Search performance is better predicted by tileability than by the presence of a unique basic feature

Chang, H., Rosenholtz, R.


Abstract

Traditional models of visual search such as feature integration theory (FIT; Treisman & Gelade, 1980), have suggested that a key factor determining task difficulty consists of whether or not the search target contains a “basic feature” not found in the other display items (distractors). Here we discriminate between such traditional models and our recent texture tiling model (TTM) of search (Rosenholtz, Huang, Raj, Balas, & Ilie, 2012b), by designing new experiments that directly pit these models against each other. Doing so is nontrivial, for two reasons. First, the visual representation in TTM is fully specified, and makes clear testable predictions, but its complexity makes getting intuitions difficult. Here we elucidate a rule of thumb for TTM, which enables us to easily design new and interesting search experiments. FIT, on the other hand, is somewhat ill-defined and hard to pin down. To get around this, rather than designing totally new search experiments, we start with five classic experiments that FIT already claims to explain: T among Ls, 2 among 5s, Q among Os, O among Qs, and an orientation/luminance-contrast conjunction search. We find that fairly subtle changes in these search tasks lead to significant changes in performance, in a direction predicted by TTM, providing definitive evidence in favor of the texture tiling model as opposed to traditional views of search.

Information

title:
Search performance is better predicted by tileability than by the presence of a unique basic feature
author:
Chang,
H.,
Rosenholtz,
R.
citation:
Journal of Vision, 16(10), 13
shortcite:
Journal of Vision
year:
2016
created:
2016-12-29
summary:
tileability16
keyword:
rosenholtz,
visstat,
crowding,
search
pdf:
http://tvst.arvojournals.org/article.aspx?articleid=2546342
type:
publication