Proceedings of the Annual Meeting of the Cognitive Science Society

Human eye movements in visual recognition reflect a balance between foveal sampling and peripheral context, but evaluating whether artificial scanpaths are "human-like" is difficult on object-centric datasets with strong center bias. Using Gaze-CIFAR-10, we show that a trivial center-fixation baseline achieves surprisingly strong scores under common scanpath metrics, blurring the distinction between behavioral alignment and central tendency. We introduce GCS (Gaze Consistency Score), a practical center-debiased and movement-aware score that normalizes against human and corner references, subtracts the center baseline, and adds a small movement-similarity term. Applying GCS to a hard-attention classifier under varied fovea-periphery constraints identifies a restricted mid-range regime: a moderate foveal patch with peripheral context yields stronger center-debiased alignment than either narrower or broader alternatives. This regime is not identified by accuracy alone; the highest-accuracy setting differs from the best-GCS setting. These results highlight the need for bias-aware scanpath evaluation and suggest that, on Gaze-CIFAR-10 and under this hard-attention setting, perceptual constraints shape when task-trained policies appear relatively human-like.