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Tao Gao, George E. Newman, Brian J. Scholl; The psychophysics of chasing. Journal of Vision 2008;8(6):314. doi: 10.1167/8.6.314.
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The currency of visual experience consists not only of features such as color and shape, but also higher-level properties such as animacy. Psychologists have long been captivated by the fact that even simple moving geometric shapes may be perceived in animate, goal-directed terms. However, the study of such phenomena has been limited by two major challenges: (1) Previous research has had difficulty measuring animacy with quantitative precision, given the haphazard construction of typical stimuli. (2) Task demands have made it difficult to distinguish the perception of animacy from higher-level inferences, especially when using simple rating scales. Here we introduce two new converging methods that address both concerns. In the Search for Chasing task, subjects viewed many identical moving discs, and had to detect whether a chase was present: on half the trials, one disc (the ‘wolf’) pursued another disc (the ‘sheep’). Across trials, we manipulated ‘chasing subtlety’ - the degree to which the wolf could deviate from a perfectly ‘heat-seeking’ trajectory. Detection accuracy revealed both a robust perception of chasing (with small subtlety values), and an ability to infer chasing without direct perception (with larger subtlety values). The Don't Get Caught! task was similar, but now subjects controlled the sheep's trajectory via the computer mouse, with the goal of avoiding contact with the wolf after identifying it. Performance was a U-shaped function of chasing subtlety. Subjects readily avoided being caught with both large deviations (when the chasing was highly inexact in the first place) and small deviations (when the wolf was easily identified and thus avoided). With intermediate deviations, however, performance was poor: the wolf essentially ‘stalked’ the sheep in a manner that was difficult to detect. These results collectively demonstrate how the perception of animacy can be measured with precision and can be distinguished from higher-level inferences.
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