A method for detection of inattentional feature blindness

Abstract

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.

Introduction to the special issue on ensemble perception

“…Several examine the role of saliency (Iakovlev & Utochkin, 2020), participant expertise (Sun & Gauthier, 2020) and practice-induced learning (Cha et al, 2020;Hochstein & Pavlovskaya, 2020) on ensemble perception. Another paper proposes a method for distinguishing noise from effects of inattention (Raidvee et al, 2021) when considering the efficiency of ensemble integration (Solomon, 2010;Solomon et al, 2011;Solomon et al, 2016). Additional papers explore ensemble representations within and outside the focus of attention (Chen, Zhuang, et al, 2020), during competing tasks like multiple object tracking (Alzahabi & Cain, 2021), and even with active ignoring (Rafiei et al, 2020).…”

A population response model of ensemble perception.

“…For example, the arithmetic mean is defined as the sum of all sampled measurements divided by their number. However, it is not likely that the visual system does exactly that computation in the averaging task given that the judgments of sum of visible features are noisier than those of the average (Lee et al, 2016; Raidvee et al, 2021). The second example of a possible misconception concerns the idea of effective set size, a way to quantitatively describe the minimum amount of information an otherwise ideal observer should get from a display to reach the same level of efficiency as the real observer.…”

A Population Response Model of Ensemble Perception

“…For example, the arithmetic mean is defined as the sum of all sampled measurements divided by their number. However, it is not likely that the visual system does exactly that computation given that the judgments of sum of visible features are noisier than those of the average (Lee et al, 2016; Raidvee et al, 2021). Moreover, these models do not specify the underlying neural mechanism of arithmetic mean and variance computation.…”