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Wilson S. Geisler; Ideal Observer Analysis of Overt Attention. Journal of Vision 2010;10(7):12. doi: 10.1167/10.7.12.
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© ARVO (1962-2015); The Authors (2016-present)
In most natural tasks humans use information detected in the periphery, together with context and other task-dependent constraints, to select their fixation locations (i.e., the locations where they apply the specialized processing associated with the fovea). A useful strategy for investigating the overt-attention mechanisms that drive fixation selection is to begin by deriving appropriate normative (ideal observer) models. Such ideal observer models can provide a deep understanding of the computational requirements of the task, a benchmark against which to compare human performance, and a rigorous basis for proposing and testing plausible hypotheses for the biological mechanisms. In recent years, we have been investigating the mechanisms of overt attention for tasks in which the observer is searching for a known target randomly located in a complex background texture (nominally a background of filtered noise having the average power spectrum of natural images). This talk will summarize some of our earlier and more recent findings (for our specific search tasks): (1) practiced humans approach ideal search speed and accuracy, ruling out many sub-ideal models; (2) human eye movement statistics are qualitatively similar to those of the ideal searcher; (3) humans select fixation locations that make near optimal use of context (the prior over possible target locations); (4) humans show relatively rapid adaptation of their fixation strategies to simulated changes in their visual fields (e.g., central scotomas); (5) there are biologically plausible heuristics that approach ideal performance.
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