ArticleNeurons, behavior, data analysis, and theory2021
Predicting Goal-directed Attention Control Using Inverse-Reinforcement Learning.
Article in Neurons, behavior, data analysis, and theory, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A Resource-Rational Account of Human Eye Movements During Immersive Visual Search.Open mind : discoveries in cognitive science · 2026Article
- Look Hear: Gaze Prediction for Speech-directed Human Attention.Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision · 2025Article
- Unifying Top-down and Bottom-up Scanpath Prediction Using Transformers.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2024Article
- Searching for meaning: Local scene semantics guide attention during natural visual search in scenes.Quarterly journal of experimental psychology (2006) · 2023Article
- Weighting the factors affecting attention guidance during free viewing and visual search: The unexpected role of object recognition uncertainty.Journal of vision · 2022Article
- Meaning and expected surfaces combine to guide attention during visual search in scenes.Journal of vision · 2021Article
- Domain Adaptation for Imitation Learning Using Generative Adversarial Network.Sensors (Basel, Switzerland) · 2021Article
- Attention in Psychology, Neuroscience, and Machine Learning.Frontiers in computational neuroscience · 2020Review
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Authors and funding
8 authors.
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Abstract
Understanding how goals control behavior is a question ripe for interrogation by new methods from machine learning. These methods require large and labeled datasets to train models. To annotate a large-scale image dataset with observed search fixations, we collected 16,184 fixations from people searching for either microwaves or clocks in a dataset of 4,366 images (MS-COCO). We then used this behaviorally-annotated dataset and the machine learning method of inverse-reinforcement learning (IRL) to learn target-specific reward functions and policies for these two target goals. Finally, we used these learned policies to predict the fixations of 60 new behavioral searchers (clock = 30, microwave = 30) in a disjoint test dataset of kitchen scenes depicting both a microwave and a clock (thus controlling for differences in low-level image contrast). We found that the IRL model predicted behavioral search efficiency and fixation-density maps using multiple metrics. Moreover, reward maps from the IRL model revealed target-specific patterns that suggest, not just attention guidance by target features, but also guidance by scene context (e.g., fixations along walls in the search of clocks). Using machine learning and the psychologically meaningful principle of reward, it is possible to learn the visual features used in goal-directed attention control.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.