Tatler Benjamin W, Hayhoe Mary M, Land Michael F, Ballard Dana H
School of Psychology, University of Dundee, Dundee, UK.
J Vis. 2011 May 27;11(5):5. doi: 10.1167/11.5.5.
Models of gaze allocation in complex scenes are derived mainly from studies of static picture viewing. The dominant framework to emerge has been image salience, where properties of the stimulus play a crucial role in guiding the eyes. However, salience-based schemes are poor at accounting for many aspects of picture viewing and can fail dramatically in the context of natural task performance. These failures have led to the development of new models of gaze allocation in scene viewing that address a number of these issues. However, models based on the picture-viewing paradigm are unlikely to generalize to a broader range of experimental contexts, because the stimulus context is limited, and the dynamic, task-driven nature of vision is not represented. We argue that there is a need to move away from this class of model and find the principles that govern gaze allocation in a broader range of settings. We outline the major limitations of salience-based selection schemes and highlight what we have learned from studies of gaze allocation in natural vision. Clear principles of selection are found across many instances of natural vision and these are not the principles that might be expected from picture-viewing studies. We discuss the emerging theoretical framework for gaze allocation on the basis of reward maximization and uncertainty reduction.
复杂场景中注视分配的模型主要源自对静态图片观看的研究。已出现的主导框架是图像显著性，其中刺激的属性在引导眼睛方面起着关键作用。然而，基于显著性的方案在解释图片观看的许多方面时表现不佳，并且在自然任务执行的背景下可能会严重失败。这些失败促使了场景观看中注视分配新模型的发展，这些新模型解决了其中的一些问题。然而，基于图片观看范式的模型不太可能推广到更广泛的实验情境中，因为刺激情境有限，且视觉的动态、任务驱动性质未得到体现。我们认为有必要摆脱这类模型，找到在更广泛环境中支配注视分配的原则。我们概述了基于显著性的选择方案的主要局限性，并强调了我们从自然视觉中注视分配研究中学到的内容。在许多自然视觉实例中都发现了明确的选择原则，而这些并非图片观看研究所预期的原则。我们基于奖励最大化和不确定性降低来讨论新兴的注视分配理论框架。
