Invited Talks

Embodied Emotion and Attention

Tom Ziemke
School of Humanities & Informatics
University of Skövde, Sweden

Abstract:

Emotions can be considered as embodied appraisals, i.e. perceptions of bodily changes that reflect selected concern-relevant aspects of the dynamics of agent-environment interaction. Emotional attunement to changing situations, ranging from relatively long-lasting background emotions (e.g. moods) to 'short-term' emotions (e.g. fear), provides agents with attention in the sense of a dynamic modulation of action tendencies and affordance perception. While robotic and living bodies certainly differ in their (quasi-) physiological reactions, the concepts of embodied appraisal and emotional attunement can also usefully be modeled in robots. The talk provides a theoretical discussion of the relation between embodiment, emotion and attention, and provides examples from a European bio-inspired cognitive robotics project called ICEA Integrating Cognition, Emotion and Autonomy).

Learning to Attend: From Bottom-Up to Top-Down

the presentation had to be cancelled due to sickness

Jochen Triesch
Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
and Department of Cognitive Science, Univ. of California, San Diego, USA

Abstract:

The control of overt visual attention relies on an interplay of "bottom-up" and "top-down" mechanisms. Purely "bottom-up" models cannot provide a satisfactory account of human attention orienting in many natural behaviors. But how do humans learn to incorporate "top-down" mechanisms into their control of attention? The phenomenon of "gaze following", i.e. the ability to infer where someone else is looking, offers an interesting window into this question. I review findings on the emergence of gaze following in human infants and present a computational model of the underlying learning processes. The model proposes an explanation for the gradual shift of emphasis from bottom-up to top-down cues in attention control. It explains this process in terms of generic reinforcement learning mechanisms and predicts a new class of "mirror neurons" specific for looking behaviors. Finally, it offers an explanation for deficits in gaze following in developmental disorders such as autism.