The importance of identifying the presence mesoscale structures in complex networks can be hardly overestimated. So far, much attention has been devoted to detection communities, bipartite and core-periphery on binary networks: such an effort led definition a unified framework based upon score function called surprise, i.e. p-value that assigned any given partition nodes, both undirected direct...

Active inference offers a principled account of behavior as minimizing average sensory surprise over time. Applications active to control problems have heretofore tended focus on finite-horizon or discounted-surprise problems, despite deriving from the infinite-horizon, average-surprise imperative free-energy principle. Here we derive an formulation optimal principles. Our returns roots in neur...

This paper describes work currently in progress to develop a computational method for generating flashback and foreshadowing, specifically targeted at the evocation of surprise in the reader’s mind. Flashback provides a backstory to explain what caused the surprise outcome. Foreshadowing provides an implicit hint about the surprise. Our study focuses on surprise as a cognitive response. The rea...

Mainly rooted in the cognitive-psychoevolutionary model of surprise proposed by the research group of the University of Bielefeld (Meyer, Reisenzein, Schützwohl, etc.), the computational model of surprise described in this paper relies on the assumption that surprise-eliciting events initiate a series of mental processes that begin with the appraisal of unexpectedness, continue with the interru...

We propose a formal Bayesian definition of surprise to capture subjective aspects of sensory information. Surprise measures how data affects an observer, in terms of differences between posterior and prior beliefs about the world. Only data observations which substantially affect the observer's beliefs yield surprise, irrespectively of how rare or informative in Shannon's sense these observatio...

Why are some events more surprising than others? We propose that events that are more difficult to explain are those that are more surprising. The two experiments reported here test the impact of different event outcomes (Outcome-Type) and task demands (Task) on ratings of surprise for simple story scenarios. For the Outcome-Type variable, participants saw outcomes that were either known or les...

Surprise is a ubiquitous phenomenon that both draws on cognition and affects cognition, in a number of different ways. For example, in artificial intelligence an agent in a changing and imperfectly-known environment has been argued to need a surprise mechanism to survive. This symposium brings together researchers in education, computer science, cognitive psychology, and business to explore the...

Surprise describes a range of phenomena from unexpected events to behavioral responses. We propose a novel measure of surprise and use it for surprise-driven learning. Our surprise measure takes into account data likelihood as well as the degree of commitment to a belief via the entropy of the belief distribution. We find that surprise-minimizing learning dynamically adjusts the balance between...

This paper addresses the issue of how to compute the intensity of surprise in an artificial agent. Resolution of this issue is important for the further specification of the computational model of surprise proposed by Macedo and Cardoso (2001) that was implemented in artificial agents “living” in a multiagent environment. This model of surprise is mainly rooted in the cognitive-psychoevolutiona...

Surprise is informative because it drives attention [IB09] and modifies learning [SD00]. Correlates of surprise have been observed at different stages of neural processing, and found to be relevant for learning and memory formation [RR03]. Although surprise is ubiquitous, there is neither a widely accepted theory that quantitatively links surprise to observed behavior, such as the startle respo...