Rethinking the Role of Top-Down Attention in Vision: Effects Attributable to a Lossy Representation in Peripheral Vision

Abstract

According to common wisdom in the field of visual perception, top-down selective attention is required in order to bind features into objects. In this view, even simple tasks, such as distinguishing a rotated T from a rotated L, require selective attention since they require feature binding. Selective attention, in turn, is commonly conceived as involving volition, intention, and at least implicitly, awareness. There is something non-intuitive about the notion that we might need so expensive (and possibly human) a resource as conscious awareness in order to perform so basic a function as perception. In fact, we can carry out complex sensorimotor tasks, seemingly in the near absence of awareness or volitional shifts of attention (“zombie behaviors”). More generally, the tight association between attention and awareness, and the presumed role of attention on perception, is problematic. We propose that under normal viewing conditions, the main processes of feature binding and perception proceed largely independently of top-down selective attention. Recent work suggests that there is a significant loss of information in early stages of visual processing, especially in the periphery. In particular, our texture tiling model (TTM) represents images in terms of a fixed set of “texture” statistics computed over local pooling regions that tile the visual input. We argue that this lossy representation produces the perceptual ambiguities that have previously been as ascribed to a lack of feature binding in the absence of selective attention. At the same time, the TTM representation is sufficiently rich to explain performance in such complex tasks as scene gist recognition, pop-out target search, and navigation. A number of phenomena that have previously been explained in terms of voluntary attention can be explained more parsimoniously with the TTM. In this model, peripheral vision introduces a specific kind of information loss, and the information available to an observer varies greatly depending upon shifts of the point of gaze (which usually occur without awareness). The available information, in turn, provides a key determinant of the visual system’s capabilities and deficiencies. This scheme dissociates basic perceptual operations, such as feature binding, from both top-down attention and conscious awareness.

Perceiving stimulus displacements across saccades

“…Detection is more likely when the eyes land near an object because objects farther from the fovea are seen with lower spatial resolution (e.g., Loschky et al, 2005;Strasburger et al, 2011). Thus, increased distance between the probe and postsaccadic eye position would yield more imprecision in the participants' estimates of the probe's location (Rosenholtz et al, 2012). There is an advantage for detecting displacements of the saccade target, most likely because of visual acuity or attentional focus being better near the fovea, but it is not the saccade target alone that determines the perception of stability across saccades.…”

“…In sum, these analyses of the effects of landing error distance on performance contradict the prediction of the saccade target object theory by showing that detection was better for objects near the saccade landing point. This is likely due to the fact that objects farther from the fovea are seen with lower spatial resolution (e.g., Loschky, McConkie, Yang, & Miller, 2005;Strasburger, Rentschler, & Juttner, 2011), so that there would be more imprecision in the participants' estimates of their locations (Rosenholtz, Huang, & Ehinger, 2012).…”

Looking back at the stare-in-the-crowd effect: Staring eyes do not capture attention in visual search

“…Rosenholtz and colleagues (e.g., Rosenholtz, Huang, & Ehinger, 2012) see visual search phenomena as arising from an output of early vision, which, they argue, contains significant information loss. So, rather than postulating a selective mechanism governing access to a limited-capacity channel, their solution is to have early vision compress the signal, which is then sent through the limitedcapacity channel.…”

“…This latter set of mechanisms is collectively known as covert visual attention, and it seems to achieve its end in a number of different ways: through enhancing the signal produced by the selected stimulus, restricting processing to selected regions of space, to perceptual groups or objects, or by tuning the visual system to certain attributes possessed by the searched-for object, such as color or orientation (for reviews see Carrasco, 2011;Scholl, 2001). In visual search, the general idea is that visual attention, however it is conceived, must operate in such a way as to result in the detection and identification of the searched-for target (e.g., Treisman & Gelade, 1980;Wolfe, 1994; but see Rosenholtz, Huang, & Ehinger, 2012, for a theory that eliminates the role of covert attention in visual search). In this paper, we are concerned with how attention is deployed in order to achieve this and, in particular, whether or not staring gazes enjoy a privileged status in this regard.…”

You can detect the trees as well as the forest when adding the leaves: Evidence from visual search tasks containing three-level hierarchical stimuli

“…Specifically, intermediate elements activated more regions that contain neurons with middle receptive visual fields compared to local elements (that activated smaller receptive visual field regions) or global elements (that activated larger receptive visual field regions). In visual search tasks, Rosenholtz, Huang, and Ehinger (2012) demonstrated by using a computational perceptive model that peripheral visual perception does not allow perceiving the details of a scene; however, the structural elements could be detected, which is consistent with the idea of a coarse-to-fine eye movement strategy in visual search (Over, Hooge, Vlaskamp, & Erkelens, 2007). They argued that the limitations of peripheral perception could explain the difference for efficient vs. less inefficient search, and they explained an important range of results in the visual search task with this model.…”