Profiles of Mind: How LLMs Perform on Assessments of Cognitive Development

Abstract

We compared four large language models (LLMs; ChatGPT, Grok, Gemini, and DeepSeek) with humans on tests of cognitive development, assessing relational integration, linguistic awareness, general and domain-specific reasoning, and cognitive self-awareness to specify how LLMs compare with humans along cognitive development hierarchies. LLMs also discussed how Descartes’s Cogito applies to them and rated themselves on aspects of Artificial General Intelligence (AGI). Accordingly, we propose a novel interdisciplinary comparison of human and LLM capabilities that integrates developmental, cognitive, and psychometric psychology. Overall, the processes in humans and LLMs were highly similar. All LLMs attained perfect linguistic and metalinguistic performance. ChatGPT and Gemini outperformed university students in mathematics and causal reasoning. Grok performed slightly better and DeepSeek considerably worse. All LLMs underperformed in visual–spatial tasks. Self-evaluation profiles broadly mirrored performance profiles: ChatGPT and Grok rated themselves highly in reasoning and low in visualization, Gemini inflated visualization by reframing it as linguistic creativity, and DeepSeek consistently underrated itself. Each LLM restated Descartes’s Cogito differently, reflecting its own priorities, and denied having high AGI; these self-characterizations were generally stable about a year later. Therefore, LLMs displayed “subjective” task scaling, implying algorithmic or functional self-monitoring, capturing their architectural profile of performance, but they were modest in claiming above-human intelligence. We discuss implications for an integrated natural–artificial intelligence theory. We also sketch a developmental engineering model that might remove the limitations of each LLM.