Do you see what I see? Probing visual perception in artificial intelligence with a Gestalt-theory driven Optical Illusion Dataset

Machine learning algorithms are increasingly deployed in safety-critical domains such as autonomous driving and medical diagnostics, where ambiguous data poses a fundamental challenge. Optical illusions offer a compelling window into human perceptual processing, yet remain largely absent from machine learning benchmarks. We introduce Ambivision, a dataset of optical illusions featuring intermingled animal pairs grounded in Gestalt psychological principles such as proximity, similarity, closure, etc., designed to evoke genuine perceptual ambiguity rather than superficial visual difficulty. Using Ambivision as a diagnostic probe, we show that widely used attribution methods (Grad-CAM, Integrated Gradients, PipNet, ACE) cannot produce discriminative, human interpretable explanations when both readings rest on the same pixels. We further find, across seven architectures, that supplying an explicit cue to the discriminative region (gaze direction or the eye) improves classification on ambiguous data over both no-cue and random-location baselines, whereas appending the same information as coordinates before the classifier head does not, which is an observation about where the usable signal lives, not a general-purpose training recipe. We release the dataset and the generation and annotation scripts. Ambivision offers a replicable, theoretically grounded methodology for studying where computational and human perception diverge. The dataset is accessible via a kaggle link which will be provided upon acceptance.