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

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...

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Autores principales: Chang, Honghua, Rosenholtz, Ruth
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Association for Research in Vision and Ophthalmology 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4995045/
https://www.ncbi.nlm.nih.gov/pubmed/27548090
http://dx.doi.org/10.1167/16.10.13
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author Chang, Honghua
Rosenholtz, Ruth
author_facet Chang, Honghua
Rosenholtz, Ruth
author_sort Chang, Honghua
collection PubMed
description 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.
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spelling pubmed-49950452016-08-24 Search performance is better predicted by tileability than presence of a unique basic feature Chang, Honghua Rosenholtz, Ruth J Vis Article 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. The Association for Research in Vision and Ophthalmology 2016-08-22 /pmc/articles/PMC4995045/ /pubmed/27548090 http://dx.doi.org/10.1167/16.10.13 Text en http://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
spellingShingle Article
Chang, Honghua
Rosenholtz, Ruth
Search performance is better predicted by tileability than presence of a unique basic feature
title Search performance is better predicted by tileability than presence of a unique basic feature
title_full Search performance is better predicted by tileability than presence of a unique basic feature
title_fullStr Search performance is better predicted by tileability than presence of a unique basic feature
title_full_unstemmed Search performance is better predicted by tileability than presence of a unique basic feature
title_short Search performance is better predicted by tileability than presence of a unique basic feature
title_sort search performance is better predicted by tileability than presence of a unique basic feature
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4995045/
https://www.ncbi.nlm.nih.gov/pubmed/27548090
http://dx.doi.org/10.1167/16.10.13
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