TY - JOUR
T1 - Measuring and Modeling Visual Appearance
AU - Maloney, Laurence T.
AU - Knoblauch, Kenneth
N1 - Publisher Copyright:
© 2020 Annual Reviews Inc.. All rights reserved.
PY - 2020/9/15
Y1 - 2020/9/15
N2 - In studying visual perception, we seek to develop models of processing that accurately predict perceptual judgments. Much of this work is focused on judgments of discrimination, and there is a large literature concerning models of visual discrimination. There are, however, non-threshold visual judgments, such as judgments of the magnitude of differences between visual stimuli, that provide a means to bridge the gap between threshold and appearance. We describe two such models of suprathreshold judgments, maximum likelihood difference scaling and maximum likelihood conjoint measurement, and review recent literature that has exploited them.
AB - In studying visual perception, we seek to develop models of processing that accurately predict perceptual judgments. Much of this work is focused on judgments of discrimination, and there is a large literature concerning models of visual discrimination. There are, however, non-threshold visual judgments, such as judgments of the magnitude of differences between visual stimuli, that provide a means to bridge the gap between threshold and appearance. We describe two such models of suprathreshold judgments, maximum likelihood difference scaling and maximum likelihood conjoint measurement, and review recent literature that has exploited them.
KW - MLCM
KW - MLDS
KW - maximum likelihood conjoint measurement
KW - maximum likelihood difference scaling
KW - proximity, diagnostics
KW - scaling
KW - suprathreshold
UR - http://www.scopus.com/inward/record.url?scp=85091192108&partnerID=8YFLogxK
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U2 - 10.1146/annurev-vision-030320-041152
DO - 10.1146/annurev-vision-030320-041152
M3 - Review article
C2 - 32421445
AN - SCOPUS:85091192108
SN - 2374-4642
VL - 6
SP - 519
EP - 537
JO - Annual review of vision science
JF - Annual review of vision science
ER -