Predicting Search Performance in Heterogeneous Visual Search Scenes with Real-World Objects
Previous work in our lab has demonstrated that efficient visual search with a fixed target has a reaction time by set size function that is best characterized by logarithmic curves. Further, the steepness of these logarithmic curves is determined by the similarity between target and distractor items...
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2017-03-01
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doaj-99095200c7734e8c9e177a7e626f8baa2020-11-24T23:49:35ZengUniversity of California PressCollabra: Psychology2474-73942017-03-013110.1525/collabra.5341Predicting Search Performance in Heterogeneous Visual Search Scenes with Real-World ObjectsZhiyuan Wang0Simona Buetti1Alejandro Lleras2University of Illinois at Urbana-Champaign, Champaign, IllinoisUniversity of Illinois at Urbana-Champaign, Champaign, IllinoisUniversity of Illinois at Urbana-Champaign, Champaign, IllinoisPrevious work in our lab has demonstrated that efficient visual search with a fixed target has a reaction time by set size function that is best characterized by logarithmic curves. Further, the steepness of these logarithmic curves is determined by the similarity between target and distractor items (Buetti et al., 2016). A theoretical account of these findings was proposed, namely that a parallel, unlimited capacity, exhaustive processing architecture is underlying such data. Here, we conducted two experiments to expand these findings to a set of real-world stimuli, in both homogeneous and heterogeneous search displays. We used computational simulations of this architecture to identify a way to predict RT performance in heterogeneous search using parameters estimated from homogeneous search data. Further, by examining the systematic deviation from our predictions in the observed data, we found evidence that early visual processing for individual items is not independent. Instead, items in homogeneous displays seemed to facilitate each other’s processing by a multiplicative factor. These results challenge previous accounts of heterogeneity effects in visual search, and demonstrate the explanatory and predictive power of an approach that combines computational simulations and behavioral data to better understand performance in visual search.https://www.collabra.org/articles/53visual searchheterogeneityparallel processingcomputational modelingvisual attentionreal-world objects |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhiyuan Wang Simona Buetti Alejandro Lleras |
spellingShingle |
Zhiyuan Wang Simona Buetti Alejandro Lleras Predicting Search Performance in Heterogeneous Visual Search Scenes with Real-World Objects Collabra: Psychology visual search heterogeneity parallel processing computational modeling visual attention real-world objects |
author_facet |
Zhiyuan Wang Simona Buetti Alejandro Lleras |
author_sort |
Zhiyuan Wang |
title |
Predicting Search Performance in Heterogeneous Visual Search Scenes with Real-World Objects |
title_short |
Predicting Search Performance in Heterogeneous Visual Search Scenes with Real-World Objects |
title_full |
Predicting Search Performance in Heterogeneous Visual Search Scenes with Real-World Objects |
title_fullStr |
Predicting Search Performance in Heterogeneous Visual Search Scenes with Real-World Objects |
title_full_unstemmed |
Predicting Search Performance in Heterogeneous Visual Search Scenes with Real-World Objects |
title_sort |
predicting search performance in heterogeneous visual search scenes with real-world objects |
publisher |
University of California Press |
series |
Collabra: Psychology |
issn |
2474-7394 |
publishDate |
2017-03-01 |
description |
Previous work in our lab has demonstrated that efficient visual search with a fixed target has a reaction time by set size function that is best characterized by logarithmic curves. Further, the steepness of these logarithmic curves is determined by the similarity between target and distractor items (Buetti et al., 2016). A theoretical account of these findings was proposed, namely that a parallel, unlimited capacity, exhaustive processing architecture is underlying such data. Here, we conducted two experiments to expand these findings to a set of real-world stimuli, in both homogeneous and heterogeneous search displays. We used computational simulations of this architecture to identify a way to predict RT performance in heterogeneous search using parameters estimated from homogeneous search data. Further, by examining the systematic deviation from our predictions in the observed data, we found evidence that early visual processing for individual items is not independent. Instead, items in homogeneous displays seemed to facilitate each other’s processing by a multiplicative factor. These results challenge previous accounts of heterogeneity effects in visual search, and demonstrate the explanatory and predictive power of an approach that combines computational simulations and behavioral data to better understand performance in visual search. |
topic |
visual search heterogeneity parallel processing computational modeling visual attention real-world objects |
url |
https://www.collabra.org/articles/53 |
work_keys_str_mv |
AT zhiyuanwang predictingsearchperformanceinheterogeneousvisualsearchsceneswithrealworldobjects AT simonabuetti predictingsearchperformanceinheterogeneousvisualsearchsceneswithrealworldobjects AT alejandrolleras predictingsearchperformanceinheterogeneousvisualsearchsceneswithrealworldobjects |
_version_ |
1725481713507762176 |