Wednesday, July 04, 2012

Making Predictive Coding More Predictive, More Enactive



Making Predictive Coding More Predictive, More Enactive
Ron Chrisley, Sackler Centre for Consciousness Science & Dept of Informatics, University of Sussex, UK
Presented at the 16th annual meeting of the Association for the Scientific Study of Consciousness
Corn Exchange, Brighton, July 3rd, 16:30-18:30: Concurrent Session 2.

Abstract:
Predictive coding (PC) architectures (e.g., Dayan, Hinton, Neal & Zemel, 1995; Rao & Ballard, 1999) have been recently proposed to explain various aspects of consciousness, including those involved in binocular rivalry (Hohwy, Roepstorff & Friston, 2008), and presence (“the subjective sense of reality of the world and of the self within the world”) (Seth, Suzuki & Critchley, 2011). It is argued that the potential of PC explanations of consciousness has been obscured by overemphasis of a number of features that are typically held to be essential to the PC approach, but which in fact are not central, and may be detrimental, to PC explanations of consciousness. For example: 1) the components of PC architectures that do the work of explaining consciousness can be de-coupled from hypotheses concerning (e.g. Bayesian) optimality; 2) the structure of the models employed by PC architectures is typically not predictive in any direct sense, being instead a representation of the causes of sensory input (Hohwy, Roepstorff & Friston, 2008); 3) these models are typically disconnected from action, accruing the familiar limitations of disembodied accounts (with (Seth, Suzuki & Critchley, 2011) being a notable exception); 4) the winner-take-all promotion of a model to be the content of consciousness can be eliminated, thus enabling PC architectures to accommodate anti-realist or at least more critically realist views of consciousness (Dennett 1991). A more general architecture, Enactive EBA (following (Chrisley & Pathermore, 2007)), is proposed to preserve the strengths of PC architectures, while avoiding the above limitations and suggesting new hypotheses and experiments to test them.

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