COCO-Search18 fixation dataset for predicting goal-directed attention control

Abstract Attention control is a basic behavioral process that has been studied for decades. The currently best models of attention control are deep networks trained on free-viewing behavior to predict bottom-up attention control – saliency. We introduce COCO-Search18, the first dataset of laboratory...

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Autores principales:	Yupei Chen, Zhibo Yang, Seoyoung Ahn, Dimitris Samaras, Minh Hoai, Gregory Zelinsky
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2a4346091b884f6db7395c2b18c34b46
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spelling	oai:doaj.org-article:2a4346091b884f6db7395c2b18c34b462021-12-02T16:45:06ZCOCO-Search18 fixation dataset for predicting goal-directed attention control10.1038/s41598-021-87715-92045-2322https://doaj.org/article/2a4346091b884f6db7395c2b18c34b462021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87715-9https://doaj.org/toc/2045-2322Abstract Attention control is a basic behavioral process that has been studied for decades. The currently best models of attention control are deep networks trained on free-viewing behavior to predict bottom-up attention control – saliency. We introduce COCO-Search18, the first dataset of laboratory-quality goal-directed behavior large enough to train deep-network models. We collected eye-movement behavior from 10 people searching for each of 18 target-object categories in 6202 natural-scene images, yielding $$\sim$$ ∼ 300,000 search fixations. We thoroughly characterize COCO-Search18, and benchmark it using three machine-learning methods: a ResNet50 object detector, a ResNet50 trained on fixation-density maps, and an inverse-reinforcement-learning model trained on behavioral search scanpaths. Models were also trained/tested on images transformed to approximate a foveated retina, a fundamental biological constraint. These models, each having a different reliance on behavioral training, collectively comprise the new state-of-the-art in predicting goal-directed search fixations. Our expectation is that future work using COCO-Search18 will far surpass these initial efforts, finding applications in domains ranging from human-computer interactive systems that can anticipate a person’s intent and render assistance to the potentially early identification of attention-related clinical disorders (ADHD, PTSD, phobia) based on deviation from neurotypical fixation behavior.Yupei ChenZhibo YangSeoyoung AhnDimitris SamarasMinh HoaiGregory ZelinskyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yupei Chen Zhibo Yang Seoyoung Ahn Dimitris Samaras Minh Hoai Gregory Zelinsky COCO-Search18 fixation dataset for predicting goal-directed attention control
description	Abstract Attention control is a basic behavioral process that has been studied for decades. The currently best models of attention control are deep networks trained on free-viewing behavior to predict bottom-up attention control – saliency. We introduce COCO-Search18, the first dataset of laboratory-quality goal-directed behavior large enough to train deep-network models. We collected eye-movement behavior from 10 people searching for each of 18 target-object categories in 6202 natural-scene images, yielding $$\sim$$ ∼ 300,000 search fixations. We thoroughly characterize COCO-Search18, and benchmark it using three machine-learning methods: a ResNet50 object detector, a ResNet50 trained on fixation-density maps, and an inverse-reinforcement-learning model trained on behavioral search scanpaths. Models were also trained/tested on images transformed to approximate a foveated retina, a fundamental biological constraint. These models, each having a different reliance on behavioral training, collectively comprise the new state-of-the-art in predicting goal-directed search fixations. Our expectation is that future work using COCO-Search18 will far surpass these initial efforts, finding applications in domains ranging from human-computer interactive systems that can anticipate a person’s intent and render assistance to the potentially early identification of attention-related clinical disorders (ADHD, PTSD, phobia) based on deviation from neurotypical fixation behavior.
format	article
author	Yupei Chen Zhibo Yang Seoyoung Ahn Dimitris Samaras Minh Hoai Gregory Zelinsky
author_facet	Yupei Chen Zhibo Yang Seoyoung Ahn Dimitris Samaras Minh Hoai Gregory Zelinsky
author_sort	Yupei Chen
title	COCO-Search18 fixation dataset for predicting goal-directed attention control
title_short	COCO-Search18 fixation dataset for predicting goal-directed attention control
title_full	COCO-Search18 fixation dataset for predicting goal-directed attention control
title_fullStr	COCO-Search18 fixation dataset for predicting goal-directed attention control
title_full_unstemmed	COCO-Search18 fixation dataset for predicting goal-directed attention control
title_sort	coco-search18 fixation dataset for predicting goal-directed attention control
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2a4346091b884f6db7395c2b18c34b46
work_keys_str_mv	AT yupeichen cocosearch18fixationdatasetforpredictinggoaldirectedattentioncontrol AT zhiboyang cocosearch18fixationdatasetforpredictinggoaldirectedattentioncontrol AT seoyoungahn cocosearch18fixationdatasetforpredictinggoaldirectedattentioncontrol AT dimitrissamaras cocosearch18fixationdatasetforpredictinggoaldirectedattentioncontrol AT minhhoai cocosearch18fixationdatasetforpredictinggoaldirectedattentioncontrol AT gregoryzelinsky cocosearch18fixationdatasetforpredictinggoaldirectedattentioncontrol
_version_	1718383503658188800

COCO-Search18 fixation dataset for predicting goal-directed attention control

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