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Is Task-Irrelevant Learning Really Task-Irrelevant?

In the present study we address the question of whether the learning of task-irrelevant stimuli found in the paradigm of task-irrelevant learning (TIPL) [1]-[9] is truly task irrelevant. To test the hypothesis that associations that are beneficial to task-performance may develop between the task-relevant and task-irrelevant stimuli, or the task-responses and the task-irrelevant stimuli, we desi...

Failures to Ignore Entirely Irrelevant Distractors

In daily life (e.g., in the work environment) people are often distracted by stimuli that are clearly irrelevant to the current task and should be ignored. In contrast, much applied distraction research has focused on task interruptions by information that requires a response and therefore cannot be ignored. Moreover, the most commonly used laboratory measures of distractibility (e.g., in the r...

Learning to Identify Irrelevant State Variables

When they are available, safe state abstractions improve the efficiency of reinforcement learning algorithms by allowing an agent to ignore irrelevant distinctions between states while still learning an optimal policy. Prior work investigated how to incorporate state abstractions into existing algorithms, but most approaches required the user to provide the abstraction. How to discover this kin...

Our Irrelevant Right to Health Care

Towards Learning to Ignore Irrelevant State Variables

Hierarchical methods have attracted much recent attention as a means for scaling reinforcement learning algorithms to increasingly complex, real-world tasks. These methods provide two important kinds of abstraction that facilitate learning. First, hierarchies organize actions into temporally abstract high-level tasks. Second, they facilitate task dependent state abstractions that allow each hig...