Spatial attention: Visual selection and deployment over space The attentional spotlight and spatial cueing Attentional shifts, splits, and resolution Object-based Selection The visual search paradigm Top-down and bottom-up control of attention Inhibitory mechanisms of attention Invalid cueing Negative priming Inhibition of return Temporal attention: Visual selection and deployment over time Single target search Attentional blink and attentional dwell time Repetition blindness NEURAL MECHANISMS OF SELECTION Single-cell physiological method Event-related potentials Functional imaging: PET and fMRI

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In this paper, a novel model of object-based visual attention extending Duncan's Integrated Competition Hypothesis [24] is presented. In contrast to the attention mechanisms used in most previous machine vision systems which drive attention based on the spatial location hypothesis, the mechanisms which direct visual attention in our system are object-driven as well as feature-driven. The competition to gain visual attention occurs not only within an object but also between objects. For this purpose, two new mechanisms in the proposed model are described and analyzed in detail. The first mechanism computes the visual salience of objects and groupings; the second one implements the hierarchical selectivity of attentional shifts. The results of the new approach on synthetic and natural images are reported.

This article describes and evaluates a class of methods for performing automated analysis of eye-movement protocols. Although eye movements have become increasingly popular as a tool for investigating user behavior, they can be extremely difficult and tedious to analyze. In this article we propose an approach to automating eye-movement protocol analysis by means of tracing—relating observed eye movements to the sequential predictions of a process model. We present three tracing methods that provide fast and robust analysis and alleviate the equipment noise and individual variability prevalent in typical eye-movement protocols. We also describe three applications of the tracing methods that demonstrate how the methods facilitate the use of eye movements in the study of user behavior and the inference of user intentions. 1.

... In this article I define a taxonomy and three dimensions of simulated task environments. The dimensions are based on viewing simulated task environments from the perspectives of the researcher, the task, and the participants. Research on complex systems is inherently complex. It is my hope that the terms and distinctions introduced in this article will further the scientific enterprise by enabling us to spend less time explaining our paradigms and more time communicating our results

A theory is presented that attempts to answer two questions. What visual contents can an observer consciously access at one moment? Answer: only one feature value (e.g., green) per dimension, but those feature values can be associated (as a group) with multiple spatially precise locations (comprising a single labeled Boolean map). How can an observer voluntarily select what to access? Answer: in one of two ways: (a) by selecting one feature value in one dimension (e.g., selecting the color red) or (b) by iteratively combining the output of (a) with a preexisting Boolean map via the Boolean operations of intersection and union. Boolean map theory offers a unified interpretation of a wide variety of visual attention phenomena usually treated in separate literatures. In so doing, it also illuminates the neglected phenomena of attention to structure.

In this paper, we show how different elements of awareness

We present an approach towards a simple, explicit model of saccadic selectivity in visual search tasks. The model in its present state includes weights for target-distractor similarities and fixation field size as its only adjustable parameters. Based on these, the model predicts the statistical distribution of saccadic endpoints for any given visual search display. Besides providing an explicit and complete mathematical specification of the model, we demonstrate the performance of its computer simulation in a triple-conjunctive search task. The model successfully simulates empirical data reported by Williams and Reingold (in press). Modeling Visual Search How do we detect a prespecified target item among a set of distractors? Numerous studies employing the paradigm of

The gaze movements accompanying target localization were examined via human observers and a computational model (target acquisition model [TAM]). Search contexts ranged from fully realistic scenes to toys in a crib to Os and Qs, and manipulations included set size, target eccentricity, and target–distractor similarity. Observers and the model always previewed the same targets and searched identical displays. Behavioral and simulated eye movements were analyzed for acquisition accuracy, efficiency, and target guidance. TAM’s behavior generally fell within the behavioral mean’s 95% confidence interval for all measures in each experiment/condition. This agreement suggests that a fixed-parameter model using spatiochromatic filters and a simulated retina, when driven by the correct visual routines, can be a good general-purpose predictor of human target acquisition behavior.

Human vision uses mechanisms of covert attention to selectively process interesting information and overt eye movements to extend this selectivity ability. Thus, visual tasks can be effectively dealt with by limited processing resources. Modelling visual attention for machine vision systems is not only critical but also challenging. In the machine vision literature there have been many conventional attention models developed but they are all space-based only and cannot perform object-based selection. In consequence, they fail to work in real-world visual environments due to the intrinsic limitations of the space-based attention theory upon which these models are built. The aim of the work presented in this thesis is to provide a novel human-like visual selection framework based on the object-based attention theory recently being developed in psychophysics. The proposed solution -- a Hierarchical Object-based Attention Framework (HOAF) based on grouping competition, consists of two closely-coupled visual selection models of (1) hierarchical object-based visual (covert) attention and (2) object-based attention-driven (overt) saccadic eye movements. The Hierarchical Object-based Attention Model (HOAM) is the primary selection mechanism and the Object-based Attention-Driven Saccading model (OADS) has a supporting role, both of which are combined in the integrated visual selection framework HOAF.