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Traditional models of visual search such as feature integration theory (FIT; Treisman & Gelade, 1980), have suggested that a key factor determining task difficulty consists of whether or not the search target contains a “basic feature” not found in the other display items (distractors). Here we discriminate between such traditional models and our recent texture tiling model (TTM) of search (Rosenholtz, Huang, Raj, Balas, & Ilie, 2012b), by designing new experiments that directly pit these models against each other. Doing so is nontrivial, for two reasons. First, the visual representation in TTM is fully specified, and makes clear testable predictions, but its complexity makes getting intuitions difficult. Here we elucidate a rule of thumb for TTM, which enables us to easily design new and interesting search experiments. FIT, on the other hand, is somewhat ill-defined and hard to pin down. To get around this, rather than designing totally new search experiments, we start with five classic experiments that FIT already claims to explain: T among Ls, 2 among 5s, Q among Os, O among Qs, and an orientation/luminance-contrast conjunction search. We find that fairly subtle changes in these search tasks lead to significant changes in performance, in a direction predicted by TTM, providing definitive evidence in favor of the texture tiling model as opposed to traditional views of search.

The TSSIM clutter metrics correlate amazingly well to both the experimental detection probabilities and the mean detection time. Based on the analysis of both probabilities of the correct detections and of the total (correct and false) number of detections made by human observers, a mathematical formula for predicting the probability of false alarms as a function of clutter metrics is presented in this paper. Comparing real experimental data with the predicted products reveal very good agreement, which is very helpful in understanding human behavior mechanisms regarding target detection tasks. It is concluded that the human observer behaves as fixed threshold signal processor /Non-CFAR.

One of the most challenging research topics in the field of target detection in electro-optical images is the relationship between image content and human detection performance. We propose two target structure similarity ͑TSSIM͒ metrics to describe global image clutter. Via a simple mathematical formula, the TSSIM clutter metrics and their combinations with different local target-to-background contrasts, which are loosely referred to as signal-to-clutter ratios ͑SCRs͒, are used to predict human detection performance. Other clutter measures and their combinations with the contrast ones are also considered for comparison. The degrees of correlation between target detection probabilities and various clutter metrics, as well as the SCR models, are presented. Experiment results show that the TSSIM measures are more suitable for quantifying electro-optical clutter than other clutter measures compared. And the SCR metric, which combines the root-sum-of-squares local contrast and a TSSIM clutter metric, generates the best predictive capabilities for the experimental detection probabilities. © 2006 Society of Photo-Optical Instrumentation Engineers.

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