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Full-Text Articles in Social and Behavioral Sciences
Bayesian Approaches To Assessing Architecture And Stopping Rule, Joseph W. Houpt, Andrew Heathcote, Ami Eidels, J. T. Townsend
Bayesian Approaches To Assessing Architecture And Stopping Rule, Joseph W. Houpt, Andrew Heathcote, Ami Eidels, J. T. Townsend
Psychology Faculty Publications
Much of scientific psychology and cognitive science can be viewed as a search to understand the mechanisms and dynamics of perception, thought and action. Two processing attributes of particular interest to psychologists are the architecture, or temporal relationships between sub-processes of the system, and the stopping rule, which dictates how many of the sub-processes must be completed for the system to finish. The Survivor Interaction Contrast (SIC) is a powerful tool for assessing the architecture and stopping rule of a mental process model. Thus far, statistical analysis of the SIC has been limited to null-hypothesis- significance tests. In this talk …
General Recognition Theory Extended To Include Response Times: Predictions For A Class Of Parallel Systems, James T. Townsend, Joseph W. Houpt, Noah H. Silbert
General Recognition Theory Extended To Include Response Times: Predictions For A Class Of Parallel Systems, James T. Townsend, Joseph W. Houpt, Noah H. Silbert
Psychology Faculty Publications
General Recognition Theory (GRT; Ashby & Townsend, 1986) is a multidimensional theory of classification. Originally developed to study various types of perceptual independence, it has also been widely employed in diverse cognitive venues, such as categorization. The initial theory and applications have been static, that is, lacking a time variable and focusing on patterns of responses, such as confusion matrices. Ashby proposed a parallel, dynamic stochastic version of GRT with application to perceptual independence based on discrete linear systems theory with imposed noise (Ashby, 1989). The current study again focuses on cognitive/perceptual independence within an identification classification paradigm. We extend …