Higher-level goals in the processing of human action events

The concept of a goal critically separates dynamic events involving humans from other events. Human behaviours are motivated by goals, which are known to the actor but typically inferred on the part of the observer. Goals can be hierarchical in nature, such that a collection of sub-goals (e.g., getting a mug, boiling water) can be nested under a higher-level goal (e.g., making tea), which can be further nested under an even higher-level goal (e.g., making breakfast). The diverse set of talks in this symposia all highlight the foundational role that goals play in action processing and representation. Eisenberg et al. detail how online prediction of others’ goals shapes observers’ sampling of information during action observation. Howard and Woodward provide evidence that children’s memory for non-human events can be facilitated by priming children with their own goal-directed actions. Loucks and Meltzoff highlight the importance of goal structure in children’s memory for complex action sequences. Finally, Cooper presents a computational model to explain the emergence of goal-directed action hierarchies

Enhancing human computer interaction with episodic memory in a virtual guide

Rabe F, Wachsmuth I. Enhancing human computer interaction with episodic memory in a virtual guide. In: Kurosu M, ed. Human-Computer Interaction: Interaction Modalities and Techniques, Part IV, HCII 2013. Lecture Notes in Computer Science. Vol 8007. Heidelberg: Springer; 2013: 117-125.Have you ever found yourself in front of a computer and asking it aloud: “Why?” We have constructed a cognitively motivated episodic memory system that enables a virtual guide to respond to this question. The guide, a virtual agent based on a belief–desire–intention (BDI) architecture, is employed in a Virtual Reality (VR) scenario where he accompanies a human visitor on a tour through a city. In this paper we explain how the agents memorizes events and episodes according to an event-indexing model and how the interaction is enhanced by using these memories. We argue that due to the cognitively motivated nature of the event-indexing model every interaction situation can be described, memorized, recalled and explained by the agent