Title: EVA: Emergent Human-Like Visual Scanpaths in Hard Attention Models
Primary area: interpretability and explainable AI
Keywords: Hard Attention, Scanpath Similarity, Reinforcement Learning, Eye Movements, Brain-Inspired Models

Abstract: Humans recognize images by actively sampling them through saccades and fixations. Hard attention models mimic this process but are typically judged only on accuracy. We introduce EVA, a lightweight, brain-inspired hard attention model trained solely on class labels with reinforcement learning. EVA integrates a foveated glimpse network, a stochastic gaze controller, and a cortical-inspired gating mechanism. On CIFAR-10, EVA achieves state-of-the-art accuracy and efficiency among hard attention models. Importantly, without any gaze supervision, EVA emerges scanpaths that align with human eye movements, demonstrated through established metrics (NSS, AUC, DTW) and qualitative visualizations. This is the first systematic evaluation of glimpse trajectories in hard attention models, showing that efficient and interpretable vision can emerge from reinforcement-trained architectures. Such alignment not only improves interpretability: humans can infer model reasoning by shared focus, but also lays groundwork for reliable human-AI collaboration.

TL;DR: We proposed EVA, a brain-inspired hard attention model that learns only from class labels yet produces emergent human-like scanpaths in image classification task, achieving state-of-the-art accuracy and efficiency among hard attention models.