Tell me Mr. AI, what do you see in this image?

TL;DR

This study applies the Rorschach inkblot test to 61 AI models to compare their interpretative responses with human profiles, revealing systematic differences in semantic richness and affective load.

Contribution

It introduces a novel framework for evaluating AI perception using psycho-semantic variables inspired by the Rorschach tradition, highlighting biases in AI models.

Findings

AI responses are highly non-random with systematic semantic convergence.

Human responses show higher affective load and variability.

AI models favor formal coherence over affective or symbolic elaboration.

Abstract

Background: The Rorschach inkblots are ambiguous stimuli developed to evoke subjective interpretations in humans, while modern artificial intelligence (AI) models are trained to recognize well established patterns and classes. The comparison of these two opposite systems arises a simple and provocative question: what happens when we ask an AI model to interpret an inkblot that "is not supposed to represent anything predefined"? Methods: We submitted the complete set of ten Rorschach inkblots to 61 AI models pretrained on the ImageNet dataset, spanning multiple architectural families. Model predictions were analyzed at the level of top-ranked classes and were quantified using a selected set of psycho-semantic variables inspired by the Rorschach tradition. Statistical analyses examined the effects of model family, computational complexity, and image conditions, comparing model-generated responses with human reference profiles. Findings: Across all architectures, model responses were highly non-random and showed systematic semantic convergence and inter-model agreement. However, quantitative analyses revealed a clear and robust separation between human responses and all AI model families. Human profiles exhibited substantially higher affective load, semantic richness, projected agency, and variability, whereas AI models converged toward frequent, formally coherent, and perceptually stable interpretations. Interpretation: Vision models consistently project the semantic organization learned, favoring consensus and formal coherence over affective or symbolic elaboration. Applying the Rorschach test to AI systems does not assess human-like cognition but provides a principled framework for exposing perceptual and semantic biases embedded in contemporary computer vision models.