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Surprise

About: Surprise is a research topic. Over the lifetime, 4371 publications have been published within this topic receiving 99386 citations.


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Journal ArticleDOI
TL;DR: Harrison and March as mentioned in this paper showed that intuitive predictions of outcomes are non-regressive, i.e., they do not obey the statistical principle of regression toward the mean (Kahneman and Tversky, 1973), and that expectations tend to be more extreme than actual outcomes of decisions, producing a preponderance of disappointments among postdecision surprises.
Abstract: Bernard Goitein In the March 1984 issue of ASQ, Harrison and March ("Decision Making and Postdecision Surprises") mathematically derive the existence of a structural bias toward postdecision disappointment. They do so by building on research in behavioral decision theory. This research has shown that intuitive predictions of outcomes are nonregressive, i.e., they do not obey the statistical principle of regression toward the mean (Kahneman and Tversky, 1973). Expectations therefore tend to be more extreme than actual outcomes of decisions, producing a preponderance of disappointments among postdecision surprises (Harrison and March, 1984).1 If the structural bias toward disappointment is straightforwardly translated into the distribution of subjective surprise, then decisions, even good decisions, are likely to be disappointing, because their outcomes fail to meet the decision maker's expectations. If, however, people have developed effective mechanisms for coping with the structural bias, then Harrison and March's finding may be only an interesting mathematical demonstration.

19 citations

Journal ArticleDOI
TL;DR: A Belief-Desire-Intention-like architecture for an explorer agent in which the psychological constructs of surprise and curiosity play an important role in decision-making, particularly in the selection of view-points during the process of exploring unknown environments is described.

19 citations

01 Jan 2014
TL;DR: This paper uses agent-based models to describe human behaviour in an n-player extension of rock-paper-scissors called the Mod game and finds that characteristic cyclic behaviour in the choices of participants that contradicts equilibrium predictions from classical game theory can be explained through the application of higher orders of theory of mind.
Abstract: When people engage in social interactions, they often rely on their theory of mind, their ability to reason about unobservable mental content of others such as beliefs, goals, and intentions. This ability allows them to both understand why others behave the way they do as well as predict future behaviour. People can also make use of higher-order theory of mind by applying theory of mind recursively, and reason about the way others make use of theory of mind such as in the sentence "Alice believes that Bob does not know about the surprise party". In this paper, we use agent-based models to describe human behaviour in an n-player extension of rock-paper-scissors called the Mod game. In previous work, we have shown how in similar competitive settings, the ability to make use of higher orders of theory of mind can be beneficial. We find that characteristic cyclic behaviour in the choices of participants that contradicts equilibrium predictions from classical game theory can be explained through the application of higher orders of theory of mind. Our results suggest that participants engage in higher orders of theory of mind reasoning in repeated play of the Mod game than previously reported in normal-form games and in repeated rock-paper-scissors games.

19 citations

Journal ArticleDOI
01 Jul 2010
TL;DR: A real-time model for wrinkles, blushing, sweating, tearing, and respiration that is capable of implementing specific autonomic signal patterns associated with certain affective states is described and the relevance of these results to artificial intelligence and intelligent virtual agents is discussed.
Abstract: Specific patterns of autonomic activity have been reported when people experience emotions. Typical autonomic signals that change with emotion are wrinkles, blushing, sweating, tearing, and respiration. This article explores whether these signals can also influence the perception of emotion in embodied agents. The article first reviews the literature on specific autonomic signal patterns associated with certain affective states. Next, it proceeds to describe a real-time model for wrinkles, blushing, sweating, tearing, and respiration that is capable of implementing those patterns. Two studies are then described. In the first, subjects compare surprise, sadness, anger, shame, pride, and fear expressed in an agent with or without blushing, wrinkles, sweating, or tears. In the second, subjects compare excitement, relaxation, focus, pain, relief, boredom, anger, fear, panic, disgust, surprise, startle, sadness, and joy expressed in an agent with or without typical respiration patterns. The first study shows a statistically significant positive effect on perception of surprise, sadness, anger, shame, and fear. The second study shows a statistically significant positive effect on perception of excitement, pain, relief, boredom, anger, fear, panic, disgust, and startle. The relevance of these results to artificial intelligence and intelligent virtual agents is discussed.

19 citations

Journal ArticleDOI
TL;DR: The potential of surprise is investigated in two experiments with prevocational students in the domain of proportional reasoning to provide some evidence that a narrative technique as surprise can be used in game-based learning for the purpose of learning.
Abstract: The challenge in serious games is to improve the effectiveness of learning by stimulating relevant cognitive processes. In this paper, we investigate the potential of surprise in two experiments with prevocational students in the domain of proportional reasoning. Surprise involves an emotional reaction, but it also serves a cognitive goal as it directs attention to explain why the surprise occurred and can play a key role in learning. In our experiments, surprises were triggered by a surprising event, ie, a nonplaying character who suddenly appeared and changed characteristics of a problem. In Experiment 1—comparing a surprise condition with a control condition—we found no overall differences, but the results suggested that surprise may be beneficial for higher level students. In Experiment 2, we combined Expectancy strength (Strong vs. Weak) with Surprise (Present vs. Absent) using higher level students. We found a marginal overall effect of surprising events on learning indicating that students who experienced surprises learned more than students who were not exposed to these surprises but we found a stronger effect of surprise when we included existing proportional reasoning skill as factor. These results provide some evidence that a narrative technique as surprise can be used in game-based learning for the purpose of learning.

19 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023675
20221,546
2021216
2020237
2019239
2018226