Hendler Ori, Segev Ronen, Shamir Maoz
Department of Physics, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
School of Brain Sciences and Cognition, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
PLoS Comput Biol. 2025 May 7;21(5):e1013092. doi: 10.1371/journal.pcbi.1013092. eCollection 2025 May.
Visual 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 types of WTA readout mechanisms: 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 neither WTA mechanism can account for the high accuracy found in behavioral experiments. The inherent neuronal heterogeneity prevents the single-best-cell WTA from accumulating information even from large populations, whereas the accuracy of the generalized population-based WTA algorithm is negatively affected by the widely reported noise correlations. These findings underscore the need to revisit the key assumptions explored in our theoretical analysis, particularly concerning the decoding mechanism and the statistical properties of neuronal population responses to pop out stimuli. The analysis identifies specific response statistics that require further empirical characterization to accurately predict WTA performance in biologically plausible models of visual pop out detection.
视觉搜索涉及在无关干扰物的背景下主动扫视环境，以定位感兴趣的物体。一种被广泛接受的理论认为，弹出式视觉搜索是由形成显著性图的上下文调制细胞之间的胜者全得（WTA）竞争来计算的。然而，先前的研究表明，WTA机制从大量神经元中积累信息的能力是有限的，因此引发了WTA是否能够作为弹出式视觉搜索基础的问题。为了解决这个问题，我们进行了一项建模研究，以探究WTA机制在弹出式任务中检测异常刺激的准确程度。我们分析了两种类型的WTA读出机制：单最佳细胞WTA，其决策基于单个获胜细胞；以及广义群体WTA，其决策基于调谐相似的细胞的获胜群体。我们的结果表明，两种WTA机制都无法解释行为实验中发现的高精度。固有的神经元异质性阻止单最佳细胞WTA即使从大量群体中积累信息，而广义群体WTA算法的准确性受到广泛报道的噪声相关性的负面影响。这些发现强调了重新审视我们理论分析中探索的关键假设的必要性，特别是关于解码机制和神经元群体对弹出式刺激反应的统计特性。该分析确定了特定的反应统计数据，需要进一步的实证表征，以便在视觉弹出式检测的生物学合理模型中准确预测WTA性能。
