论文标题
惊喜定义的分类学
A taxonomy of surprise definitions
论文作者
论文摘要
令人惊讶的事件触发了可衡量的大脑活动，并通过影响学习，记忆和决策来影响人类行为。但是，目前在惊喜的定义上尚无共识。在这里，我们确定了统一框架中惊奇的18个数学定义。我们首先根据对代理人的信念的依赖，展示它们如何相互关系，并在它们无法区分的条件下证明它们如何相互联系，将这些定义的技术分类分为三组。除了这项技术分析之外，我们提出了一个惊喜定义的分类法，并根据它们测量的数量将它们分类为四个概念类别：（i）“预测惊喜”衡量预测与观察之间的不匹配； （ii）“更改点检测惊喜”衡量了环境变化的可能性； （iii）“信心校正的惊喜”明确解释了信心的影响； （iv）“信息获得惊喜”衡量了对新观察的信念更新。该分类法为大脑中功能作用和生理特征的原则研究奠定了基础。
Surprising events trigger measurable brain activity and influence human behavior by affecting learning, memory, and decision-making. Currently there is, however, no consensus on the definition of surprise. Here we identify 18 mathematical definitions of surprise in a unifying framework. We first propose a technical classification of these definitions into three groups based on their dependence on an agent's belief, show how they relate to each other, and prove under what conditions they are indistinguishable. Going beyond this technical analysis, we propose a taxonomy of surprise definitions and classify them into four conceptual categories based on the quantity they measure: (i) 'prediction surprise' measures a mismatch between a prediction and an observation; (ii) 'change-point detection surprise' measures the probability of a change in the environment; (iii) 'confidence-corrected surprise' explicitly accounts for the effect of confidence; and (iv) 'information gain surprise' measures the belief-update upon a new observation. The taxonomy poses the foundation for principled studies of the functional roles and physiological signatures of surprise in the brain.
