Bringing Comparative Cognition To Computers

Artificial intelligence (AI) systems, from large language models (LLMs) to reinforcement learning agents, now exhibit behaviours once assumed to be exclusive to humans and other animals. As such, researchers are increasingly probing these systems using psychological methods, asking questions about how they explore new environments, make decisions in risky conditions, and reason about their own uncertainty [1]. This work appears to be driven by two motivations: better characterising what AI can and cannot do so that we can improve it and use it safely; and the tantalising proposition that AI constitutes a new class of cognitive system worthy of serious scientific attention, not only to learn more about how they work but to better understand our own cognition [2]. But applying methods designed for human cognitive psychology to test AI risks both under-and over-attributing cognitive capacities to them - because those tests may be ill-designed for these non-human subjects. Comparative cognition - the study of non-human animal behaviour - has grappled with similar challenges for decades. By adopting its methods, AI research could avoid pitfalls, join the cognitive sciences, and clarify the nature of cognition itself.