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ARXIV:2604.04439 · REINFORCEMENT LEARNING ANALYSIS · SUBMITTED 07 APR · 20:13 UTC · FRESHNESS UNKNOWN
ARXIV:2604.04439REINFORCEMENT LEARNING ANALYSISSUBMITTED 07 APR · 20:13 UTCFRESHNESS UNKNOWNHenrik Krauss · Takehisa Yairi · arXiv
This research reverse-engineers human decision-making in Atari games by quantifying the impact of peripheral vision, gaze, and past states on action prediction, offering insights into how agents can better mimic…
Opportunity summary
Pain This research reverse-engineers human decision-making in Atari games by quantifying the impact of peripheral vision, gaze, and past states on action prediction, offering insights into how agents can better mimic human strategies.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
This research reverse-engineers human decision-making in Atari games by quantifying the impact of peripheral vision, gaze, and past states on action prediction, offering insights into how agents can better mimic human strategies. Using Atari-HEAD,…
We study how different visual information sources contribute to human decision making in dynamic visual environments. Using Atari-HEAD, a large-scale Atari gameplay dataset with synchronized eye-tracking, we introduce a controlled ablation framework as a…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Across 20 games, peripheral information shows by far the strongest contribution, with median prediction-accuracy drops in the range of 35.27-43.90% when removed. Code availability…
Reinforcement Learning Analysis moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
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This research reverse-engineers human decision-making in Atari games by quantifying the impact of peripheral vision, gaze, and past states on action prediction, offering insights into how agents can better mimic…
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Paper Pack
10.48550/arXiv.2604.04439This research reverse-engineers human decision-making in Atari games by quantifying the impact of peripheral vision, gaze, and past states on action prediction, offering insights into how agents can better mimic human strategies.
Abstract
We study how different visual information sources contribute to human decision making in dynamic visual environments. Using Atari-HEAD, a large-scale Atari gameplay dataset with synchronized eye-tracking, we introduce a controlled ablation framework as a means to reverse-engineer the contribution of peripheral visual information, explicit gaze information in form of gaze maps, and past-state information from human behavior. We train action-prediction networks under six settings that selectively include or exclude these information sources. Across 20 games, peripheral information shows by far the strongest contribution, with median prediction-accuracy drops in the range of 35.27-43.90% when removed. Gaze information yields smaller drops of 2.11-2.76%, while past-state information shows a broader range of 1.52-15.51%, with the upper end likely more informative due to reduced peripheral-information leakage. To complement aggregate accuracies, we cluster states by true-action probabilities assigned by the different model configurations. This analysis identifies coarse behavioral regimes, including focus-dominated, periphery-dominated, and more contextual decision situations. These results suggest that human decision making in Atari depends strongly on information beyond the current focus of gaze, while the proposed framework provides a way to estimate such information-source contributions from behavior.
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PROBLEM
This research reverse-engineers human decision-making in Atari games by quantifying the impact of peripheral vision, gaze, and past states on action prediction, offering insights into how agents can better mimic human strategies. Using Atari-HEAD, a large-scale Atari gameplay da...
METHOD
We study how different visual information sources contribute to human decision making in dynamic visual environments. Using Atari-HEAD, a large-scale Atari gameplay dataset with synchronized eye-tracking, we introduce a controlled ablation framework as a means to reverse-enginee...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Across 20 games, peripheral information shows by far the strongest contribution, with median prediction-accuracy drops in the range of 35.27-43.90% when removed. Code availability is flagged in the produc...
WHY NOW
Reinforcement Learning Analysis moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
This research reverse-engineers human decision-making in Atari games by quantifying the impact of peripheral vision, gaze, and past states on action prediction, offering insights into how agents can better mimic human strategies. Using Atari-HEAD, a large-scale Atari gameplay dataset with synchronized eye-tracking, we introduce a controlled ablation framework as a means to reverse-engineer the contribution of peripheral visual information, explicit gaze information in form of gaze maps, and past-state information from human behavior.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
We study how different visual information sources contribute to human decision making in dynamic visual environments. Using Atari-HEAD, a large-scale Atari gameplay dataset with synchronized eye-tracking, we introduce a controlled ablation framework as a means to reverse-engineer the contribution of peripheral visual information, explicit gaze information in form of gaze maps, and past-state information from human behavior.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Across 20 games, peripheral information shows by far the strongest contribution, with median prediction-accuracy drops in the range of 35.27-43.90% when removed. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Reinforcement Learning Analysis moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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This research reverse-engineers human decision-making in Atari games by quantifying the impact of peripheral vision, gaze, and past states on action prediction, offering insights into how agents can better mimic human strategies.
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Reinforcement Learning Analysis
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