Abstract
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.
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Dessalles, JL. (2013). Algorithmic Simplicity and Relevance. In: Dowe, D.L. (eds) Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence. Lecture Notes in Computer Science, vol 7070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44958-1_9
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DOI: https://doi.org/10.1007/978-3-642-44958-1_9
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