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A method for detection of inattentional feature blindness
Attention, Perception, & Psychophysics ( IF 1.7 ) Pub Date : 2021-03-02 , DOI: 10.3758/s13414-020-02234-5
Aire Raidvee , Mai Toom , Jüri Allik

In ensemble displays, two principal factors determine the precision with which the mean value of some perceptual attribute, such as size and orientation, can be discriminated: inefficiency and representational noise of each element. Inefficiency is mainly caused by biased inference, or by inattentional (feature) blindness (i.e., some elements or their features are not processed). Here, we define inattentional feature blindness as an inability to perceive the value(s) of certain feature(s) of an object while the presence of the object itself may be registered. Separation of the effects of inattentional (feature) blindness and perceptual noise has escaped traditional analytic methods because of their trade-off effects on the slope of the psychometric discrimination function. Here, we propose a method that can separate the effects of inattentional feature blindness from that of the representational noise. The basic idea is to display a set of elements from which only one contains information relevant for solving the task, while all other elements are “dummies” carrying no useful information because they do not differ from the reference. If the single informative element goes unprocessed, the correct answer can only be given by a random guess. The guess rate can be modeled similarly to the lapse rate, traditionally represented by λ. As an illustration, we present evidence that the presence versus lack of inattentional feature blindness in orientation pooling depends on the feature types present in the display.



中文翻译:

一种检测疏忽特征盲的方法

在整体显示中,两个主要因素决定了可以区分某些感知属性(例如大小和方向)的平均值的精度:每个元素的无效性和代表性噪声。效率低下主要是由偏见推论引起的,或者是由于疏忽(特征)的盲目性(即某些元素或其特征未得到处理)而引起的。在这里,我们定义了注意力不集中的盲点这是因为当对象本身的存在可能被注册时,无法感知对象的某些特征的值。注意力不集中的(特征)盲和感性噪声的影响的分离已经摆脱了传统的分析方法,因为它们在心理计量学辨别函数的斜率上具有折衷效果。在这里,我们提出了一种方法,可以将注意力不集中的特征失明的影响与代表性噪声的影响分开。基本思想是显示一组元素,其中只有一个元素包含与解决任务相关的信息,而所有其他元素都是“虚拟”,不包含有用的信息,因为它们与参考没有区别。如果单个信息元素未经处理,则只能通过随机猜测给出正确答案。猜测率可以类似于传统上由λ表示的失败率进行建模。作为说明,我们提供了证据,即定向池中是否存在无意识的特征盲与否取决于显示器中存在的特征类型。

更新日期:2021-03-03
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