Putting the Ghost in the Machine: Emulating Cognitive Style in Large Language Models

Abstract

Large Language Models (LLMs) have emerged as pivotal technology in the evolving world. Their significance in design lies in their transformative potential to support engineers and collaborate with design teams throughout the design process. However, it is not known whether LLMs can emulate the cognitive and social attributes which are known to be important during design, such as cognitive style. This research evaluates the efficacy of LLMs to emulate aspects of Kirton’s Adaption–Innovation theory, which characterizes individual preferences in problem-solving. Specifically, we use LLMs to generate solutions for three design problems using two different cognitive style prompts (adaptively framed and innovatively framed). Solutions are evaluated with respect to feasibility and paradigm relatedness, which are known to have discriminative value in other studies of cognitive style. We found that solutions generated using the adaptive prompt tend to display higher feasibility and are paradigm-preserving, while solutions generated using the innovative prompts were more paradigm-modifying. This aligns with prior work and expectations for design behavior based on Kirton's Adaption–Innovation theory. Ultimately, these results demonstrate that LLMs can be prompted to accurately emulate cognitive style.