Should Agents Have Two Systems to Track Beliefs and Belief-Like States?

Abstract

Adult humans are typically capable of impressive, often recursive, reasoning about the mental states of others, but recent evidence has suggested that said reasoning, called Theory of Mind reasoning (ToM), is not easy or automatic. This has lead to the theory that human ToM reasoning requires two systems. One system, efficient but inflexible, enables rapid judgements by operating without explicit modeling of beliefs, while a separate, effortful system, enables richer predictions over more complex belief encodings. We argue that computational ToM requires a similar distinction. However, we propose a different model: a single process, but with effortful re-representation leading to two phases of ToM reasoning. Efficient reasoning, in our view, occurs over representations that include actions, but not necessarily explicit belief states. Effortful reasoning, then, involves re-representation of these initial encodings in order to handle errors, resolve real-world conflicts, and fully account for others’ belief states. We present an implemented computational model, based in memory retrieval and structural alignment, and discuss possible implications for computational agents in human-machine teams.