Hierarchical Nonlinear Models in Cognition and Perception
Abstract
Simple but nonlinear models are relatively common in modern perception and cognition. Popular examples include the theory of signal detection, the process dissociation model, and the three-parameter power and exponential models of skill acquisition. In typical applications, unintended variability is introduced by the selection of participants and/or items. I show how this real-world constraint results in asymptotically biased estimation which, in turn, compromises inference. My colleagues and I have been advocating hierarchical Bayesian models as a principled means of analyzing nonlinear models in real-world contexts. In this talk, I demonstrate the problems of conventional analyses and the potentially dramatic benefits of hierarchical models in three domains: dissociation of memory processes, subliminal/near-liminal priming, and lexical access
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