Crowding changes appearance systematically in peripheral, amblyopic, and developing vision

Abstract

Visual crowding is the disruptive effect of clutter on object recognition. Although most prominent in adult peripheral vision, crowding also disrupts foveal vision in typically developing children and those with strabismic amblyopia. Do these crowding effects share the same mechanism? Here we exploit observations that crowded errors in peripheral vision are not random: Target objects appear either averaged with the flankers (assimilation) or replaced by them (substitution). If amblyopic and developmental crowding share the same mechanism, then their errors should be similarly systematic. We tested foveal vision in children aged 3 to 8 years with typical vision or strabismic amblyopia and peripheral vision in typical adults. The perceptual effects of crowding were measured by requiring observers to adjust a reference stimulus to match the perceived orientation of a target “Vac-Man” element. When the target was surrounded by flankers that differed by ± 30°, all three groups (adults and children with typical or amblyopic vision) reported orientations between the target and flankers (assimilation). Errors were reduced with ± 90° differences but primarily matched the flanker orientation (substitution) when they did occur. A population pooling model of crowding successfully simulated this pattern of errors in all three groups. We conclude that the perceptual effects of amblyopic and developing crowding are systematic and resemble the near periphery in adults, suggesting a common underlying mechanism.

The Bouma law accounts for crowding in fifty observers

“…Large individual differences enhance crowding’s potential as a biomarker for studying cortical health and development. Specifically, crowding varies across children too (Kalpadakis-Smith et al, 2022) and predicts RSVP reading speed (Pelli et al, 2007). If crowding correlates with reading speed of beginning readers, then preliterate measures of crowding might help identify the children who need extra help before they learn to read.…”

A common cortical basis for variations in visual crowding

“…As outlined in the introduction, the systematicity of crowded errors is well explained by ‘pooling’ models of crowding (Parkes et al, 2001; Greenwood, Bex, & Dakin, 2009; Harrison & Bex, 2015; Rosenholtz, Yu, & Keshvari, 2019), which model these effects as a combination of the target and flanker elements. Population pooling models are particularly well equipped to account for the diverse effects of crowding on appearance, given their demonstrated simulation of both assimilation and repulsion errors (van den Berg, Roerdink, & Cornelissen, 2010; Harrison & Bex, 2015; Greenwood & Parsons, 2020), as well as crowding effects in typical vision and amblyopia (Kalpadakis-Smith et al, 2022). Here we examine whether population pooling can account for the variations in crowding found for both threshold elevation and assimilation in the current study.…”

“…In brief, the model simulates the response of a population of detectors, each with a Gaussian sensitivity profile for orientation and an inhibitory surround. Population responses were determined for target and flanker orientations separately and combined via weights, as in recent models (Greenwood & Parsons, 2020; Kalpadakis-Smith et al, 2022). Variations in the effect of crowding were produced by varying these weights from 0-1.…”

“…This coupling is also a central aspect of “pooling models” that depict crowding as the unwanted combination of target and flanker elements (Parkes et al, 2001; Greenwood, Bex, & Dakin, 2009; Harrison & Bex, 2015; Rosenholtz, Yu, & Keshvari, 2019; Greenwood & Parsons, 2020). The generality of this approach is evident in the application of these models in a variety of contexts – simulating the effect of crowding on motion and colour (Greenwood & Parsons, 2020), higher-level elements such as faces (Kalpadakis-Smith, Goffaux, & Greenwood, 2018), and associated elevations in clinical cases like amblyopia (Kalpadakis-Smith et al, 2022). A similar coupling between performance and appearance could in theory also arise in the broader class of pooling-based ‘texturisation’ models that depict crowding as the extraction of summary statistics across wide regions of the visual field (Balas, Nakano, & Rosenholtz, 2009; Freeman & Simoncelli, 2011; Rosenholtz, Yu, & Keshvari, 2019).…”

“…Errors follow the average of the target-flanker features when elements are similar to one another, while larger target-flanker differences give errors that resemble a substitution of the flanker identities (Greenwood, Bex, & Dakin, 2009; Ester, Zilber, & Serences, 2015; Harrison & Bex, 2015). Both of these error types are well described by population-coding models where crowding occurs due to the unwanted combination of the population responses to each element (van den Berg, Roerdink, & Cornelissen, 2010; Harrison & Bex, 2015; Greenwood & Parsons, 2020; Kalpadakis-Smith, Tailor, Dahlmann-Noor, & Greenwood, 2022). The implication of these systematic errors is that participants make errors in clutter not because they see nothing, but because the target appearance is changed.…”