Figure 1: Two camouflage images produced by our technique. The left and right images have seven and four camouflaged objects, respectively, at various levels of difficulty. By removing distinguishable elements from the camouflaged objects we make feature search difficult, forcing the viewers to use conjunction search, a serial and delayed procedure. (Please zoom in for a better effect. Answer keys are on the last page.) Camouflage images contain one or more hidden figures that remain imperceptible or unnoticed for a while. In one possible explanation, the ability to delay the perception of the hidden figures is attributed to the theory that human perception works in two main phases: feature search and conjunction search. Effective camouflage images make feature based recognition difficult, and thus force the recognition process to employ conjunction search, which takes considerable effort and time. In this paper, we present a technique for creating camouflage images. To foil the feature search, we remove the original subtle texture details of the hidden figures and replace them by that of the surrounding apparent image. To leave an appropriate degree of clues for the conjunction search, we compute and assign new tones to regions in the embedded figures by performing an optimization between two conflicting terms, which we call immersion and standout, corresponding to hiding and leaving clues, respectively. We show a large number of camouflage images generated by our technique, with or without user guidance. We have tested the quality of the images in an extensive user study, showing a good control of the difficulty levels. 1

The authors investigated whether the gestalt grouping principles can be quantified and whether the conjoint effects of two grouping principles operating at the same time on the same stimuli differ from the sum of their individual effects. After reviewing earlier attempts to discover how grouping principles interact, they developed a probabilistic model of grouping by proximity, which allows measurement of strength on a ratio scale. Then, in 3 experiments using dot lattices, they showed that the strength of the conjoint effect of 2 grouping principles—grouping by proximity and grouping by similarity—is equal to the sum of their separate effects. They propose a physiologically plausible model of this law.

Theories of probabilistic cognition postulate that internal representations are made up of multiple simultaneously held hypotheses, each with its own probability of being correct (henceforth, “probability distributions”). However, subjects make discrete responses and report the phenomenal contents of their mind to be all-or-none states rather than graded probabilities. How can these 2 positions be reconciled? Selective attention tasks, such as those used to study crowding, the attentional blink, rapid serial visual, and so forth, were recast as probabilistic inference problems and used to assess how graded, probabilistic representations may produce discrete subjective states. The authors asked subjects to make multiple guesses per trial and used 2nd-order statistics to show that (a) visual selective attention operates in a graded fashion in time and space, selecting multiple targets to varying degrees on any given trial; and (b) responses are generated by a process of sampling from the probabilistic states that result from graded selection. The authors concluded that although people represent probability distributions, their discrete responses and conscious states are products of a process that samples from these probabilistic representations.

Abstract not found

VERSION: 2010-2011: Document creation, done by O. Le Meur. 2011-2012: Correction, adding a slide on face/horizon line, done by O. Le Meur.

Given that the core issues of attention research have been recognized for millenia, we do not know as much about attention as we should. I argue that the reasons for this failure are (1) we create spurious dichotomies, (2) we reify attention, treating it as a cause, when it is an effect, and (3) we equate a collection of facts with a theory. In order to correct these errors, we need a new technical vocabulary that allows for attentional effects to be continuously distributed, rather than merely present or absent, and that provides a basis for quantitative behavioral predictions that map onto neural substrates. The terminology of the Bayesian decision process has already proved useful for structuring conceptual discussions in other psychological domains, such as perception and decision making under uncertainty, and it had demonstrated early success in the domain of attention. By rejecting a reified, causal conception of attention, in favor of theories that produce attentional effects as consequences, psychologists will be able to conduct more definitive experiments. Such conceptual advances will then enhance the productivity of neuroscientists by allowing them to concentrate their data collection efforts on the richest soil.

A hidden image is a form of artistic expression in which one or more secondary objects (or scenes) are hidden within a primary image. Features of the primary image, especially its edges and texture, are used to portray a secondary object. People can recognize both the primary and secondary intent in such pictures, although the time taken to do so depends on the prior experience of the viewer and the strength of the clues. Here, we present a system for creating such images. It relies on the ability of human perception to recognize an object, e.g. a human face, from incomplete edge information within its interior, rather than its outline. Our system detects edges of the object to be hidden, and then finds a place where it can be embedded within the scene, together with a suitable transformation for doing so, by optimizing an energy based on edge differences. Embedding is perfromed using a modified Poisson blending approach, which strengthens matched edges of the host image using edges of the object being embedded. We show various hidden images generated by our system.

A fundamental goal of visualization is to produce images of data that support visual analysis, exploration, and discovery of novel insights. An important consideration during visualization design is the role of human visual perception. How we “see” details in an image can directly impact a viewer’s efficiency and effectiveness. This article surveys research on attention and visual perception, with a specific focus on results that have direct relevance to visualization and visual analytics. We discuss theories of low-level visual perception, then show how these findings form a foundation for more recent work on visual memory and visual attention. We conclude with a brief overview of how knowledge of visual attention and visual memory is being applied in visualization and graphics. We also discuss how challenges in visualization are motivating research in psychophysics.

non-retinotopic processing