Thursday, May 16, 2013

A Figure of Human Cognitive Functions

In this and the subsequent postings, I'm going to present a simplified and organized view of major human cognitive functions with an illustration (mind map -- the figure below) to serve for AGI designs.  (The branches in the map indicates cognitive functions and branching indicates their specialization.)

In this page, I'll explain the path from sensory input to motor output via pattern recognizers and association (the blue arrows 1a & 1b).

1a Temporal Hierarchical Categorizers

Temporal Hierarchical Categorizers (THC, hereafter) are pattern recognizers that classify (categorize) sensory input.
  • THC’s Categorizing function is acquired through supervised or unsupervised learning, so that a Categorizer may acquire categories of sensory input by itself.  In last decades, various (computational) neural networks have been proposed to explain automatic categorizing of input data, suggesting that biological neural networks may have such a function.
  • THC is Hierarchical.  While many of proposed neural network models were hierarchical, a new kind of hierarchical models called 'Deep Learning' has made successes in categorization tasks in recent years.  The importance of hierarchy in neo-cortical modeling has been also emphasized in the books by J. Hawkins and R. Kurzweil (the term THC, of course, echoes Hawkins’s Temporal Hierarchy Memory).  For pattern recognition in mammalian cerebra is carried out by the hierarchy of neo-cortices, the Categorizer is thought to be hierarchical.
  • THC is Temporal; for animals or robots to interact with the environment, the patterns they have to deal with are time series.  If a pattern recognizer is to be modeled as a neural network (model), a recurrent network would do the job.  The figure's reference to the diencephalon suggests a circuitry involving the part of the brain constitutes recurrent networks.

1b Association

Now let’s look at the Association part of the figure.  One of the most elementary associations would be that from sensory output to motor output, which associates a sensory pattern (or category) with motor pattern (or category).  Association is not pattern recognition but involves the recollection of patterns within a modality or between modalities, where the patterns are the ones recognized by pattern recognizers.  This means association appropriates the function of pattern recognizers.
The arrow titled ‘Associating Features’ from Association to THC in the figure suggests that there is association of lower feature patterns with higher patterns (e.g., when we imagine a flower, we recall the color and shape).  In real brains, this function would be assumed by massive efferent connections.

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