Some Aspects of the History of Cognitive Science

1. Some Aspects of the History of Cognitive Science Jay McClelland Symbolic Systems 100 Spring, 2010

2. Decartes’ Legacy • Mechanistic approach to sensation and action • Divine inspiration creates mind • This leads to three dissociations: • Mind / Brain • Higher Cognitive Functions / Sensory-motor systems • Human / Animal • Today we accept that the mind arises from brain activity. • Some cognitive scientists ground their models of mind in the workings of the brain, while others continue to view cognition abstractly.

3. Early History of the Study of Human Mental Processes • Introspectionism (Wundt, Titchener) • Thought as conscious content, but two problems: • Suggestibility • Gaps • Freud suggests that mental processes are not all conscious • Behaviorists (Watson, Skinner) eschew talk of mental processes altogether

4. Can Experiments Teach Us About the Contents of the Mind? • Conrad: Verbal coding in short-term memory • Sachs: Representation of meaning in long-term memory

5. Conrad’s Experiment • You will see a series of letters. • Try to remember them so that, when you see the word recall, you can write them down in the correct order. • There will be six letters, followed by a brief delay, then the word ‘Recall’ will appear. • After you see the word recall, write down the letters in order, starting with the first letter and then proceeding through the list.

6. .

7. B

8. M

9. S

10. F

11. V

12. N

13. .

14. .

15. .

16. Recall

17. B M F S V N

18. Sachs’ Experiment • Participants heard a story containing a sentence such as: • He sent Galileo, the great Italian Scientist, a letter about it. • Either immediately, or after reading a few more sentences, the participants were asked which of the following sentences they had heard: • He sent Galileo, the great Italian Scientist, a letter about it. • He sent a letter about it to Galileo, the great Italian Scientist. • Galileo, the great Italian Scientist, sent him a letter about it. • When tested immediately, nearly all participants chose the correct sentence. • After a delay, many participants chose the second sentence, but no one chose the third.

19. A Question: • What sort of a mechanism should we use to capture the processes that underlie human thought? • A mechanism like the brain? • Or a mechanism like a computer?

21. Output fromneuron j wij Output Neuron i Threshold Input The McCulloch-Pitts Neuron 1 0 McCulloch-Pitts neurons can be used to compute logical functions,such as A-AND-B, A-OR-B, A-AND-NOT-B, etc

22. The Perceptron

23. Problems for the Perceptron • Depends crucially on the φi • Some functions require an exponential number of φi • No one figured out how to train the weights coming in to the φi • all the possible φi that might ever be needed had to be provided in advance

24. The Rise of Symbolic Computation • Mathematics and logic grew up around the use of symbols: • Marks on paper that stand for things. • Computer programs that do math and logic make use of symbols too. • Rules of mathematics and logic can be expressed in terms of statements about symbols. • ‘If p then q’ and ‘p’ implies ‘q’ • So symbolic models seemed like they might be effective ways of using computers to model human reasoning.

25. But AI Didn’t Live Up to It’s Promise Either • Computers could do math and logic, but they couldn’t: • Recognize objects • Recognize speech • Understand sentences • Retrieve relevant information from memory • Was there something wrong with the specific models or languages people were using or was there something wrong with the whole approach?

27. Ubiquity of the Constraint SatisfactionProblem • In sentence processing • I saw the grand canyon flying to New York • I saw the sheep grazing in the field • In comprehension • Margie was sitting on the front steps when she heard the familiar jingle of the “Good Humor” truck. She remembered her birthday money and ran into the house. • In reaching, grasping, typing…

28. Graded and variable nature of neuronal responses

29. Lateral Inhibition in Eye of Limulus (Horseshoe Crab)

30. Neural Network Models of Cognition: The Interactive Activation Model

31. Newer Directions • Reasoning with uncertain information: • Probabilistic models of cognition • Cognition as an embodied process, tied to experience and action.