For effective human-robot teaming, robots must gain the appropriate social-cognitive mechanisms that allow them to function naturally and intuitively in social interactions with humans. However, there is a lack of consensus on social cognition broadly, and how to design such mechanisms for embodied robotic systems. To this end, recommendations are advanced that are drawn from HRI, psychology, robotics, neuroscience and philosophy as well as theories of embodied cognition, dual process theory, ecological psychology, and dynamical systems. These interdisciplinary and multi-theoretic recommendations are meant to serve as integrative and foundational guidelines for the design of robots with effective social-cognitive mechanisms.
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References
1.
BarghJ. A. (1984). Automatic and conscious processing of social information. In WyerR. S.Jr.SrullT. K. (Eds.), The Handbook of Social Cognition (Vol. 3, pp. 1-43). Hillsdale, NJ: Erlbaum.
BarsalouL. W. (2008). Grounded cognition. Annual Review of Psychology, 59, 617-645.
4.
BartneckC.ForlizziJ. (2004). A designed-centered framework for social human-robot interaction. Proceedings of the Ro-Man 2004, Kurashiki, 591-594.
5.
BeerR. D. (1995). A dynamical systems perspective on agent-environment interaction. Artificial Intelligence, 72, 173-215.
6.
Bockelman MorrowP.FioreS.M. (2012). Supporting human-robot teams in social dynamicism: An overview of the metaphoric inference framework. Proceedings of 56th Annual Meeting of the HFES (pp. 1718-1722). Boston, MA: HFES.
7.
BohlV.van den BosW. (2012). Toward an integrative account of social cognition: Marrying theory of mind and interactionism to study the interplay of type 1 and type 2 processes. Frontiers in Human Neuroscience, 6(274), 1-15.
8.
BreazealC. (2004). Social interactions in HRI: The robot view. IEEE Transactions on Systems Man and Cybernetics Part C Applications and Reviews. IEEE.
9.
BreazealC.GrayJ.BerlinM. (2009). An embodied cognition approach to mindreading skills for socially intelligent robots. The International Journal of Robotics Research, 28, 656-680.
10.
BrooksR. A. (1999). Cambrian intelligence: The early history of the new AI. Cambridge, MA: MIT Press.
11.
ChaikenS.TropeY. (1999). Dual Process Theories in Social Psychology. New York: Guilford.
12.
ChaminadeT.ChengG. (2009). Social cognitive neuroscience and humanoid robotics. Journal of Physiology – Paris, 103, 286-295.
13.
ChemeroA. (2003) An outline of a theory of affordances. Ecological Psychology15(2), pp. 181–95.
14.
ChemeroA.TurveyM. T. (2007). Gibsonian affordances for roboticists. Adaptive Behavior, 15(4), 473-480.
15.
DautenhahnLOgdenB.QuickT. (2002). From embodied to social embedded agents: Implications for interaction-aware robots. Cognitive Systems Research, 3, 397-428.
16.
De JaegherH. (2009). Social understanding through direct perception? Yes, by interacting. Consciousness and Cognition, 18(2), 535–542.
EliasJ.FioreS. M. (2008). From Psychology, to Neuroscience, to Robots: An Interdisciplinary Approach to Bio-Inspired Robotics. Presented at the 20th Annual Convention of the American Psychological Society, May, Chicago, IL.
19.
FioreS. M.EliasJ.GallagherS.JentschF. (2008). Cognition and coordination: Applying cognitive science to understand macrocognition in human-agent teams. Proceedings of the 8th Annual Symposium on Human Interaction with Complex Systems, Norfolk, Virginia.
20.
GallagherS. (2007). Social cognition and social robots. Pragmatics & Cognition, 15(3), 435-453.
21.
GallagherS. (2008). Direct perception in the intersubjective context. Consciousness and Cognition, 17, 535–543.
22.
GallagherS.HuttoD. D. (2008). Understanding others through primary interaction and narrative practice. In ZlatevJ.RacineT.SinhaC.ItkonenE. (Eds.), The shared mind: Perspectives on intersubjectivity. Amsterdam: John Benjamins.
23.
GangohopadhyayN.SchilbachL. (2011). Seeing minds: A neurophilosophical investigation of the role of perception-action coupling in social perception. Social Neuroscience, 1-14.
24.
GibsonJ. J. (1979). The Ecological Approach to Visual Perception. Boston: Houghton Mifflin.
25.
HoffmanG. (2012). Embodied cognition for autonomous interactive robots. Topics in Cognitive Science, 4(4), 759-772.
26.
HoffmanG.BreazealC. (2004). Collaboration in human-robot teams. In Proceedings of AIAA 1st Intelligent Systems Technical Conference, Chicago, IL (pp. 1-18). Reston, VA: AIAA.
27.
HortonT. E.ChakrabortyA.AmantR. S. (2012). Affordances for robots: A brief survey. Avant, 3(2), 70-84.
28.
KleinG.WoodsD. D.BradshawJ. M.HoffmanR. R.FeltovichP. J. (2004). Ten challenges for making automation a. Intelligent Systems, IEEE, 19(6), 91-95.
29.
KnoblichG.SebanzN. (2006). The social nature of perception and action. Current Directions in Psychological Science, 15(3), 99-104.
30.
KonoT. (2009). Social affordances and the possibility of ecological linguistics. Integrative Psychological Behavioral Science, 43, 356-373.
31.
LackeyS. J.BarberD. J.Reinerman-JonesL.BadlerN.HudsonI. (2011). Defining next-generation multi-modal communication in human-robot interaction. Proceedings of 55th Annual Meeting of the HFES (pp. 461-464). Santa Monica, CA: HFES.
32.
MarshK. L.RichardsonM. J.SchmidtR. C. (2009). Social connection through joint action and interpersonal coordination. Topics in Cognitive Science, 1, 320-339.
33.
NoëA. (2004). Action in perception. Cambridge: MIT Press.
34.
PandeyA. K.AlamiR. (2012, July). Visuo-spatial ability, effort and affordance analyses: Towards building blocks for robot’s complex socio-cognitive behaviors. In Workshops at the Twenty-Sixth AAAI Conference on Artificial Intelligence.
35.
PezulloG. (2012). The “Interaction Engine”: A common pragmatic competence across linguistic and nonlinguistic interactions. IEEE Transactions on Autonomous Mental Development, 4(2), 105-123.
36.
PezulloG.BarsalouL. W.CangelosiA.FischerA.McRaeK.SpiveyM. (2013). Computational Grounded Cognition: A new alliance between grounded cognition and computational modeling. Frontiers in Psychology, 3(612), 1-11.
PhillipsE.OsoskyS.JentschF. (2011). From tools to teammates: Toward the development of appropriate mental models for intelligent robots. Proceedings of 55th Annual Meeting of the HFES (pp. 1491-1495). Santa Monica, CA: HFES.
39.
SahinE.CakmakM.DogarM. R.UgurE.UcolikG. (2007). To afford or not to afford: A new formalization of affordances toward affordance-based robot control. Adaptive Behavior, 15(4), 447–472.
40.
SatputeA. B.LiebermanM. D. (2006). Integrating automatic and controlled processes into neurocognitive models of social cognition. Brain Research, 1079, 86-97.
41.
Schütz-BosbachS.PrinzW. (2007). Perceptual resonance: Actioninduced modulation of perception. Trends in Cognitive Sciences, 11(8), 349-355.
42.
StreaterJ. P.Bockelman MorrowP.FioreS. M. (2012). Making things that understand people: The beginnings of an interdisciplinary approach for engineering computational social intelligence. Presented at the 56th Annual Meeting of the Human Factors and Ergonomics Society (October 22-26), Boston, MA.
43.
ThompsonE. (2005). Sensorimotor subjectivity and the enactive approach to experience. Phenomenology and the Cognitive Sciences, 4, 407–427.
VinciarelliA.PanticN.HeylenD.PelachaudC.PoggiI.D’ErricoF.SchröderM. (2012). Bridging the gap between social animal and unsocial machine: A survey of social signal processing. IEEE Transactions on Affective Computing, 3(1), 69-87.
