A Contrast-Based Computational Model of Surprise and Its Applications



We review our work on a contrast-based computational model of surprise and its applications. The review is contextualized within related research from psychology, philosophy, and particularly artificial intelligence. Influenced by psychological theories of surprise, the model assumes that surprise-eliciting events initiate a series of cognitive processes that begin with the appraisal of the event as unexpected, continue with the interruption of ongoing activity and the focusing of attention on the unexpected event, and culminate in the analysis and evaluation of the event and the revision of beliefs. It is assumed that the intensity of surprise elicited by an event is a nonlinear function of the difference or contrast between the subjective probability of the event and that of the most probable alternative event (which is usually the expected event); and that the agent's behavior is partly controlled by actual and anticipated surprise. We describe applications of artificial agents that incorporate the proposed surprise model in three domains: the exploration of unknown environments, creativity, and intelligent transportation systems. These applications demonstrate the importance of surprise for decision making, active learning, creative reasoning, and selective attention.


Surprise, Computational models, Artificial agents, Exploration of unknown environments, Creativity, Selective attention


Topics in Cognitive Science, Edward Munnich, Mark Keane, and Meadhbh Foster , November 2017


Cited by

Year 2020 : 2 citations

 Simandan, D. (2020). Being surprised and surprising ourselves: a geography of personal and social change. Progress in Human Geography, 44(1), 99-118.

 Vignero, L., & Demey, L. (2020). The perfect surprise: a new analysis in dynamic epistemic logic. Logic Journal of the IGPL, 28(3), 341-362.

Year 2019 : 3 citations

 Reisenzein, R., Horstmann, G., & Schützwohl, A. (2019). The cognitive?evolutionary model of surprise: A review of the evidence. Topics in cognitive science, 11(1), 50-74.

 Gerten, J., & Topolinski, S. (2019). Shades of surprise: Assessing surprise as a function of degree of deviance and expectation constraints. Cognition, 192, 103986.

 Munnich, E. L., Foster, M. I., & Keane, M. T. (2019). Editors’ Introduction and Review: An Appraisal of Surprise: Tracing the Threads That Stitch It Together. Topics in cognitive science, 11(1), 37-49.

Year 2017 : 1 citations

 Schutzwohl, A., Reisenzein, R., & Horstmann, G. (2017). The Cognitive-Evolutionary Model of Surprise: A Review of the Evidence.