Challenges to pooling models of crowding: Implications for visual mechanisms

Rosenholtz, R., Yu, D., Keshvari, S.


Abstract

A set of phenomena known as crowding reveal peripheral vision's vulnerability in the face of clutter. Crowding is important both because of its ubiquity, making it relevant for many real-world tasks and stimuli, and because of the window it provides onto mechanisms of visual processing. Here we focus on models of the underlying mechanisms. This review centers on a popular class of models known as pooling models, as well as the phenomenology that appears to challenge a pooling account. Using a candidate high-dimensional pooling model, we gain intuitions about whether a pooling model suffices and reexamine the logic behind the pooling challenges. We show that pooling mechanisms can yield substitution phenomena and therefore predict better performance judging the properties of a set versus a particular item. Pooling models can also exhibit some similarity effects without requiring mechanisms that pool at multiple levels of processing, and without constraining pooling to a particular perceptual group. Moreover, we argue that other similarity effects may in part be due to noncrowding influences like cuing. Unlike low-dimensional straw-man pooling models, high-dimensional pooling preserves rich information about the stimulus, which may be sufficient to support high-level processing. To gain insights into the implications for pooling mechanisms, one needs a candidate high-dimensional pooling model and cannot rely on intuitions from low-dimensional models. Furthermore, to uncover the mechanisms of crowding, experiments need to separate encoding from decision effects. While future work must quantitatively examine all of the challenges to a high-dimensional pooling account, insights from a candidate model allow us to conclude that a high-dimensional pooling mechanism remains viable as a model of the loss of information leading to crowding.

Information

title:
Challenges to pooling models of crowding: Implications for visual mechanisms
author:
Rosenholtz,
R.,
Yu,
D.,
Keshvari,
S.
citation:
Journal of Vision, 19(7):15
shortcite:
Journal of Vision
year:
2019
created:
2019-08-06
summary:
pooling_models_challenges_jov_2019
keyword:
rosenholtz,
crowding,
pooling,
peripheral_vision
pdf:
https://jov.arvojournals.org/article.aspx?articleid=2740058
type:
publication
 
publications/pooling_models_challenges_jov_2019.txt · Last modified: 2019/08/06 17:19 by shaiyan