Probabilistic, positional averaging predicts object-level crowding effects with letter-like stimuli

Abstract

We investigated how crowding-a breakdown in object recognition that occurs in the presence of nearby distracting clutter-works for complex letter-like stimuli. Subjects reported the orientation (up/down/left/right) of a T target, abutted by a single flanker composed of randomly positioned horizontal and vertical bars. In addition to familiar retinotopic anisotropies (e.g., more crowding from more eccentric flankers), we report three object-centered anisotropies. First, inversions of the target element were rare: errors included twice as many ±90° as 180° target rotations. Second, flankers were twice as intrusive when they lay above or below (end-flanking) compared to left or right (side-flanking) of an upright T target (an effect that holds under global rotation of the target-flanker pair). Third, end flankers induce subjects to make erroneous reports that resemble the flanker (producing a structured pattern of errors), but errors induced by side flankers do not (instead producing random errors). A model based on probabilistic weighted averaging of the feature positions within contours can account for these effects. Thus, we demonstrate a set of seemingly "high-level" object-centered crowding effects that can arise from "low-level" interactions between the features of letter-like elements.

A Unifying Model of Orientation Crowding in Peripheral Vision

“…Therefore, to estimate the population response to a flanker, we simply down-weight the response to an unflanked target by the flanker’s distance from the target center (equation 6). Thus, whereas other models propose the flanker probabilistically either causes crowding or doesn’t [9, 30], here the flanker influences the population response with a strength that diminishes with distance from the target, akin to the properties of neural response fields [31]. …”

Eyes on crowding: Crowding is preserved when responding by eye and similarly affects identity and position accuracy

“…The current main theories explain crowding either in terms of excessive feature pooling (e.g., Pelli & Tillman, 2008;van den Berg, Roerdink, & Cornelissen, 2010) or as due to a loss of positional information (source confusion) resulting in reporting a flanking object as the target (e.g., Dakin, Cass, Greenwood, & Bex, 2010;Greenwood, Bex, & Dakin, 2009;Strasburger, Harvey, & Rentschler, 1991;Strasburger & Malania, 2013). A recent model integrates both of these accounts by assuming that uncertainty (i.e., the width of the internal noise distribution) about both stimulus positions and identities depends on flanker proximity (van den Berg, Johnson, Martinez Anton, Schepers, & Cornelissen, 2012).…”

Visual crowding is a combination of an increase of positional uncertainty, source confusion, and featural averaging

“…Changes in positional uncertainty and feature averaging are found in experiments in which an observer is required to make a spatial judgment about a continuous property of the target, such as its orientation or relative position, for examples, see refs 40 and 42. Feature substitutions – mistaking a distractor element for a target – are mostly found in paradigms in which the observer is required to report the categorical identity of target such as a letter; trials in which the observer reports a distractor identity instead of the target reveal source confusions373843, which may be independent of an increase in positional uncertainty444546.…”

“…Indeed, even the term crowding refers to the result of some visual process and not a mechanism. The underlying cause of crowding has previously been explained by various computational models1123404142474849 and higher-level mechanistic hypotheses2450. Population code models, in which all visual features probabilistically contribute to perceptual reports, can produce a wide variety of data51, including so-called averaging and substitution errors41.…”