A Pouget

A Tversky

ACR Martins

AF Healy

AM Treisman

B Anderson

B Li

B McElree

BA Dosher

Benjamin T. Vincent

BT Vincent

C Morvan

CC Wood

CJ Downing

D Fernandez-Duque

D Kahneman

D Liston

EL Cameron

GJ Zelinsky

H Mazyar

H Zhang

HJ Müller

HS Bashinski

IC Gould

J Duncan

J Fennell

J Najemnik

J Palmer

JA Droll

JM Beck

JM Wolfe

JS Bowers

L Acerbi

LW Nolte

M Druker

M Eckstein

M Jones

M Kubovy

MD Lee

MI Jordan

ML Shaw

MW Spratling

N Bruce

P Verghese

PL Smith

R Ratcliff

RA Kinchla

RHS Carpenter

RJ Krauzlis

RL Gregory

RPN Rao

RS Zemel

S Baldassi

S Bitzer

S Farrell

S Zhang

SD Brown

SS Shimozaki

T Wickens

TL Griffiths

V Navalpakkam

W James

WA Johnston

WA Schoonveld

WA Wickelgren

WJ Ma

WT Maddox

Z-L Lu

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.</p

Bayesian accounts of covert selective attention:a tutorial review

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