Problem-Solving as a Language: A Computational Lens into Human and Monkey Intelligence

Human intelligence is characterized by our remarkable ability to solve complex problems. This involves planning a sequence of actions that leads us from an initial state to a desired goal state. Quantifying and comparing problem-solving capabilities across species and finding its evolutional roots is a fundamental challenge in cognitive science, and is critical for understanding how the brain carries out this intricate process. In this study, we introduce the Language of Problem-Solving (LoPS) model as a novel quantitative framework that investigates the structure of problem-solving behavior through a language model. We adapted the classic Pac-Man game as a cross-species behavioral paradigm to test both humans and macaque monkeys. Using the LoPS model, we extracted the latent structure — or grammar — embedded in the agents’ gameplay, revealing the non-Markovian temporal structure of their problem-solving behavior. The LoPS model captured fine-grained individual differences among the players and revealed the striking differences in the complexity and hierarchical organization of problem-solving behavior between humans and monkeys, reflecting the distinct cognitive capabilities of each species. Furthermore, both humans and monkeys evolved their LoPS grammars during learning, progressing from simpler to more complex ones, suggesting that the language of problem-solving is not fixed, but rather evolves to support more sophisticated and efficient problem-solving. Through the lens of a language model, our study provides insights into how humans and monkeys break down problem-solving into compositional units and navigate complex tasks. This framework deepens our understanding of human intelligence and its evolution, and establishes a foundation for future investigations of the neural mechanisms of problem-solving.