Can Peripheral Representations Improve Clutter Metrics on Complex Scenes?

Previous studies have proposed image-based clutter measures that correlate with human search times and/or eye movements. However, most models do not take into account the fact that the effects of clutter interact with the foveated nature of the human visual system: visual clutter further from the fovea has an increasing detrimental influence on perception. Here, we introduce a new foveated clutter model to predict the detrimental effects in target search utilizing a forced fixation search task. We use Feature Congestion (Rosenholtz et al.) as our non foveated clutter model, and we stack a peripheral architecture on top of Feature Congestion for our foveated model. We introduce the Peripheral Integration Feature Congestion (PIFC) coefficient, as a fundamental ingredient of our model that modulates clutter as a non-linear gain contingent on eccentricity. We show that Foveated Feature Congestion (FFC) clutter scores (r(44) = 0.82 0.04, p < 0.0001) correlate better with target detection (hit rate) than regular Feature Congestion (r(44) = 0.19 0.13, p = 0.0774) in forced fixation search; and we extend foveation to other clutter models showing stronger correlations in all cases. Thus, our model allows us to enrich clutter perception research by computing fixation specific clutter maps.