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Artificial Agents

Understanding Non-human Agents & Action

In daily life, we observe and interact with others moving in myriad different ways, some more predictable than others. Dominant theories of action understanding suggest that we understand others’ actions by using our own motor experience to extrapolate what others might be doing, and what they are likely to do next. But what happens when we see a person moving like a machine, or a dancing robot, or a triangle negotiating a barrier to grab a cookie?

Our research suggests that the same sensorimotor brain regions engaged when watching our fellow humans move naturally are also engaged when watching non-human agents in action (whether Lego robots, 2D shapes, or a wind-up bulldozer), or humans moving in decidedly unpredictable and unfamiliar ways. Ongoing investigations seek to understand top-down modulations of these perceptual processes, and how different expectations change how we perceive and interact with non-human agents.

This strand of research within the SoBA Lab is being further developed with the award of a €1.8M ERC Starting Grant to Prof. Cross, which supports the establishment of a 'Social Robots' research team within the SoBA Lab from 2016-2021.

Want to know more about the Social Robots project in the SoBA Lab?  click here!

Selected publications:
Cross, E. S., Ramsey, R., Liepelt, R., Prinz, W. & Hamilton, A. F. de C. (2016). The shaping of social perception by stimulus and knowledge cues to human animacy. Philosophical Transactions of the Royal Society B, 371 (1686)

Cross, E. S., Hamilton, A. F. de C., Kraemer, D. J. M, Kelley, W. M. & Grafton, S. T. (2009).  Dissociable substrates for body motion and physical experience in the human action observation network.  European Journal of Neuroscience, 30, 1383-1392.

Cross, E. S., Liepelt, R., Hamilton, A. F. de C., Parkinson, J., Ramsey, R., Stadler, W., & Prinz, W.  (2012). Robotic movement preferentially engages the action observation network. Human Brain Mapping, 33, 2238-2254.

Cross, E. S., Stadler, W., Parkinson, J., Schütz-Bosbach, S. & Prinz, W. (2013). The influence of visual training on complex action prediction. Human Brain Mapping, 34(2), 467-486.

Grossman, T., Cross, E. S., Ticini, L. F. & Daum, M. M. (2013). Action observation in the infant brain: The role of body form and motion. Social Neuroscience, 8(1), 22-30.

Klapper, A., Ramsey, R., Wigboldus, D. H. J., Cross, E. S. (2014). The control of automatic imitation based on bottom-up and top-down cues to animacy: Insights from brain and behaviour . Journal of Cognitive Neuroscience, 26(11), 2503-2513.

Ramsey, R., & Hamilton, A. F. de C. (2010). Triangles have goals too: understanding action representation in left aIPS. Neuropsychologia, 48, 2773–2776.

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