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因果捕捉:情境对碰撞事件感知的影响。

Causal capture: contextual effects on the perception of collision events.

作者信息

Scholl Brian J, Nakayama Ken

机构信息

Department of Psychology, Yale University, New Haven, CT 06520-8205, USA.

出版信息

Psychol Sci. 2002 Nov;13(6):493-8. doi: 10.1111/1467-9280.00487.

Abstract

In addition to perceiving the colors, shapes, and motions of objects, observers can perceive higher-level properties of visual events. One such property is causation, as when an observer sees one object cause another object to move by colliding with it. We report a striking new type of contextual effect on the perception of such collision events. Consider an object (A) that moves toward a stationary object (B) until they are adjacent, at which point A stops and B starts moving along the same path. Such "launches" are perceived in terms beyond these kinematics: As noted in Michotte's classic studies, observers perceive A as being the cause of B's motion. When A and B fully overlap before B's motion, however observers often see this test event as a completely noncausal "pass": One object remains stationary while another passes over it. When a distinct launch event occurs nearby, however, the test event is "captured": It too is now irresistibly seen as causal. For this causal capture to occur, the context event need be present for only 50 ms surrounding the "impact," but capture is destroyed by only 200 ms of temporal asynchrony between the two events. We report a study of such cases, and others, that help define the rules that the visual system uses to construct percepts of seemingly high-level properties like causation.

摘要

除了感知物体的颜色、形状和运动外,观察者还能感知视觉事件的更高层次属性。其中一种属性就是因果关系,比如观察者看到一个物体通过与另一个物体碰撞使其移动。我们报告了一种对这类碰撞事件感知的惊人新型情境效应。考虑一个物体(A)朝着一个静止物体(B)移动,直到它们相邻,此时A停止,B开始沿相同路径移动。这种“发射”现象的感知超出了这些运动学范畴:正如米乔特的经典研究中所指出的,观察者将A视为B运动的原因。然而,当A和B在B运动之前完全重叠时,观察者通常会把这个测试事件视为一个完全无因果关系的“经过”:一个物体保持静止,而另一个物体从其上方经过。然而,当附近发生一个明显的发射事件时,这个测试事件就会被“捕获”:现在它也不可抗拒地被视为有因果关系。为了发生这种因果捕获,情境事件只需在“碰撞”周围出现50毫秒,但两个事件之间200毫秒的时间异步就会破坏捕获。我们报告了对这类情况及其他情况的一项研究,该研究有助于确定视觉系统用于构建诸如因果关系等看似高层次属性感知的规则。

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