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Artificial intelligence is emerging as a powerful tool for probing the mysteries of the human brain. While AI neural networks, like the large language model Centaur (based on Meta’s Llama 3.1), aren’t perfect replicas of biological brains, their shared architecture of interconnected components allows researchers to model and predict human behavior.

A new study published in *Nature* details how Centaur, fine-tuned with data from 160 psychology experiments, outperformed traditional models in forecasting human responses. Proponents believe analyzing Centaur’s internal workings could offer valuable clues about human cognition. However, some psychologists caution that Centaur’s success may be primarily attributed to its vast scale, rather than genuine insight into cognitive processes.

An alternative approach utilizes smaller, more manageable neural networks to simulate behavior in both animals and humans. These smaller models, while task-specific, enable researchers to monitor individual neuron activity and generate testable hypotheses about human cognitive functions. These diverse approaches underscore the growing role of AI in unraveling the complexities of the human mind, but emphasize the need for continued scrutiny and validation of AI-driven insights.