Understanding Cognitive Architecture: Flow Charts, Modularity, and the Mind's Mechanisms

1. Chapter Three of Green: Intro to Cogsci Spring 2005

2. Review: Boxes and Flows • Needed with Crane • Flow Charts: Used to express procedure and algorithms; boxes represent operations or decisions and arrows represent flow of control. “How to do it” • Box/arrow diagram: boxes represent cognitive processes and arrows represent flow of information. “How it happens”

3. Flow charts • How to do it • Example: Recipe NO Is oven on to 350? Turn on to 350 Yes Open Package

4. Box/arrows • How it is done, from input to output Proximal Stimuli Perception of distal stimuli

5. Review: Attractions of Turing • Non-mental explanation of mental: a Turing machine does not have to understand meanings in order to perform its basic operations. • Retains compositionality, systematicity and so productivity • Compositionality of X: meaning of X determined by parts and rules of compostion. • Examples: Grass is green. Blood is red. • Compositionality seems to give us sytematicity: can understand same rules, same elements combined differently. • Example: Grass is red. Blood is green. • Productivity: potential infinite number of X’s can be understood.

6. Architecture and modularity • What is cognitive architecture and how does it differ from the brain’s architecture?

7. Features of a Module • Domain specificity • Information encapsulation • Mandatory • Speedy (because of first three) • Shallow output representations • Same ontogency across species • Characteristic and isolatable breakdowns • Associated with a fixed and sometimes localized neural architecture Note: 6-8 & innately prespecified

8. Modularity in practice • SAQ 3.1 • 3.2 • 3.3 • 3.4

9. Other Issues re modularity of lang system • Domain specificity • McGurk effect (p. 66) • Encapsulation • Parsing • Word recognition

10. Parsing • “When you are happy, visiting relatives…” [people, activity] • When you are happy, visiting relatives will enjoy your home. • When you are happy, visiting relatives can be a good idea. • Two views compatible with Fodorean modularity: • All interpretations present and then selected • Done in fixed order with no contextual influence on order • Why is contextual influence important? • According to Green, evidence favors encapsulation

11. Word Recognition: • A possible problem for Fodorean modularity: • Example: The player went to the coach. • Responding quicker = primed • Priming: Process faster/easier because of earlier process. • Fodor: this is dumb association, not informationally informed.

12. The Frame Problem • What is it? • Why is it concerned with “central systems” • Humans just do update their beliefs reasonably successfully. • See Crane on relevance and Dreyfus

13. How modular should the mind be? • Marr and the principle of modular design • Fodorean arguments for modularity: • We need some systems to be fast, automatic, etc • Fodor’s teleological argument for non-modularity: is it evolutionarily sensible?

14. Piaget • Epigenetic constructivism • Self-organizing system structured and shaped by its environment • 3 basic operations and interactions with the environment explain adult cognition

15. Karmiloff-Smith • Innate dispositions to attend to particular stimuli and some innate skeletal knowledge structures. • Thinks information encapsulation is acquired, not inborn • Questions poverty of stimulus argument: environments are more structured than we thought. • Infant mind is very plastic.

16. Connectionism: Advantages?? • Neurally more realistic? • Learns in a way that allows generalizing – e.g., pattern learning/voice recognition • Graceful degredation: unlike Turing machines

17. Pattern associators • Learning rule • Activation function • Which is the Hebb rule? • Instead of “a little learning is a dangerous thing,” we can have “a lot of learning is a dangerous thing.”

18. Delta Rule • Two advantages over Hebb Rule. • What are they? • How they operate • What they operate on • Why the Perceptron?

19. All or nothing rule

22. What have you Learned?