A groundbreaking study has peered into the inner workings of a large language model (LLM), specifically GPT-2-XL, to investigate how it processes language at the neuron level. Researchers used psycholinguistic tests typically given to humans to probe the model’s abilities in sound-shape association, sound-gender association, and implicit causality. Surprisingly, while the model struggled to connect sounds with shapes, it demonstrated human-like abilities in associating sounds with genders and understanding causality implied by different verbs. By selectively activating or silencing specific neurons, the researchers found a direct correlation between these neurons and the model’s performance on the gender and causality tasks. When key neurons were amplified, the model's performance improved, while silencing them led to a decrease. This suggests that certain linguistic abilities are tied to specific neurons, much like in the human brain. However, for tasks where the AI didn't show an inherent understanding, such as linking sounds to shapes, manipulating neurons didn't change performance, indicating that the model either hasn't learned this association or employs a more distributed representation. The findings offer an intriguing glimpse into how language models encode linguistic phenomena and how we may be able to influence their behavior by fine-tuning individual neuron activity. While GPT-2-XL is an older model, this research paves the way for deeper exploration of newer, larger LLMs and the complex cognitive processes they exhibit. Future research aims to extend these findings to more advanced models, potentially revealing how these AI systems achieve ever-more-complex forms of language processing.