Introduction and modeling of human consciousness

4. Computational Aspects of
Cognition and Consciousness in Intelligent Devices

5. • Introduction: Models of Consciousness and Cognition
• Cortical Correlates of Cognition & Consciousness
• Population Models of Cognitive Functions
• Intentional Dynamic Systems
• Simulating Computational Intelligence
• Conclusions

6. Models of Cognition & Consciousness

7. Humans are not passive receivers of perceptual information.
They actively search for sensory input.

8. Physical Symbol System
“A physical symbol system has the
necessary and sufficient means for general intelligent action.”

9. Physical Symbol System
Inflexible
Hard to scale up
Preset, fixed

10. Situated Intelligence Approach
”Intelligence is defined in the context of an environment.”

11. Global Workspace Theory
The Theater of Consciousness

12. Connectionism
Mental phenomena can be
described by interconnected networks and uniform units.

13. Cortical Correlates of Cognition & Consciousness

14. Sudden changes in cortical neural activity have been identified.

15. Dynamic Systems Theory

16. Population Models of Cognitive Functions

17. K Sets
Mesoscopic models that represent an intermediate level between
microscopic neurons and macroscopic brain structures.

18. K0 Sets
A non-interactive collection of neurons
And the module for K sets.

19. K1 Sets
Populations with mutual excitation or mutual inhibition.

20. K2 Sets
Interactions among excitatory and inhibitory populations.

21. K3 Sets
The olfactory system, learning, and pattern classification.

22. K4 Sets
The limbic system and navigation.

23. K5 Sets
The neocortex and cognition.

24. Intentional Dynamic Systems

25. Form hypotheses about
expected states one may face in the future.

26. Express the hypotheses as
meaningful goals to be aimed at.

27. Formulate a plan of action to achieve the goals.

28. Inform the sensory and perceptual apparatus
about the expected future input.

29. Act into the environment in accordance with
the action to achieve the formulated goals.

30. Manipulate the sensors,
adjust their properties and orientations
to receive the sensory data.

31. Generalize and categorize the sensory data
and combine them into multisensory percepts.

32. Verify and test the hypotheses,
and update and learn the brain model
to correspond better to the perceived data.

33. Learning from Memories
Hebbian Correlation Learning
Habituation

34. Habituation
The process in which
one decreases or ceases to respond to a stimulus
after repeated presentations.

35. Hebbian Correlation Learning
Cells that fire together, wire together.

36. Simulating Computational Intelligence

37. Orientation toward Preset Goal
Distance to Obstacles

39. Conclusions