102 5 A Generic Architecture of Human-Like Cognition
touch and smell (the latter being better modeled as something like an asymmetric Hopfield net, prone to frequent chaotic dynamics [LLW~05]) — these may also cross-connect with each other and with the more hierarchical perceptual subnetworks. Of course the suggested architecture could inclide any number of sensory modalities; the diagram is restricted to four just for simplicity.
The self-organized patterns in the upper layers of perceptual hierarchies may become quite complex and may develop advanced cognitive capabilities like episodic memory, reasoning, lan- guage learning, etc. A pure deep learning approach to intelligence argues that all the aspects of intelligence emerge from this kind of dynamics (among perceptual, action and reinforcement hierarchies). Our own view is that the heterogeneity of human brain architecture argues against this perspective, and that deep learning systems are probably better as models of perception and action than of general cognition. However, the integrative diagram is not committed to our perspective on this — a deep-learning theorist could accept the integrative diagram, but argue that all the other portions besides the perceptual, action and reinforcement hierarchies should be viewed as descriptions of phenomena that emerge in these hierarchies due to their interaction.
ACTION AND REINFORCEMENT SUBSYSTEM
SENSORY-MOTOR MEMORY 3 T MOTIVATION, HIGHER LEVEL - ACTION SELECTION
MOTOR PLANNING
—— | ——1
|} {| HIERARCHY \ a HIERARCHY /
REINFORCEM EN T HIERARCHY
HIERARCHY
Fig. 5.6: Architecture for Action and Reinforcement
Figure 5.6 shows an action subsystem and a reinforcement subsystem, parallel to the per- ception subsystem. Two action hierarchies, one for an arm and one for a leg, are shown for
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