Imperfectly Imperfect: Evaluating Graded Cognitive Diversity in LLM Persona Simulations

Sion Weatherhead, Ben Newell, Aaron Belbasis, Flora Salim

Large language model (LLM) agents are increasingly used as synthetic participants in social, behavioural, and decision-making research, yet it remains unclear whether they can express graded cognitive diversity as assigned when engaging in simulated behaviour. We introduce a cognition-suite evaluation that tests whether simulated populations reproduce human variation in reasoning, self-reported traits, their associations, and downstream behaviour. Agents conditioned on synthetic or human-derived profiles of cognitive reflection, Need for Cognition, and Big Five personality answer the same suite as a cohort of 473 adults: two newly assembled tests, a Cognitive Reflection Test (CRT) and a Behavioural Reflection Test (BRT) whose decision scenarios measure reflection in behaviour, alongside an established CRT and the two trait questionnaires. Across seven full-battery simulation runs of four models, agents recover Need for Cognition and Big Five trait distributions on self-report questionnaires, but fall short on simulating diversity of cognitive task performance and downstream behaviours. In the four Claude Sonnet 4.6 runs, every agent scores full marks on both CRTs, whatever…