Centre for Cognitive Science, Technical University Darmstadt, 64283 Darmstadt, Germany.
Institute of Psychology, Technical University Darmstadt, 64283 Darmstadt, Germany.
Proc Natl Acad Sci U S A. 2018 Feb 27;115(9):2246-2251. doi: 10.1073/pnas.1714220115. Epub 2018 Feb 14.
Eye blinking is one of the most frequent human actions. The control of blinking is thought to reflect complex interactions between maintaining clear and healthy vision and influences tied to central dopaminergic functions including cognitive states, psychological factors, and medical conditions. The most imminent consequence of blinking is a temporary loss of vision. Minimizing this loss of information is a prominent explanation for changes in blink rates and temporarily suppressed blinks, but quantifying this loss is difficult, as environmental regularities are usually complex and unknown. Here we used a controlled detection experiment with parametrically generated event statistics to investigate human blinking control. Subjects were able to learn environmental regularities and adapted their blinking behavior strategically to better detect future events. Crucially, our design enabled us to develop a computational model that allows quantifying the consequence of blinking in terms of task performance. The model formalizes ideas from active perception by describing blinking in terms of optimal control in trading off intrinsic costs for blink suppression with task-related costs for missing an event under perceptual uncertainty. Remarkably, this model not only is sufficient to reproduce key characteristics of the observed blinking behavior such as blink suppression and blink compensation but also predicts without further assumptions the well-known and diverse distributions of time intervals between blinks, for which an explanation has long been elusive.
眨眼是人类最频繁的动作之一。眨眼的控制被认为反映了维持清晰健康的视觉与包括认知状态、心理因素和医疗状况在内的中脑多巴胺功能之间的复杂相互作用。眨眼最直接的后果是暂时失去视力。最小化这种信息丢失是解释眨眼频率和暂时抑制眨眼的主要原因，但量化这种丢失很困难，因为环境规律通常很复杂且未知。在这里，我们使用带有参数生成事件统计数据的受控检测实验来研究人类眨眼控制。实验对象能够学习环境规律，并战略性地调整眨眼行为，以更好地检测未来的事件。至关重要的是，我们的设计使我们能够开发一种计算模型，该模型可以根据任务表现来量化眨眼的后果。该模型通过描述在内在抑制成本与感知不确定性下错过事件的任务相关成本之间进行权衡的最优控制，从主动感知的角度来形式化眨眼的概念。值得注意的是，该模型不仅足以再现观察到的眨眼行为的关键特征，如眨眼抑制和眨眼补偿，而且还可以在没有进一步假设的情况下预测到众所周知的和多样化的眨眼时间间隔分布，而这种分布的解释长期以来一直难以捉摸。
