Attention in cognitive systems: 5th International Workshop on Attention in Cognitive Systems, WAPCV 2008, Fira, Santorini, Greece, May 12, 2008: revised selected papers / Lucas Paletta, John K. Tsotsos (eds.)

This volume constitutes the thoroughly refereed post-workshop proceedings of the 5th International Workshop on Attention in Cognitive Systems, WAPCV 2008, held in Fira, Santorini, Greece in May 2008 as an associated event of the 6th International Conference on Computer Vision Systems (ICVS 2008). The 13 revised full papers presented together with with 9 posters, have been carefully selected from 34 submissions. The papers are organized in topical sections on attention in scene exploration, contextueal cueing and saliency, spatiotemporal saliency, attentional networks as well as attentional modelling. 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. Front Matter....Pages - On the Optimality of Spatial Attention for Object Detection....Pages 1-14 Decoding What People See from Where They Look: Predicting Visual Stimuli from Scanpaths....Pages 15-26 A Novel Hierarchical Framework for Object-Based Visual Attention....Pages 27-40 Where Do We Grasp Objects? – An Experimental Verification of the Selective Attention for Action Model (SAAM)....Pages 41-53 Integrating Visual Context and Object Detection within a Probabilistic Framework....Pages 54-68 The Time Course of Attentional Guidance in Contextual Cueing....Pages 69-84 Conspicuity and Congruity in Change Detection....Pages 85-97 Spatiotemporal Saliency: Towards a Hierarchical Representation of Visual Saliency....Pages 98-111 Motion Saliency Maps from Spatiotemporal Filtering....Pages 112-123 Model Based Analysis of fMRI-Data: Applying the sSoTS Framework to the Neural Basic of Preview Search....Pages 124-138 Modelling the Efficiencies and Interactions of Attentional Networks....Pages 139-152 The JAMF Attention Modelling Framework....Pages 153-165 Modeling Attention and Perceptual Grouping to Salient Objects....Pages 166-182 Attention Mechanisms in the CHREST Cognitive Architecture....Pages 183-196 Modeling the Interactions of Bottom-Up and Top-Down Guidance in Visual Attention....Pages 197-211 Relative Influence of Bottom-Up and Top-Down Attention....Pages 212-226 Towards Standardization of Evaluation Metrics and Methods for Visual Attention Models....Pages 227-241 Comparing Learning Attention Control in Perceptual and Decision Space....Pages 242-256 Automated Visual Attention Manipulation....Pages 257-272 Back Matter....Pages -