Research not for publishing papers, but for fun, for satisfying curiosity, and for revealing the truth.

This blog reports latest progresses in
(1) Signal Processing and Machine Learning for Biomedicine, Neuroimaging, Wearable Healthcare, and Smart-Home
(2) Sparse Signal Recovery and Compressed Sensing of Signals by Exploiting Spatiotemporal Structures
(3) My Works

Friday, April 13, 2012

Paper Club: Whatever Next? Predictive Brains, Situated agents and the future of cognitive science

Today 11 am at Ed's lab (3509 Mandler) we will discuss a very interesting and comprehensive paper:

Andy Clark (2012) "Whatever Next? Predictive Brains, Situated agents and the future of cognitive science", to appear in Behavioral and Brain Sciences

Here is the download link:

Abstract: Brains, it has recently been argued, are essentially prediction machines. They are bundles of cells that support perception and action by constantly attempting to match incoming sensory inputs with top-down expectations or predictions. This is achieved using a hierarchical generative model that aims to minimize prediction error within a bidirectional cascade of cortical processing. Such accounts offer a unifying model of perception and action, illuminate the functional role of attention, and may neatly capture the special contribution of cortical processing to adaptive success. The paper critically examines this ‘hierarchical prediction machine’ approach, concluding that it offers the best clue yet to the shape of a unified science of mind and action. Sections 1 and 2 lay out the key elements and implications of the approach. Section 3 explores a variety of pitfalls and challenges, spanning the evidential, the methodological, and the more properly conceptual. The paper ends (sections 4 and 5) by asking how such approaches might impact our more general vision of mind, experience, and agency.

My comment: Although the paper does not involve any computational tools or specific computational frameworks, it in fact points out the incapability of ICA and sparse coding in the computational vision field. Also, I further understand that why deep belief networks and other hierarchical Bayesian models are so promising in the computational vision field.

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