Prediction and Metacognition

A case is made for the following claims. Higher animals have evolved the ability to make predictions because it increases their chance of survival. The basic steps in the prediction process are - registration of the internal spatial model with the world, initialization of salient objects (including actors) within the world model, projection of actor behaviors and movements through time, and assessment of results against drives and goals. Predictions are compared with situation estimates, and world models and previous estimates are revised based on this discrepancy. Better world models and situation estimates, in turn, lead to better initialization for the next prediction. This bootstrapping cycle improves both prediction and situation estimation. Thus, prediction can act as an evolutionary driver for more complex world models, higher-level behaviors, and greater sensory discrimination. Higher-level behaviors, with built-in contingencies, are time-consuming to build and resistant to change. However, a self-adaptive system must be able to change its higherlevel behaviors. When prediction is applied to one’s own thinking a biofeedback loop is set up that can improve higher-level behaviors. It is observed that many meditation techniques are metacognitive processes that seek to setup this type of biofeedback loop.

[1]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[2]  V. Braitenberg.  Vehicles, Experiments in Synthetic Psychology , 1984 .

[3]  E. Tulving.  Memory and consciousness. , 1985 .

[4]  James L. Eilbert.  Biologically-Inspired Approach to Recognizing Dangerous Objects , 2009, AAAI Fall Symposium: Biologically Inspired Cognitive Architectures.

[5]  C. Gallistel.  The organization of learning , 1990 .

[6]  J. P. Huston,et al.  The case for episodic memory in animals , 2006, Neuroscience & Biobehavioral Reviews.

[7]  James Hicinbothom,et al.  A Cognitive Framework for Modeling Mental Space Construction and Switching During Situation Assessment , 2006, FLAIRS.

[8]  Richards J. Heuer,et al.  Psychology of Intelligence Analysis , 1999 .

[9]  Ennio Mingolla,et al.  Neural dynamics of perceptual grouping: Textures, boundaries, and emergent segmentations , 1985 .

[10]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[11]  M. W. Brown,et al.  Episodic memory, amnesia, and the hippocampal–anterior thalamic axis , 1999, Behavioral and Brain Sciences.

[12]  Nuttapong Chentanez,et al.  Intrinsically Motivated Learning of Hierarchical Collections of Skills , 2004 .

[13]  S. Grossberg,et al.  Texture segregation by visual cortex: Perceptual grouping, attention, and learning , 2007, Vision Research.

[14]  M. Mehta.  Human spatial memory , 2010 .

[15]  Robin G. Morris,et al.  Neurocognitive Components of Spatial Memory , 2004 .

[16]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[17]  C. Gallistel,et al.  The learning curve: implications of a quantitative analysis. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Stephen Grossberg,et al.  Space, time and learning in the hippocampus: How fine spatial and temporal scales are expanded into population codes for behavioral control , 2007, Neural Networks.

[19]  G. DeAngelis,et al.  Organization of Disparity-Selective Neurons in Macaque Area MT , 1999, The Journal of Neuroscience.

[20]  S. Grossberg,et al.  View-invariant object category learning, recognition, and search: How spatial and object attention are coordinated using surface-based attentional shrouds , 2009, Cognitive Psychology.