Bayesian Models of Attention

A number of recent theoretical models, based on Bayesian probability theory, have formalized the need of attentional selection to be shaped by computational desiderata (Dayan and Zemel 1999; Dayan and Yu 2002; Yu and Dayan 2005a, 2005b; Yu et al. 2009). Bayesian probability theory is a powerful and increasingly prevalent ideal observer (Green and Swets 1966) framework for understanding selective processing, as it provides a set of statistically optimal tools for the quantification and integration of imperfect information sources. It has been successfully applied to explain human and animal behaviour in a number of cognitive tasks, including perceptual inference (Grenander 1976-81; Bolle and Cooper 1984; Geman and Geman 1984; Marroquin et al. 1987; Szeliski 1989; Clark and Yuille 1990; Knill and Richards 1996), multi-modal sensory integration (Jacobs 1999; Ernst and Banks 2002; Battaglia et al. 2003; Dayan et al. 2000; Kording and Wolpert 2004; Shams et al. 2005; Kording et al. 2007a), reward learning (Behrens et al. 2007), and motor adaptation (Kording et al. 2007b). In the following, we will review recently proposed Bayesian models of attention for learning, covert spatial attention, and overt spatial attention. (PsycInfo Database Record (c) 2022 APA, all rights reserved