While attention has been an increasingly popular component in deep neural networks to both interpret and boost performance of models, little work has examined how attention progresses to accomplish a task and whether it is reasonable. In this work, we propose an Attention with Reasoning capability (AiR) framework that uses attention to understand and improve the process leading to task outcomes. We first define an evaluation metric based on a sequence of atomic reasoning operations, enabling quantitative measurement of attention that considers the reasoning process. We then collect human eye-tracking and answer correctness data, and analyze various machine and human attentions on their reasoning capability and how they impact task performance. Furthermore, we propose a supervision method to jointly and progressively optimize attention, reasoning, and task performance so that models learn to look at regions of interests by following a reasoning process. We demonstrate the effectiveness of the proposed framework in analyzing and modeling attention with better reasoning capability and task performance. The code and data are available at https://github.com/szzexpoi/AiR.

We present typical examples to demonstrate that guiding models to look at places following the reasoning process (AiR-M) leads to better attention and task performance. While the compared state-of-the-art models only have a static attention map, AiR-M shows progressive attention following the reasoning process. Better alignments with the ground-truth are also shown (AiR-E scores).

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We present typical examples to demonstrate that where humans look affect answer correctness. The correct and incorrect attention maps evolve over time, suggesting the temporal development of attention. The correct attention maps align better with reasoning annotations.

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