A flexible behavioral method for measuring human and artificial intelligence alignment using representational similarity analysis.

As we consider entrusting large language models (LLMs) with key societal and decision-making roles, measuring their alignment with human cognition becomes critical. This requires methods that can assess how these systems represent information and facilitate comparisons with human understanding across diverse tasks. To meet this need, we adapted representational similarity analysis (RSA), using pairwise ratings to help quantify alignment between AIs and humans. Among the models we studied, GPT-5-mini and Claude Sonnet 4.5 showed the strongest alignment with human text ratings. Llama-4 was the best aligned open-source model. However, gaps between LLM and human behavior remain. No model we studied adequately captured the inter-individual variability observed among human participants, and models only moderately aligned with individual human responses. We demonstrate the utility of this approach across multiple modalities (words, sentences, and images), helping further our understanding of how LLMs encode knowledge, and enabling an examination of alignment with human cognition.