NORMAL BLINDNESS: LOOKING BUT FAILING TO SEE

Looked But Failed to See (LBFTS) errors occur when observers fail to notice a clearly visible item. They can happen across a wide range of tasks and settings, from driving and medical image perception to laboratory visual search tasks, overlooking typos in a paper, or failing to see a cyclist in an intersection. LBFTS can be thought of as a form of “normal blindness.” Although obviously far less severe than clinical blindness, it is so universal that its costs are substantial at a societal level. An article published in the September 2022 issue of the journal Trends in Cognitive Sciences outlines a new, unified account of such errors, arguing that processes that function well in most situations are guaranteed to produce a steady stream of LBFTS errors under some circumstances. The authors advance the proposition that normal blindness is the by-product of the limited-capacity prediction engine that is the human visual system. Processes that evolved to allow individuals to move through the world with ease are virtually guaranteed to cause missing certain significant stimuli, especially in important tasks like driving and medical image perception.

Although various LBFTS situations may seem like distinct phenomena, it can be argued that based on recent work, they all can be seen as products of the same normal mechanisms of attention and object recognition. Specifically: (i) observers only select a subset of what they could process on each fixation (although they are not blind to the rest of the visual input); (ii) even the items that are selected by attention will be missed if too little time is given to their processing; (iii) the processes that give rise to routine visual awareness persuade us that we have seen more than we have actually seen; and (iv) attentional guidance (attentional set) can guide observers away from targets as well as toward them. Taken together, these factors produce a state of ‘normal blindness’ that has significant implications. A framework is shown in which multiple types of LBFTS errors arise from the same underlying processes. A relatively complex task is used in the form of a cartoon as an example.