Seeing absence
Seeing absence
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看见缺席
作者：
Anya Farennikova
凭直觉，我们经常看到缺席。例如，如果有人在咖啡馆偷了您的笔记本电脑，您可能会发现它不在您的桌子上。然而，缺勤知觉呈现出一个悖论。在流行的感知模型中，我们只看到当前的物体和场景（Marr、Gibson、Dretske）。因此，我们实际上无法看到不存在的东西。这表明我们从来没有真正感知到缺席；相反，我们开始相信基于我们所感知的认知，某些东西是不存在的。但这种认知解释并不能公正地对待现象学。许多缺席的经历都具有直接的、感知的品质。人们可能会进一步争辩说，发现某些缺席的能力赋予了强大的适应性优势，因此对人类来说必须像看到积极的事物一样原始和基本。我认为我们确实可以看到缺席情况；除了表示物体之外，知觉还表示物体的不存在。我提出了一个基于视觉期望和视觉匹配过程的视觉缺席模型。因此，看到缺失的现象可以作为感知内容理论的充分性检验。如果缺席的体验是可能的，那么我们就有另一个理由（继西格尔之后）拒绝感知内容仅限于颜色和形状的观点。此外，如果所提出的解释是正确的，那么我们就有理由将视缺席与其他基于图像的现象（称为“缺席中的感知存在”）区分开来（Noë，Macpherson）。
Intuitively, we often see absences. For example, if someone steals your laptop at a café, you may see its absence from your table. However, absence perception presents a paradox. On prevailing models of perception, we see only present objects and scenes (Marr, Gibson, Dretske). So, we cannot literally see something that is not present. This suggests that we never literally perceive absences; instead, we come to believe that something is absent cognitively on the basis of what we perceive. But this cognitive explanation does not do justice to the phenomenology. Many experiences of absence possess immediate, perceptual qualities. One may further argue that the ability to detect certain absences confers strong adaptive advantage and therefore must be as primitive and fundamental to humans as seeing positive things. I argue that we can literally see absences; in addition to representing objects, perception represents absences of objects. I present a model of seeing absence based on visual expectations and a visual matching process. The phenomenon of seeing absence can thus serve as an adequacy-test for a theory of perceptual content. If experiences of absence are possible, then we have another reason (following Siegel) to reject the view that perceptual content is restricted to colors and shapes. Furthermore, if the proposed account is correct, then we have grounds for dissociating seeing absence from other imagery-based phenomena termed “perceptual presence-in-absence” (Noë, Macpherson).
影响因子：
3.7
作者：
Esterman, Michael;Yantis, Steven
通讯作者：
Yantis, Steven