Locally Bayesian Learning
John K. Kruschke · eScholarship (California Digital Library) · 2006
This article is concerned with trial-by-trial, online learning of cue-outcome mappings.In models structured as successions of component functions, an external target can be backpropagated such that the lower layer's target is the input to the higher layer that maximizes the probability of the higher layer's target.Each layer then does locally Bayesian learning.The resulting parameter updating is not globally Bayesian, but can better capture human behavior.The approach is implemented for an associative learning model that first maps inputs to attentionally filtered inputs, and then maps attentionally filtered inputs to outputs.The model is applied to the humanlearning phenomenon called highlighting, which is challenging to other extant Bayesian models, including the rational model of Anderson, the Kalman filter model of Dayan and Kakade et al., the noisy-OR model of Tenenbaum and Griffiths et al., and the sigmoid-belief networks of Courville et al.Further details and applications are provided by Kruschke (in press); the present article reports new simulations of the Kalman filter and rational model. Cognition Modeled as a Succession of TransformationsCognitive models are often conceived to be successions of transformations from an input representation, through various internal representations, to an output or response representation.Each transformation is a formal operation, typically having various parameter values that are tuned by experience.A well-know example is Marr's (1982) modeling of vision as a succession from a representation of image intensity to a "primal sketch" to a "2 1 2 -D sketch" to a 3-D model representation.