Bayesian accounts of covert selective attention: A tutorial review

Abstract

Decision making and optimal observer models offer an important theoretical approach to the study of covert selective attention. While their probabilistic formulation allows quantitative comparison to human performance, the models can be complex and their insights are not always immediately apparent. Part 1 establishes the theoretical appeal of the Bayesian approach, and introduces the way in which probabilistic approaches can be applied to covert search paradigms. Part 2 presents novel formulations of Bayesian models of 4 important covert attention paradigms, illustrating optimal observer predictions over a range of experimental manipulations. Graphical model notation is used to present models in an accessible way and Supplementary Code is provided to help bridge the gap between model theory and practical implementation. Part 3 reviews a large body of empirical and modelling evidence showing that many experimental phenomena in the domain of covert selective attention are a set of by-products. These effects emerge as the result of observers conducting Bayesian inference with noisy sensory observations, prior expectations, and knowledge of the generative structure of the stimulus environment.

A Quantitative Relationship between Signal Detection in Attention and Approach/Avoidance Behavior

“…Moreover, foundational work by Busemeyer and Townsend as well as teams led by Usher, McClelland, Rangel and others have also included frameworks emphasizing attentional models of reward and value-based choice (Busemeyer and Townsend, 1993 ; Roe et al, 2001 ; Usher and McClelland, 2001 , 2004 ; Krajbich et al, 2010 , 2012 ; Milosavljevic et al, 2010 ; Krajbich and Rangel, 2011 ; Vincent, 2015 ). Such work has involved both linear (e.g., Krajbich et al, 2010 , 2012 ; Krajbich and Rangel, 2011 ) and non-linear (e.g., Busemeyer and Townsend, 1993 ; Usher and McClelland, 2001 , 2004 ) diffusion models of evidence accumulation bearing on value-based decisions (see Vincent, 2015 for recent review). In particular, Usher and McClelland have investigated the role of loss aversion (analogous to predictability bias) in multi-alternative value-based choice under a diffusion framework (Usher and McClelland, 2004 ).…”

Visual Search Revived: The Slopes Are Not That Slippery: A Reply to Kristjansson (2015)

“…In this case, each word probably needs to be fixated before it is read, enforcing seriality. With tasks that do not require eye movements, models, like Guided Search, that propose covert, serial deployments of attention can be countered by models that propose parallel processing of all items ( Palmer, 1995 , see also Vincent, 2015 ). Standard RT × set size data will not distinguish these models ( Townsend, 1990 ; Townsend & Wenger, 2004 ).…”

Reconsidering Visual Search

“…Approaches that assume that observers adapt to the search environment, such as with Bayesian updating of preferences and biases (e.g. Vincent, 2015 ) also show promise in accounting for the operation of visual attention. All these approaches bypass the serial or parallel distinction (see also Wolfe, 2007 ).…”