Monthly Archives: September 2015

The Mixed Instrumental Controller

mic_MG_5849This is more or less a continuation of the previous post based on Andy Clark’s “Embodied Prediction,” in T. Metzinger & J. M. Windt (Eds). Open MIND: 7(T). Frankfurt am Main: MIND Group (2015).   It further weighs in on the issue of changing strategies or changing weights (see post Revisiting Swiss Army Knife or Adaptive Tool Box). Clark has brought to my attention the terms model free and model based which seem to roughly equate to intuition/system 1 and analysis/system 2 respectively. With this translation, I am helped in trying to tie this into ideas like cognitive niches and parallel constraint satisfaction. Clark in a footnote:

Current thinking about switching between model-free and model based strategies places them squarely in the context of hierarchical inference, through the use of “Bayesian parameter averaging”. This essentially associates model-free schemes with simpler (less complex) lower levels of the hierarchy that may, at times, need to be contextualized
by (more complex) higher levels.

As humans, we have been able to use language, our social skills, and our understanding of hierarchy to extend our cognition.  Multiplication of large numbers is an example. We cannot remember enough numbers in our heads so we created a way to do any multiplication on paper or its equivalent if we can learn our multiplication tables. Clark cites the example of the way that learning to perform mental arithmetic has been scaffolded, in some cultures, by the deliberate use of an abacus. Experience with patterns thus made available helps to install appreciation of many complex arithmetical operations and relations. We structure (and repeatedly re-structure) our physical and social environments in ways that make available new knowledge and skills. Prediction-hungry brains, exposed in the course of embodied action to novel patterns of sensory stimulation, may thus acquire forms of knowledge that were genuinely out-of reach prior to such physical-manipulation-based re-tuning of the generative model. Action and perception thus work together to reduce prediction error against the more slowly evolving backdrop of a culturally distributed process that spawns a succession of designed environments whose impact on the development and unfolding of human thought and reason can hardly be overestimated. Continue reading

Embodied(Grounded) prediction(cognition)


clark514DJ8Bec6L._SX329_BO1,204,203,200_This post is based on a paper by Andy Clark: “Embodied Prediction,” in T. Metzinger & J. M. Windt (Eds). Open MIND: 7(T). Frankfurt am Main: MIND Group (2015). Andy Clark is a philosopher at the University of Edinburgh whose tastes trend toward the wild shirt. He is a very well educated philosopher in the brain sciences and a good teacher. The paper seems to put forward some major ideas for decision making even though that is not its focus. Hammond’s idea of the Cognitive Continuum is well accommodated. It also seems quite compatible with Parallel Constraint Satisfaction, but leaves room for Fast and Frugal Heuristics. It seems to provide a way to merge Parallel Constraint Satisfaction and Cognitive Niches. I do not really understand PCS well enough, but it seems potentially to add hierarchy to PCS and make it into a generative model that can introduce fresh constraint satisfaction variables and constraints as new components. If you have not read the post Prediction Machine, you should because the current post skips much background. It is also difficult to distinguish Embodied Prediction and Grounded Cognition. There are likely to be posts that follow on the same general topic.

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