In our latest paper, published in Science Advances and accessible here, we argue that a fruitful direction for researching LLMs is engaging them in behavioral experiments inspired by psychology that have traditionally been aimed at understanding human cognition and behavior. In our article, we highlight and summarize theoretical perspectives, experimental paradigms, and computational analysis techniques that this approach brings to the table. It paves the way for a “machine psychology” for LLMs that goes beyond performance benchmarks and focuses instead on computational insights that move us toward a better understanding and discovery of emergent abilities and behavioral patterns in LLMs.