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当相信即看见:预测在塑造视觉感知中的作用。

When believing is seeing: the role of predictions in shaping visual perception.

作者信息

Król Magdalena Ewa, El-Deredy Wael

机构信息

School of Psychological Sciences, University of Manchester, Manchester, UK.

出版信息

Q J Exp Psychol (Hove). 2011 Sep;64(9):1743-71. doi: 10.1080/17470218.2011.559587. Epub 2011 Aug 9.

DOI:10.1080/17470218.2011.559587
PMID:21824013
Abstract

The study investigated the influence of predictions on perception-in particular, how strong but erroneous prediction coupled with poor sensory data can trigger misperceptions. Using signal detection, we tested whether predictions induced by a semantic cue change the recognition threshold (criterion) or the subjectively perceived differences between stimuli (sensitivity). In a series of 3 experiments, participants observed pictures of (a) real objects distorted to various extents (targets), (b) noise with elements corresponding to meaningful objects (foils), or (c) nonsense images (noise). Stimuli were preceded either by a semantic cue or by a blank screen with equal probability. Presence of the cue resulted in a more liberal criterion, but only for targets and foils. The cue decreased sensitivity between the distorted targets, but increased sensitivity between the targets and noise-that is, the cue increased between-class differences, but decreased within-class differences. When there was no correspondence between prediction and the sensory input, prediction actually increased the chances of correctly rejecting noise. The influence of the cue was strongest for the moderately distorted targets and foils-when uncertainty related to the bottom-up input was the highest.

摘要

该研究调查了预测对感知的影响——特别是,强烈但错误的预测与糟糕的感官数据相结合如何引发错误感知。我们使用信号检测来测试由语义线索引发的预测是否会改变识别阈值(标准)或刺激之间的主观感知差异(敏感性)。在一系列3个实验中,参与者观察了以下图片:(a) 扭曲程度各异的真实物体(目标),(b) 带有与有意义物体相对应元素的噪声(陪衬物),或 (c) 无意义图像(噪声)。刺激出现之前,要么是语义线索,要么是空白屏幕,出现概率相等。线索的存在导致了更宽松的标准,但仅适用于目标和陪衬物。线索降低了扭曲目标之间的敏感性,但增加了目标与噪声之间的敏感性——也就是说,线索增加了类别间差异,但减少了类别内差异。当预测与感官输入之间不存在对应关系时,预测实际上增加了正确排除噪声的机会。对于适度扭曲的目标和陪衬物,线索的影响最为强烈——此时与自下而上输入相关的不确定性最高。

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