain. Thus, a neural response in AI and rostral ACC, activated in common for "self" and "other" conditions, suggests that the neural substrate for empathic experience does not involve the entire "pain matrix." We conclude that only that part of the pain network associated with its affective qualities, but not its sensory qualities, mediates empathy.
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            Adults' expertise in recognizing faces has been attributed to configural processing. We distinguish three types of configural processing: detecting the first-order relations that define faces (i.e. two eyes above a nose and mouth), holistic processing (glueing the features together into a gestalt), and processing second-order relations (i.e. the spacing among features). We provide evidence for their separability based on behavioral marker tasks, their sensitivity to experimental manipulations, and their patterns of development. We note that inversion affects each type of configural processing, not just sensitivity to second-order relations, and we review evidence on whether configural processing is unique to faces.
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              On seeing human: a three-factor theory of anthropomorphism.

              Anthropomorphism describes the tendency to imbue the real or imagined behavior of nonhuman agents with humanlike characteristics, motivations, intentions, or emotions. Although surprisingly common, anthropomorphism is not invariant. This article describes a theory to explain when people are likely to anthropomorphize and when they are not, focused on three psychological determinants--the accessibility and applicability of anthropocentric knowledge (elicited agent knowledge), the motivation to explain and understand the behavior of other agents (effectance motivation), and the desire for social contact and affiliation (sociality motivation). This theory predicts that people are more likely to anthropomorphize when anthropocentric knowledge is accessible and applicable, when motivated to be effective social agents, and when lacking a sense of social connection to other humans. These factors help to explain why anthropomorphism is so variable; organize diverse research; and offer testable predictions about dispositional, situational, developmental, and cultural influences on anthropomorphism. Discussion addresses extensions of this theory into the specific psychological processes underlying anthropomorphism, applications of this theory into robotics and human-computer interaction, and the insights offered by this theory into the inverse process of dehumanization. PsycINFO Database Record (c) 2007 APA, all rights reserved.

                Author and article information

                Front Psychol
                Front Psychol
                Front. Psychol.
                Frontiers in Psychology
                Frontiers Media S.A.
                04 October 2017
                : 8
                1Department of Psychology, George Mason University , Fairfax, VA, United States
                2Istituto Italiano di Tecnologia , Genoa, Italy
                Author notes

                Edited by: Tom Ziemke, University of Skövde and Linköping University, Sweden

                Reviewed by: Robert J. Lowe, University of Gothenburg, Sweden; Martin Cooney, Halmstad University, Sweden

                *Correspondence: Eva Wiese, ewiese@ 123456gmu.edu

                This article was submitted to Cognitive Science, a section of the journal Frontiers in Psychology

                Copyright © 2017 Wiese, Metta and Wykowska.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Page count
                Figures: 5, Tables: 1, Equations: 0, References: 270, Pages: 19, Words: 0
                Funded by: H2020 European Research Council 10.13039/100010663
                Award ID: 715058


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