Viterbi School of Engineering.
Psychol Rev. 2017 Mar;124(2):168-178. doi: 10.1037/rev0000053.
The concept of relevance is used ubiquitously in everyday life. However, a general quantitative definition of relevance has been lacking, especially as pertains to quantifying the relevance of sensory observations to one's goals. We propose a theoretical definition for the information value of data observations with respect to a goal, which we call "goal relevance." We consider the probability distribution of an agent's subjective beliefs over how a goal can be achieved. When new data are observed, its goal relevance is measured as the Kullback-Leibler divergence between belief distributions before and after the observation. Theoretical predictions about the relevance of different obstacles in simulated environments agreed with the majority response of 38 human participants in 83.5% of trials, beating multiple machine-learning models. Our new definition of goal relevance is general, quantitative, explicit, and allows one to put a number onto the previously elusive notion of relevance of observations to a goal. (PsycINFO Database Record
关联性的概念在日常生活中被广泛应用。然而，对于关联性的定量定义，特别是对于将感官观察与目标的关联性进行量化，一直缺乏一个普遍的定义。我们提出了一个关于数据观测相对于目标的信息价值的理论定义，我们称之为“目标关联性”。我们考虑了一个主体对于如何实现目标的主观信念的概率分布。当新的数据被观察到，它的目标关联性可以通过观察前后的信念分布的 Kullback-Leibler 散度来衡量。对于模拟环境中不同障碍的关联性的理论预测，在 83.5%的试验中，与 38 名人类参与者的多数反应一致，击败了多个机器学习模型。我们的新的目标关联性定义是通用的、定量的、明确的，并且可以将观察与目标的关联性这个以前难以捉摸的概念转化为一个数字。
