Understanding Human Cognition through Experimental and Computational Methods

Understanding Human Cognition through Experimental and Computational Methods Jay Mc. Clelland Symbolic Systems 100 Spring, 2011

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

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

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.

B

M

S

F

X

T

V

N

Recall

BMSFXTVN

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 third. •

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?

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

The Perceptron

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

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.

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?

Ubiquity of the Constraint Satisfaction Problem • 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…

Graded and variable nature of neuronal responses

Lateral Inhibition in Eye of Limulus (Horseshoe Crab)

Neural Network Models of Cognition: The Interactive Activation Model

Newer Directions • Cognitive Neuroscience: – Using measurements of human brain activity to learn more about mental processing • Reasoning with uncertain information: – Probabilistic models of cognition • Cognition as an embodied process, tied to experience and action.