Attention in Cognitive Systems

1. Front Matter

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2. Attention in Scene Exploration

   1. On the Optimality of Spatial Attention for Object Detection

      • Jonathan Harel, Christof Koch

      Pages 1-14

   2. Decoding What People See from Where They Look: Predicting Visual Stimuli from Scanpaths

      • Moran Cerf, Jonathan Harel, Alex Huth, Wolfgang Einhäuser, Christof Koch

      Pages 15-26

   3. A Novel Hierarchical Framework for Object-Based Visual Attention

      • Rebecca Marfil, Antonio Bandera, Juan Antonio Rodríguez, Francisco Sandoval

      Pages 27-40

   4. Where Do We Grasp Objects? – An Experimental Verification of the Selective Attention for Action Model (SAAM)

      • Christoph Böhme, Dietmar Heinke

      Pages 41-53

3. Contextual Cueing and Saliency

   1. Integrating Visual Context and Object Detection within a Probabilistic Framework

      • Roland Perko, Christian Wojek, Bernt Schiele, Aleš Leonardis

      Pages 54-68

   2. The Time Course of Attentional Guidance in Contextual Cueing

      • Andrea Schankin, Anna Schubö

      Pages 69-84

   3. Conspicuity and Congruity in Change Detection

      • Jean Underwood, Emma Templeman, Geoffrey Underwood

      Pages 85-97

4. Spatiotemporal Saliency

   1. Spatiotemporal Saliency: Towards a Hierarchical Representation of Visual Saliency

      • Neil D. B. Bruce, John K. Tsotsos

      Pages 98-111

   2. Motion Saliency Maps from Spatiotemporal Filtering

      • Anna Belardinelli, Fiora Pirri, Andrea Carbone

      Pages 112-123

5. Attentional Networks

   1. Model Based Analysis of fMRI-Data: Applying the sSoTS Framework to the Neural Basic of Preview Search

      • Eirini Mavritsaki, Harriet Allen, Glyn Humphreys

      Pages 124-138

   2. Modelling the Efficiencies and Interactions of Attentional Networks

      • Fehmida Hussain, Sharon Wood

      Pages 139-152

   3. The JAMF Attention Modelling Framework

      • Johannes Steger, Niklas Wilming, Felix Wolfsteller, Nicolas Höning, Peter König

      Pages 153-165

6. Attentional Modeling

   1. Modeling Attention and Perceptual Grouping to Salient Objects

      • Thomas Geerinck, Hichem Sahli, David Henderickx, Iris Vanhamel, Valentin Enescu

      Pages 166-182

   2. Attention Mechanisms in the CHREST Cognitive Architecture

      • Peter C. R. Lane, Fernand Gobet, Richard Ll. Smith

      Pages 183-196

   3. Modeling the Interactions of Bottom-Up and Top-Down Guidance in Visual Attention

      • David Henderickx, Kathleen Maetens, Thomas Geerinck, Eric Soetens

      Pages 197-211

   4. Relative Influence of Bottom-Up and Top-Down Attention

      • Matei Mancas

      Pages 212-226

   5. Towards Standardization of Evaluation Metrics and Methods for Visual Attention Models

      • Muhammad Zaheer Aziz, Bärbel Mertsching

      Pages 227-241

   6. Comparing Learning Attention Control in Perceptual and Decision Space

      • Maryam S. Mirian, Majid Nili Ahmadabadi, Babak N. Araabi, Ronald R. Siegwart

      Pages 242-256

   7. Automated Visual Attention Manipulation

      • Tibor Bosse, Rianne van Lambalgen, Peter-Paul van Maanen, Jan Treur

      Pages 257-272

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 requiredrelevant 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.

• cognitive science
• cognitive systems
• computational vision
• computer vision
• context awareness
• neuroscience
• object detection
• perception
• scene recognition
• video analysis
• visual attention
• visual perception
• visual search

• Joanneum Research, Graz, Austria

  Lucas Paletta

• Center for Vision Research (CVR) and, Department of Computer Science and Engineering, York University, Toronto, Canada

  John K. Tsotsos

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