New Developments in Visual Attention Research

Damien Delagarza (Editor)

Series: Psychology Research Progress
BISAC: MED009000

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The human brain deals, at every instant, with a huge amount of visual stimuli. Besides that, the problem of treating all this information becomes even more complex if we consider that each component of a given stimuli needs to be compared to a set of known signals stored in memory. In Chapter One a numerical solution of Hodgkin Huxley equations is presented to describe the behavior of a neuron and the solution is illustrated by a graphical chart interface to finely tune the behavior of the neuron visually programmed in Java. Chapter Two explores the connection between visual attention algorithms and the recognition of objects by computers in digital images. Chapter Three reviews research and provides original data asserting that bias in legal judgment persists despite the inclusion of visual evidence partly because decision-makers’ perceptions of visual evidence may be swayed by subjective factors. The preference for a product is usually influenced by the visual appearance of the product image. Chapter Four proposes a new content-based approach, denominated CBAS, that combines textual attributes, visual features and visual attention to compose the products profile. Chapter Five uses electroencephalography (EEG) to investigate the brain activations of visual attention in production designers and analyse the differences between higher creativity (HC) and lower creativity (LC) designers. (Imprint: Nova)

Preface

Chapter 1. Simulating the Visual Attention Process by a Multi-Agent Approach
Terje Kristensen (Department of Computing, Physics and Mathematics, Western Norway University of Applied Sciences, Bergen, Norway, and others)

Chapter 2. Object Recognition Guided by Visual Attention Algorithms
Rafael G. de Mesquita and Carlos A. B. Mello (Centro de Informática, Universidade Federal de Pernambuco, Recife, Brasil)

Chapter 3. One-Sighted: How Visual Attention Biases Legal Decision-Making
Anni Sternisko, Yael Granot and Emily Balcetis (Department of Psychology, New York University, New York, NY, USA, and others)

Chapter 4. A Content-Based Image Recommending System by Using Textual, Visual and Attention Data
Ernani Viriato de Melo (Federal Institute of Triângulo Mineiro (IFTM), Uberaba, MG, Brazil)

Chapter 5. Investigating How the Brain Activations of Visual Attention Differ among Designers with Different Levels of Creativity
Yu-Cheng Liu and Chaoyun Liang (Department of Bio-Industry Communication and Development, National Taiwan University, Taipei, Taiwan)

Index

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