Anqi Zhang

Visual search is a fundamental natural task for humans and other animals. We investigated the decision processes that humans use in covert (single fixation) search with briefly presented displays having well-separated potential target locations. Performance was compared with the Bayesian-optimal decision process under the assumption that the information from the different potential target locations is statistically independent. Surprisingly, humans performed slightly better than optimal, despite humans' substantial loss of sensitivity in the fovea ("foveal neglect") and the implausibility of the human brain replicating the optimal computations. We show that three factors can quantitatively explain these seemingly paradoxical results. Most importantly, simple and fixed heuristic decision rules reach near optimal search performance. Second, foveal neglect primarily affects only the central potential target location. Finally, spatially correlated neural noise can cause search performance to exceed that predicted for independent noise. These findings have broad implications for understanding visual search tasks and other identification tasks in humans and other animals.

A number of recent studies have been directed at measuring and modeling detection of targets at specific locations in natural backgrounds, a key subtask of visual search in natural environments. A useful approach is to bin natural background patches into joint histograms with bins along specific background dimensions. By measuring psychometric functions in a sparse subset of these bins, it is possible to estimate how the included dimensions jointly affect detectability over the whole space of natural backgrounds. In previous studies, we found that threshold is proportional to the product of the background luminance, contrast, and similarity; a result predicted by a simple template-matching observer with divisive normalization along each of the dimensions. The measure of similarity was the cosine similarity of the amplitude spectra of the target and background (S_A)—a phase-invariant measure. Here, we investigated the effect of the cosine similarity of the target and background images (S_I|A)—a phase-dependent measure. We found that threshold decreases monotonically with S_I|A in agreement with a recent study (Rideaux et al., 2022). In contrast, the template-matching observer predicts threshold to be a U-shaped function of S_I|A reaching a minimum when the target and background are orthogonal (S_I|A = 0). Surprisingly, when the template-matching observer includes a small amount of intrinsic position uncertainty (measured in a separate experiment) the pattern of thresholds is explained.

Most studies of detection in complex backgrounds have measured and modeled human performance for statistically uniform (stationary) backgrounds. However, natural and medical images have statistical properties that vary over space. We measured detection of various target shapes presented in Gaussian 1/f noise backgrounds that were statistically uniform over space, and in ones that modulated in contrast over space. We find that the pattern of human thresholds is not consistent with the ideal observer but is consistent with a suboptimal observer that performs partial whitening in spatial frequency and whitening (reliability-weighting) in space, and has a small level of intrinsic position uncertainty.