Winner-take-all fails to account for pop out accuracy

Abstract

AbstractVisual search involves active scanning of the environment to locate objects of interest against a background of irrelevant distractors. One widely accepted theory posits that pop out visual search is computed by a winner-take-all (WTA) competition between contextually modulated cells that form a saliency map. However, previous studies have shown that the ability of WTA mechanisms to accumulate information from large populations of neurons is limited, thus raising the question of whether WTA can underlie pop out visual search. To address this question, we conducted a modeling study to investigate how accurately the WTA mechanism can detect the deviant stimulus in a pop out task. We analyzed two architectures of WTA networks: single-best-cell WTA, where the decision is made based on a single winning cell, and a generalized population-based WTA, where the decision is based on the winning population of similarly tuned cells. Our results show that WTA performance cannot account for the high accuracy found in behavioral experiments. On the one hand, inherent neuronal heterogeneity prevents the single-best-cell WTA from accumulating information even from large populations. On the other, the accuracy of the generalized population-based WTA algorithm is negatively affected by the widely reported noise correlations. These findings suggest the need for revisiting current understandings of the underlying mechanism of pop out visual search put forward to account for observed behavior.