A Logic of General Attention Using Edge-Conditioned Event Models (Extended Version)

• URL: http://arxiv.org/abs/2505.14539v1
• Date: Tue, 20 May 2025 15:56:34 GMT
• Title: A Logic of General Attention Using Edge-Conditioned Event Models (Extended Version)
• Authors: Gaia Belardinelli, Thomas Bolander, Sebastian Watzl,
• Abstract summary: We present the first general logic of attention.<n>Our work treats attention as a modality, like belief or awareness.<n>We illustrate our framework with examples of AI agents reasoning about human attentional biases.
• Score: 1.6199400106794555
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we present the first general logic of attention. Attention is a powerful cognitive ability that allows agents to focus on potentially complex information, such as logically structured propositions, higher-order beliefs, or what other agents pay attention to. This ability is a strength, as it helps to ignore what is irrelevant, but it can also introduce biases when some types of information or agents are systematically ignored. Existing dynamic epistemic logics for attention cannot model such complex attention scenarios, as they only model attention to atomic formulas. Additionally, such logics quickly become cumbersome, as their size grows exponentially in the number of agents and announced literals. Here, we introduce a logic that overcomes both limitations. First, we generalize edge-conditioned event models, which we show to be as expressive as standard event models yet exponentially more succinct (generalizing both standard event models and generalized arrow updates). Second, we extend attention to arbitrary formulas, allowing agents to also attend to other agents' beliefs or attention. Our work treats attention as a modality, like belief or awareness. We introduce attention principles that impose closure properties on that modality and that can be used in its axiomatization. Throughout, we illustrate our framework with examples of AI agents reasoning about human attentional biases, demonstrating how such agents can discover attentional biases.

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