Frontal Eye Field (FEF) neurons discriminate between relevant and irrelevant visual stimuli and their response magnitude predicts conscious perception. How this is reflected in the spatial representation of a visual stimulus at the neuronal population level is unknown. We recorded neuronal population activity in the FEF while monkeys were performing a forced choice cued detection task with identical target and distractor stimuli. We quantified, using machine learning techniques, estimates of target and distractor location from FEF population multiunit activities. We found that the FEF population activity provides a precise single trial estimate of reported stimuli locations. Importantly, the closer this prefrontal population single trial estimate is to the veridical stimulus location, the higher the probability that the target or the distractor is reported as perceived. We show that stimulus perception is rescued by the estimate of attention allocation specifically when the latter is close enough to the actual stimulus location, thus indicating a partial independence between attention and perception. Overall, we thus show that how and what we perceive of our environment depends on the spatial precision with which this environment is coded by prefrontal neuronal populations.

中文翻译：

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神经元群体与目标选择和干扰过滤的相关性

额叶眼场 (FEF) 神经元区分相关和不相关的视觉刺激，它们的反应幅度预测有意识的感知。这如何反映在神经元群体水平的视觉刺激的空间表示中是未知的。我们记录了 FEF 中的神经元群体活动，而猴子正在执行具有相同目标和干扰物刺激的强制选择提示检测任务。我们使用机器学习技术对来自 FEF 群体多单元活动的目标和干扰物位置的估计进行了量化。我们发现 FEF 种群活动提供了对报告刺激位置的精确单次试验估计。重要的是，这个前额叶人口单次试验估计越接近真实的刺激位置，目标或干扰项被报告为感知的概率越高。我们表明，当注意力分配与实际刺激位置足够接近时，通过估计注意力分配来拯救刺激感知，从而表明注意力和感知之间存在部分独立性。总的来说，我们因此表明我们对环境的感知方式和感知取决于该环境由前额叶神经元群编码的空间精度。