Dataset Information

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-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 [Formula: see text] 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.

SUBMITTER: Chen Y

PROVIDER: S-EPMC8062491 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Chen Yupei Y Yang Zhibo Z Ahn Seoyoung S Samaras Dimitris D Hoai Minh M Zelinsky Gregory G

Scientific reports 20210422 1

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, yiel ...[more]

PMID: 33888734

Similar Datasets

Project description:IntroductionHarmful behavior such as smoking may reflect a disturbance in the balance of goal-directed and habitual control. Animal models suggest that habitual control develops after prolonged substance use. In this study, we investigated whether smokers (N = 49) differ from controls (N = 46) in the regulation of goal-directed and habitual behavior. It was also investigated whether individual differences in nicotine dependence levels were associated with habitual responding.MethodsWe used two different multistage instrumental learning tasks that consist of an instrumental learning phase, subsequent outcome devaluation, and a testing phase to measure the balance between goal-directed and habitual responding. The testing phases of these tasks occurred after either appetitive versus avoidance instrumental learning. The appetitive versus aversive instrumental learning stages in the two different tasks modeled positive versus negative reinforcement, respectively.ResultsSmokers and nonsmoking controls did not differ on habitual versus goal-directed control in either task. Individual differences in nicotine dependence within the group of smokers, however, were positively associated with habitual responding after appetitive instrumental learning. This effect seems to be due to impaired stimulus-outcome learning, thereby hampering goal-directed task performance and tipping the balance to habitual responding.ConclusionsThe current finding highlights the importance of individual differences within smokers. For future research, neuroimaging studies are suggested to further unravel the nature of the imbalance between goal-directed versus habitual control in severely dependent smokers by directly measuring activity in the corresponding brain systems.ImplicationsGoal-directed versus habitual behavior in substance use and addiction is highly debated. This study investigated goal-directed versus habitual control in smokers. The findings suggest that smokers do not differ from controls in goal-directed versus habitual control. Individual differences in nicotine dependence within smokers, however, were positively associated with habitual responding after appetitive instrumental learning. This effect seems to be due to impaired stimulus-outcome learning, thereby hampering goal-directed task performance and tipping the balance to habitual responding. These findings add to the ongoing debate on habitual versus goal-directed control in addiction and emphasize the importance of individual differences within smokers.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets