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Attention has represented a core scienti?c topic in the design of AI-enabled systems in the last few decades. Today, in the ongoing debate, design, and c- putationalmodelingofarti?cialcognitivesystems, attentionhasgainedacentral position as a focus of research. For instance, attentional methods are considered in investigating the interfacing of sensory and cognitive information processing, for the organization of behaviors, and for the understanding of individual and social cognition in infant development. Whilevisualcognitionplaysacentralroleinhumanperception, ?ndingsfrom neuroscience and experimental psychology have provided strong evidence about the perception-action nature of cognition. The embodied nature of senso- motor intelligence requires a continuous and focused interplay between the c- trolofmotoractivitiesandtheinterpretationoffeedbackfromperceptualmod- ities. Decision making about the selection of information from the incoming sensory stream - in tune with contextual processing on a current task and an agent´s global objectives - becomes a further challenging issue in attentional control. Attention must operate at interfaces between a bottom-up-driven world interpretationandtop-down-driveninformationselection, thusactingatthecore of arti?cial cognitive systems. These insights have already induced changes in AI-related disciplines, such as the design of behavior-based robot control and the computational modeling of animats. Today, the development of enabling technologiessuch as autonomous robotic systems, miniaturizedmobile-evenwearable-sensors, andambientintelligence systems involves the real-time analysis of enormous quantities of data. These data have to be processed in an intelligent way to provide ´on time delivery´ of the required relevant information. Knowledge has to be applied about what needs to be attended to, and when, and what to do in a meaningful sequence, in correspondence with visual feedback.



Table of Contents:
Attention in Scene Exploration.- On the Optimality of Spatial Attention for Object Detection.- Decoding What People See from Where They Look: Predicting Visual Stimuli from Scanpaths.- A Novel Hierarchical Framework for Object-Based Visual Attention.- Where Do We Grasp Objects? An Experimental Verification of the Selective Attention for Action Model (SAAM).- Contextual Cueing and Saliency.- Integrating Visual Context and Object Detection within a Probabilistic Framework.- The Time Course of Attentional Guidance in Contextual Cueing.- Conspicuity and Congruity in Change Detection.- Spatiotemporal Saliency.- Spatiotemporal Saliency: Towards a Hierarchical Representation of Visual Saliency.- Motion Saliency Maps from Spatiotemporal Filtering.- Attentional Networks.- Model Based Analysis of fMRI-Data: Applying the sSoTS Framework to the Neural Basic of Preview Search.- Modelling the Efficiencies and Interactions of Attentional Networks.- The JAMF Attention Modelling Framework.- Attentional Modeling.- Modeling Attention and Perceptual Grouping to Salient Objects.- Attention Mechanisms in the CHREST Cognitive Architecture.- Modeling the Interactions of Bottom-Up and Top-Down Guidance in Visual Attention.- Relative Influence of Bottom-Up and Top-Down Attention.- Towards Standardization of Evaluation Metrics and Methods for Visual Attention Models.- Comparing Learning Attention Control in Perceptual and Decision Space.- Automated Visual Attention Manipulation.´