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Click to download PDF version Click to download BibTeX data Clik to view abstract V. Navalpakkam, L. Itti, Optimal feature gain modulation during visual search, In: Proc. Vision Science Society Annual Meeting (VSS06), May 2006. (Cited by 1)

Abstract: Despite substantial neurobiological and behavioral evidence that knowledge modulates feature processing and facilitates visual search, currently, there is no mathematical theory to capture such top-down influences. We propose an optimal theory of how prior statistical knowledge of target and distractor features modulates the response gains of neurons encoding low-level visual features, such that search speed is maximized. Through numerical simulations, we show that this theory successfully explains many reported behavioral and electrophysiological observations including top-down effects such as the role of priming, role of uncertainty, target enhancement and distractor suppression, as well as bottom-up effects such as pop-out, role of target-distractor discriminability, distractor heterogeneity, linear separabilty and others. Further, the theory makes surprising predictions whereby finding a target may sometimes require suppression of target features, or enhancement of non-target features. We validate these counter-intuitive predictions through new psychophysics experiments. Four naive subjects performed a difficult search for 55 degree oriented target among 50 degree distractors. The gains thus set up were tested by randomly inserting probe trials, in which we briefly flashed (200ms) four items representing the distractor (50 degree), the target (55 degree), relevant as predicted by the theory (60 degree), and steep (80 degree) cues. Although subjects searched for a 55 degree target, as predicted by the theory, there were significantly higher number of reports on the 60 degree item (paired t-test with p<0.05). These results provide direct experimental evidence that humans may deploy optimal feature gain modulation strategies.

Note: Recipient of VSS student travel fellowship

Themes: Computational Modeling, Model of Top-Down Attentional Modulation, Human Psychophysics


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