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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| Format: | Article |
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Association for Research in Vision and Ophthalmology (ARVO)
2018
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| Online Access: | http://hdl.handle.net/1721.1/113614 |
| _version_ | 1811079189902655488 |
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| author | Chang, Honghua Rosenholtz, Ruth Ellen |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Chang, Honghua Rosenholtz, Ruth Ellen |
| author_sort | Chang, Honghua |
| collection | MIT |
| 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. |
| first_indexed | 2024-09-23T11:11:17Z |
| format | Article |
| id | mit-1721.1/113614 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T11:11:17Z |
| publishDate | 2018 |
| publisher | Association for Research in Vision and Ophthalmology (ARVO) |
| record_format | dspace |
| spelling | mit-1721.1/1136142022-10-01T01:56:14Z Search performance is better predicted by tileability than presence of a unique basic feature Chang, Honghua Rosenholtz, Ruth Ellen Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Chang, Honghua Rosenholtz, Ruth Ellen 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. National Eye Institute (R01-EY021473) 2018-02-12T22:52:41Z 2018-02-12T22:52:41Z 2016-08 2016-04 2018-02-09T14:03:04Z Article http://purl.org/eprint/type/JournalArticle 1534-7362 http://hdl.handle.net/1721.1/113614 Chang, Honghua, and Ruth Rosenholtz. “Search Performance Is Better Predicted by Tileability Than Presence of a Unique Basic Feature.” Journal of Vision 16, no. 10 (August 22, 2016): 13. © 2016 Association for Research in Vision and Ophthalmology. http://dx.doi.org/10.1167/16.10.13 Journal of Vision Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) https://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Association for Research in Vision and Ophthalmology (ARVO) |
| spellingShingle | Chang, Honghua Rosenholtz, Ruth Ellen 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 |
| url | http://hdl.handle.net/1721.1/113614 |
| work_keys_str_mv | AT changhonghua searchperformanceisbetterpredictedbytileabilitythanpresenceofauniquebasicfeature AT rosenholtzruthellen searchperformanceisbetterpredictedbytileabilitythanpresenceofauniquebasicfeature |