透视图(图形)
感知
认知心理学
方向(向量空间)
心理旋转
心理学
认知
任务(项目管理)
视觉感受
点(几何)
社会心理学
计算机科学
人工智能
数学
神经科学
几何学
经济
管理
作者
Patric Bach,Giorgio Ganis,Patric Bach
出处
期刊:Current Biology
[Elsevier BV]
日期:2019-03-01
卷期号:29 (5): 874-880.e4
被引量:60
标识
DOI:10.1016/j.cub.2019.01.046
摘要
Visual perspective taking (VPT) is a core process of social cognition, providing humans with insights into what the environment looks like from another's point of view [1-4]. While VPT is often described as a quasi-perceptual phenomenon [5, 6], evidence for this proposal has been lacking. Here, we provide direct evidence that another's perspective can "stand in" for one's own sensory input during perceptual decision making. In a variant of the classic mental rotation task, participants judged whether characters presented in different orientations were canonical or mirror inverted. In the absence of another person, we replicate the well-established positive linear relationship between recognition times and angle of orientation such that recognition becomes slower the more an item has to be mentally rotated into its canonical orientation [7]. Importantly, this relationship was disrupted simply by placing another individual in the scene. Items rotated away from the participant were recognized more rapidly the closer they appeared in their canonical orientation, not only to the participant, but also to this other individual, showing that another's visual perspective drives mental rotation and item recognition in a similar way as one's own visual perspective. The effects were large and replicated in the three independent studies. They were observed even when the other person was completely passive, enhanced when the participant was explicitly instructed to take the other person's perspective, but reduced when the persons in the scenes were replaced with objects. The content of another's perspective is therefore spontaneously derived, takes a quasi-perceptual form, and can stand in for one's own sensory input during perceptual decision making.
科研通智能强力驱动
Strongly Powered by AbleSci AI