Visual Large Language Models Exhibit Human-Level Cognitive Flexibility

Guangfu Hao, Frederic Alexandre, Shan Yu

Keywords: Cognitive Flexibility, Visual Large Language Models, Wisconsin Card Sorting Test

TL;DR: This study evaluates the cognitive flexibility of Visual Large Language Models (VLLMs) using the Wisconsin Card Sorting Test, revealing their human-like set-shifting capabilities influenced by input modality and prompting strategies.

Abstract: Cognitive flexibility has been extensively studied in human cognition but remains relatively unexplored in the context of Visual Large Language Models (VLLMs). This study assesses the cognitive flexibility of state-of-the-art VLLMs (GPT-4o, Gemini-1.5 Pro, and Claude-3.5 Sonnet) using the Wisconsin Card Sorting Test (WCST), a classic measure of set-shifting ability. Our results reveal that VLLMs achieve or surpass human-level set-shifting capabilities under chain-of-thought prompting with text-based inputs. However, their abilities are highly influenced by both input modality and prompting strategy. In addition, we find that through role-playing, VLLMs can simulate various functional deficits aligned with patients having impairments in cognitive flexibility, suggesting that VLLMs may possess a cognitive architecture, at least regarding the ability of set-shifting, similar to the brain. This study reveals the fact that VLLMs have already approached the human level on a key component underlying our higher cognition, and highlights the potential to use them to emulate complex brain processes.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 4401