Jiri Najemnik

Optimal eye movement strategies in visual search

To perform visual search, humans, like many mammals, encode a large field of view with retinas having variable spatial resolution, and then use high-speed eye movements to direct the highest-resolution region, the fovea, towards potential target locations. Good search performance is essential for survival, and hence mammals may have evolved efficient strategies for selecting fixation locations. Here we address two questions: what are the optimal eye movement strategies for a foveated visual system faced with the problem of finding a target in a cluttered environment, and do humans employ optimal eye movement strategies during a search? We derive the ideal bayesian observer for search tasks in which a target is embedded at an unknown location within a random background that has the spectral characteristics of natural scenes. Our ideal searcher uses precise knowledge about the statistics of the scenes in which the target is embedded, and about its own visual system, to make eye movements that gain the most information about target location. We find that humans achieve nearly optimal search performance, even though humans integrate information poorly across fixations. Analysis of the ideal searcher reveals that there is little benefit from perfect integration across fixations—much more important is efficient processing of information on each fixation. Apparently, evolution has exploited this fact to achieve efficient eye movement strategies with minimal neural resources devoted to memory.

Visual search: The role of peripheral information measured using gaze-contingent displays

Two of the factors limiting progress in understanding the mechanisms of visual search are the difficulty of controlling and manipulating the retinal stimulus when the eyes are free to move and the lack of an ideal observer theory for fixation selection during search. Recently, we developed a method to precisely control retinal stimulation with gaze-contingent displays (J. S. Perry & W. S. Geisler, 2002), and we derived a theory of optimal eye movements in visual search (J. Najemnik & W. S. Geisler, 2005). Here, we report a parametric study of visual search for sine-wave targets added to spatial noise backgrounds that have spectral characteristics similar to natural images (the amplitude spectrum of the noise falls inversely with spatial frequency). Search time, search accuracy, and eye fixations were measured as a function of target spatial frequency, 1/f noise contrast, and the resolution falloff of the display from the point of fixation. The results are systematic and similar for the two observers. We find that many aspects of search performance and eye movement pattern are similar to those of an ideal searcher that has the same falloff in resolution with retinal eccentricity as the human visual system.

Simple summation rule for optimal fixation selection in visual search

When searching for a known target in a natural texture, practiced humans achieve near-optimal performance compared to a Bayesian ideal searcher constrained with the human map of target detectability across the visual field [Najemnik, J., & Geisler, W. S. (2005). Optimal eye movement strategies in visual search. Nature, 434, 387-391]. To do so, humans must be good at choosing where to fixate during the search [Najemnik, J., & Geisler, W.S. (2008). Eye movement statistics in humans are consistent with an optimal strategy. Journal of Vision, 8(3), 1-14. 4]; however, it seems unlikely that a biological nervous system would implement the computations for the Bayesian ideal fixation selection because of their complexity. Here we derive and test a simple heuristic for optimal fixation selection that appears to be a much better candidate for implementation within a biological nervous system. Specifically, we show that the near-optimal fixation location is the maximum of the current posterior probability distribution for target location after the distribution is filtered by (convolved with) the square of the retinotopic target detectability map. We term the model that uses this strategy the entropy limit minimization (ELM) searcher. We show that when constrained with human-like retinotopic map of target detectability and human search error rates, the ELM searcher performs as well as the Bayesian ideal searcher, and produces fixation statistics similar to human.