Ideal cue combination for localizing texture-defined edges

TLDR

Accurate visual perception relies on integrating multiple sources of information, and optimal performance requires combining these cues. The study used two vernier experiments that varied texture frequency, contrast, and orientation cues, manipulated cue reliability through texture blur, perturbed cue alignment, and fitted an ideal‑observer model to the resulting data. The ideal‑observer model captured most data trends, whereas a trial‑by‑trial cue‑switching model performed poorly.

Abstract

Many visual tasks can be carried out by using several sources of information. The most accurate estimates of scene properties require the observer to utilize all available information and to combine the information sources in an optimal manner. Two experiments are described that required the observers to judge the relative locations of two texture-defined edges (a vernier task). The edges were signaled by a change across the edge of two texture properties [either frequency and orientation (Experiment 1) or contrast and orientation (Experiment 2)]. The reliability of each cue was controlled by varying the distance over which the change (in frequency, orientation, or contrast) occurred—a kind of "texture blur." In some conditions, the position of the edge signaled by one cue was shifted relative to the other ("perturbation analysis"). An ideal-observer model, previously used in studies of depth perception and color constancy, was fitted to the data. Although the fit can be rejected relative to some more elaborate models, especially given the large quantity of data, this model does account for most trends in the data. A second, suboptimal model that switches between the available cues from trial to trial does a poor job of accounting for the data.