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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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Book
01 Jan 1991
TL;DR: The Myth of Two Pains as mentioned in this paper states that pain comes in two types: physical and mental, and that these two types of pain are as different as land and sea, and the meanings we make out of them are different as well.
Abstract: This is a book about the meanings we make out of pain. The greatest surprise I encountered in discussing this topic over the past ten years was the consistency with which I was asked a single unvarying question: Are you writing about physical pain or mental pain? The overwhelming consistency of this response convinces me that modern culture rests upon and underlying belief so strong that it grips us with the force of a founding myth. Call it the Myth of Two Pains. We live in an era when many people believe - as a basic, unexamined foundation of thought - that pain comes divided into separate types: physical and mental. These two types of pain, so the myth goes, are as different as land and sea. You feel physical pain if your arm breaks, and you feel mental pain if your heart breaks. Between these two different events we seem to imagine a gulf so wide and deep that it might as well be filled by a sea that is impossible to navigate.

456 citations

Proceedings Article
05 Dec 2005
TL;DR: The concept of surprise is central to sensory processing, adaptation, learning, and attention as discussed by the authors, and it is used to measure the extent to which humans direct their gaze towards surprising items while watching television and video games.
Abstract: The concept of surprise is central to sensory processing, adaptation, learning, and attention. Yet, no widely-accepted mathematical theory currently exists to quantitatively characterize surprise elicited by a stimulus or event, for observers that range from single neurons to complex natural or engineered systems. We describe a formal Bayesian definition of surprise that is the only consistent formulation under minimal axiomatic assumptions. Surprise quantifies how data affects a natural or artificial observer, by measuring the difference between posterior and prior beliefs of the observer. Using this framework we measure the extent to which humans direct their gaze towards surprising items while watching television and video games. We find that subjects are strongly attracted towards surprising locations, with 72% of all human gaze shifts directed towards locations more surprising than the average, a figure which rises to 84% when considering only gaze targets simultaneously selected by all subjects. The resulting theory of surprise is applicable across different spatio-temporal scales, modalities, and levels of abstraction.

437 citations

Journal ArticleDOI
TL;DR: A computational account of how the relevant representations might arise is presented, proposing a direct connection between event learning and the learning of semantic categories.
Abstract: Our experience of the world seems to divide naturally into discrete, temporally extended events, yet the mechanisms underlying the learning and identification of events are poorly understood. Research on event perception has focused on transient elevations in predictive uncertainty or surprise as the primary signal driving event segmentation. We present human behavioral and functional magnetic resonance imaging (fMRI) evidence in favor of a different account, in which event representations coalesce around clusters or 'communities' of mutually predicting stimuli. Through parsing behavior, fMRI adaptation and multivoxel pattern analysis, we demonstrate the emergence of event representations in a domain containing such community structure, but in which transition probabilities (the basis of uncertainty and surprise) are uniform. We present a computational account of how the relevant representations might arise, proposing a direct connection between event learning and the learning of semantic categories.

425 citations

Journal ArticleDOI
TL;DR: It is suggested that, in a statistical account of conditioning, surprise signals change and therefore uncertainty and the need for new learning, and how these phenomena help constrain statistical theories of animal and human learning.

419 citations


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