Monthly Archives: February 2015

Revisiting Swiss Army Knife or Adaptive Tool Box

IMG_0494This post is based on a paper: “What is adaptive about adaptive decision making? A parallel constraint satisfaction account,” that was written by Andreas Glöckner, Benjamin E. Hilbig, and Marc Jekel and appeared in Cognition 133 (2014) 641–666. The paper is quite similar to that discussed in the post Swiss Army Knife or Adaptive Tool Box. However, it reflects an updated model that they call the PCS-DM model (parallel constraint satisfaction-decision making). From what I can tell this model attempts to address past weaknesses by describing the network structure more fully and does this at least partially by setting up a one-free parameter implementation which can accommodate individual differences and differences between tasks.

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Prediction Error Minimization and Implications


slowimages This is the final of three posts on this subject. It reflects the work of Jakob Hohwy as referenced in the post Explaining Away and an interview in connection with his book: The Predictive Mind.

An interesting example of the hierarchical predictive coding model is binocular rivalry. Binocular rivalry is a form of visual experience that occurs when, using a special experimental set-up, each eye is presented (simultaneously) with a different visual stimulus. Thus, the right eye might be presented with an image of a house, while the left receives an image of a face. Under these albeit artificial conditions, subjective experience unfolds in a surprising, “bi-stable” manner. Instead of visually experiencing a confusing all-points merger of house and face information, subjects report a kind of perceptual alternation between seeing the house and seeing the face.

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Explaining Away

explainingawayindexThis is the second of three posts about the brain having a singular purpose of prediction error minimization. PEM literature has many contributors. Karl Friston is probably the strongest idea man, but Andy Clark and Jakob Hohwy are more understandable. Hohwy’s papers include:  Hohwy, J. (2015). “The Neural Organ Explains the Mind”. In T. Metzinger & J. M. Windt (Eds). Open MIND: 19(T). Frankfurt am Main: MIND Group. Hohwy, J., Roepstorff, A., & Friston, K.(2008). “Predictive coding explains binocular rivalry: an epistemological review.” Cognition 108, 687-701.  Hohwy, J. (2012). “Attention and conscious perception in the hypothesis testing brain.” Frontiers in Psychology/Consciousness Research, April 2012, Volume 3, Article 96. Paton, B., Skewes, J., Firth, C., & Hohwy, J(2013). “Skull-bound perception and precision optimization through culture.” Commentary in Behavioral and Brain Sciences (2013) 36:3, p 42.

Both Clark and Hohwy use “explaining away” to illustrate the concept of cancelling out sensory prediction error. Perception thus involves “explaining away” the driving (incoming) sensory signal by matching it with a cascade of predictions pitched at a variety of spatial and temporal scales. These predictions reflect what the system already knows about the world (including the
body) and the uncertainties associated with its own processing. What we perceive depends heavily upon the set of priors that the brain brings to bear in its best attempt to predict the current sensory signal.

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The Prediction Machine

prDSCN1739-thomson-tide-machineThis post is derived from the paper, “Whatever next? Predictive brains, situated agents, and the future of cognitive science,” Behavioral and Brain Sciences (2013) 36:3, written by Andy Clark. I stumbled upon this paper and its commentary several weeks ago and have tried to figure out what to do with it. That has led me to other papers. In the next three posts, I will try to give the high points of this idea of PEM, prediction error minimization. It provides an overall background that is compatible with Parallel Constraint Satisfaction.

Clark suggests that the brain’s jobs are minimizing prediction error, selective sampling of sensory data, optimizing expected precisions, and minimizing complexity of internal models. To accomplish these tasks, the brain has evolved into a bundle of cells that support perception and action by attempting to match incoming sensory inputs with top-down expectations–predictions. This is done by using a hierarchical model that minimizes prediction error within a bidirectional cascade of cortical processing. This model maps on to perception, action, attention, and model selection, respectively (and dare I say judgment and decision making).

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MM-ML Strategy Classification

mmmlrh4002This post is derived from “Chapter 5, Multiple Measure Strategy Classification-Outcomes, Decision Times, and Confidence Ratings” authored by Andreas Glockner from Foundations for Tracing Intuition– Challenges and Methods, edited by Andreas Glockner and Cilia Witteman 2010 Psychology Press NY. It shines a little light on how intuition experiments seeking to answer if a person is using a Take the Best strategy or a Parallel Constraint Satisfaction strategy, etc are actually done. It is written more understandably than a typical paper for a journal. It will hopefully give more meaning to the letters MM-ML.

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