Algorithmic Simplicity and Relevance
This addresses the problem of understanding human cognition and communication for researchers in psychology and AI, but it appears incremental as it extends known complexity principles to new domains without major breakthroughs.
The paper tackles the problem of modeling higher cognitive processes like situation selection and conversation moves by proposing that relevance is determined by situations being simpler to describe than to generate, offering a predictive model for interestingness and conversational appropriateness.
The human mind is known to be sensitive to complexity. For instance, the visual system reconstructs hidden parts of objects following a principle of maximum simplicity. We suggest here that higher cognitive processes, such as the selection of relevant situations, are sensitive to variations of complexity. Situations are relevant to human beings when they appear simpler to describe than to generate. This definition offers a predictive (i.e. falsifiable) model for the selection of situations worth reporting (interestingness) and for what individuals consider an appropriate move in conversation.