= PDF Reprint, = BibTeX entry, = Online Abstract
J. Tanner, L. Itti, Goal relevance as a quantitative model of human task relevance, Psychological review, Vol. 124, No. 2, p. 168, American Psychological Association, Mar 2017. [2016 impact factor: 7.638]
Abstract: 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.
Themes: Computational Modeling, Model of Bottom-Up Saliency-Based Visual Attention, Model of Top-Down Attentional Modulation, Human Eye-Tracking Research
Copyright © 2000-2007 by the University of Southern California, iLab and Prof. Laurent Itti.
This page generated by bibTOhtml on Fri Jan 26 09:25:23 PST 2018