scholarly journals Inferential challenges for General Recognition Theory: Mean-shift Integrality and Perceptual Configurality

2010 ◽  
Vol 10 (7) ◽  
pp. 1211-1211
Author(s):  
T. Menneer ◽  
M. Wenger ◽  
L. Blaha
Keyword(s):  
Mean Shift ◽  
General Recognition Theory ◽  
Recognition Theory ◽  
General Recognition

scholarly journals Detecting mean shift integrality using the Hering illusion: initial results using general recognition theory and systems factorial theory

2018 ◽  
Vol 18 (10) ◽  
pp. 797
Author(s):  
Michael Wenger ◽  
Douglas Bryant ◽  
James Townsend ◽  
Ru Zhang ◽  
Yanjun Liu
Keyword(s):  
Mean Shift ◽  
General Recognition Theory ◽  
Recognition Theory ◽  
General Recognition ◽  
Initial Results

Multidimensional Signal Detection Theory

2015 ◽  
Author(s):  
F. Gregory Ashby ◽  
Fabian A. Soto
Keyword(s):  
Signal Detection ◽  
Signal Detection Theory ◽  
Detection Theory ◽  
Response Alternative ◽  
Perceptual Independence ◽  
Multidimensional Signal ◽  
General Recognition Theory ◽  
Multivariate Probability Distribution ◽  
Recognition Theory ◽  
General Recognition

Multidimensional signal detection theory is a multivariate extension of signal detection theory that makes two fundamental assumptions, namely that every mental state is noisy and that every action requires a decision. The most widely studied version is known as general recognition theory (GRT). General recognition theory assumes that the percept on each trial can be modeled as a random sample from a multivariate probability distribution defined over the perceptual space. Decision bounds divide this space into regions that are each associated with a response alternative. General recognition theory rigorously defines and tests a number of important perceptual and cognitive conditions, including perceptual and decisional separability and perceptual independence. General recognition theory has been used to analyze data from identification experiments in two ways: (1) fitting and comparing models that make different assumptions about perceptual and decisional processing, and (2) testing assumptions by computing summary statistics and checking whether these satisfy certain conditions. Much has been learned recently about the neural networks that mediate the perceptual and decisional processing modeled by GRT, and this knowledge can be used to improve the design of experiments where a GRT analysis is anticipated.

scholarly journals Sensitivity vs. Awareness Curve: A Novel Model-Based Analysis to Uncover the Processes Underlying Nonconscious Perception

2019 ◽  
Author(s):  
Ali Pournaghdali ◽  
Bennett L Schwartz ◽  
Jason Scott Hays ◽  
Fabian Soto
Keyword(s):  
Expression Recognition ◽  
Continuous Flash Suppression ◽  
Model Based ◽  
General Recognition Theory ◽  
Recognition Theory ◽  
Conscious Processing ◽  
General Recognition ◽  
Nonconscious Perception ◽  
Novel Model ◽  
Model Based Analysis

In this study, we present a novel model-based analysis of the association between awareness and perceptual processing based on a multidimensional version of signal detection theory (general recognition theory, or GRT). The analysis fits a GRT model to behavioral data and uses the estimated model to construct a sensitivity vs. awareness (SvA) curve, representing sensitivity in the discrimination task at each value of relative likelihood of awareness. This approach treats awareness as a continuum rather than a dichotomy, but also provides an objective benchmark for low likelihood of awareness. In two experiments, we assessed nonconscious facial expression recognition using SvA curves in a condition in which emotional faces (fearful vs. neutral) were rendered invisible using continuous flash suppression (CFS) for 500 and 700 milliseconds. We predicted and found sub-conscious processing of face emotion, in the form of higher than chance-level sensitivity in the area of low likelihood of awareness.

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