Some Aspects of the History of Cognitive Science

Slides:



Advertisements
Similar presentations
IT’S STORY TIME.
Advertisements

Summer 2011 Tuesday, 8/ No supposition seems to me more natural than that there is no process in the brain correlated with associating or with.
Artificial intelligence COS 116, Spring 2012 Adam Finkelstein.
Fostering Algebraic Thinking October 26  December 2  6-hour Assignment after Session 2  January 20 Presented by: Janna Smith
1Neural Networks B 2009 Neural Networks B Lecture 1 Wolfgang Maass
1 12 September, 2000HKU Introduction to Cognitive Science COGN 1001 Schedule –11:40 – 12:30 –Tuesday: K. K. Leung Building, LG 102 –Thursday: K. K. Leung.
New topic Cognition and Ideology Good News: no annotative references! Bad News: An exercise on the Panels in Zimmerman West Wing ( we need to take a field.
CSE 471/598,CBS598 Introduction to Artificial Intelligence Fall 2004
Test Taking Tips How to help yourself with multiple choice and short answer questions for reading selections A. Caldwell.
IT’S STORY TIME IT’S STORY TIME Elements of Fiction Elements of Fiction.
May 19-22,  Become familiar with the Fostering Algebraic Thinking materials.  Examine activities that may be challenging to facilitate. 
Chapter 7 - Memory Psychology McGonigle- College Prep/ Honors.
Speech Comprehension: Decoding meaning from speech.
Artificial Intelligence Introductory Lecture Jennifer J. Burg Department of Mathematics and Computer Science.
The PDP Approach to Understanding the Mind and Brain J. McClelland Cognitive Core Class Lecture March 7, 2011.
The PDP Approach to Understanding the Mind and Brain Jay McClelland Stanford University January 21, 2014.
The Interactive Activation Model. Ubiquity of the Constraint Satisfaction Problem In sentence processing –I saw the grand canyon flying to New York –I.
Awakening from the Cartesian Dream: The PDP Approach to Understanding the Mind and Brain Jay McClelland Stanford University February 7, 2013.
AI History, Philosophical Foundations Part 2. Some highlights from early history of AI Gödel’s theorem: 1930 Turing machines: 1936 McCulloch and Pitts.
Models of Cognitive Processes: Historical Introduction with a Focus on Parallel Distributed Processing Models Psychology 209 Stanford University Jan 7,
1 Introduction to Cognition Cognitive processes are very familiar and taken for granted. –These processes are performed without effort so we perceive them.
Artificial intelligence COS 116, Spring 2010 Adam Finkelstein.
Introduction to Artificial Intelligence (G51IAI) Dr Rong Qu Neural Networks.
Introduction to Machine Learning Kamal Aboul-Hosn Cornell University Chess, Chinese Rooms, and Learning.
Perception, Thought and Language as Graded Constraint Satisfaction Processes Jay McClelland SymSys 100 April 12, 2011.
The Field of Psychology.  Psychologists work in just about every setting you can imagine.  About 1/3 help people with personal problems.  Psychology.
Understanding Human Cognition through Experimental and Computational Methods Jay McClelland Symbolic Systems 100 Spring, 2011.
Approaches to A. I. Thinking like humans Cognitive science Neuron level Neuroanatomical level Mind level Thinking rationally Aristotle, syllogisms Logic.
PDP Class Stanford University Jan 4, 2010
Copyright © 2010, Pearson Education Inc., All rights reserved.  Prepared by Katherine E. L. Norris, Ed.D.  West Chester University of Pennsylvania This.
The PDP Approach to Understanding the Mind and Brain Jay McClelland Stanford University January 21, 2014.
Perception and Thought as Constraint Satisfaction Processes Jay McClelland Symsys 100 April 27, 2010.
Anne Watson Hong Kong  grasp formal structure  think logically in spatial, numerical and symbolic relationships  generalise rapidly and broadly.
COSC 4426 AJ Boulay Julia Johnson Artificial Neural Networks: Introduction to Soft Computing (Textbook)
Part 1: A brief look at the roots of Psychology. A quote… “Psychology has a long past, but a short history.” -Hermann Ebbinghaus What do you think this.
1 ARTIFICIAL INTELLIGENCE Gilles BÉZARD Version 3.16.
CELDT PRACTICE Listening Version B. LISTENING CELDT assesses students’ listening skills in 20 items CELDT divides the listening assessment in three parts.
IT’S STORY TIME.
IT’S STORY TIME.
First Grade.
Course Overview What is AI? What are the Major Challenges?
Can they help us understand the human mind?
Developmental Psychology
Some Aspects of the History of Cognitive Science
Historical Roots of Psychology
group 1 : Alyssa D., Jacob S., Joana D., David N.
Chapter 7 Psychology: Memory.
Grade Eight Heather MacLean
Helping Children Learn
IT’S STORY TIME.
Paper 1 writing – short stories
Can they help us understand the human mind?
Emergence of Semantics from Experience
Over the past fifty years, three main theoretical positions have been advanced to explain language development from infancy through the early school years:
Lesson 2 Programming constructs – Algorithms – Scratch – Variables Intro.
IT’S STORY TIME.
Over the past fifty years, three main theoretical positions have been advanced to explain language development from infancy through the early school years:
The. the of and a to in is you that with.
The of and to in is you that it he for was.
Developmental Psychology
Can they help us understand the human mind?
IT’S STORY TIME.
Over the past fifty years, three main theoretical positions have been advanced to explain language development from infancy through the early school years:
The Stages of Language & Literacy Development
Developmental Psychology
Introduction to Neural Network
Active, dynamic, interactive, system
David Kauchak CS158 – Spring 2019

Human Cognition: Is it more like a computer or a neural net?
Presentation transcript:

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

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.

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 long-term 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

V

N

.

.

.

Recall

B M F S V N

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 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.

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 McCulloch-Pitts Neuron Neuron i Output from neuron j wij 1 Input Threshold Output McCulloch-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 Reasoning with uncertain information: Probabilistic models of cognition Cognition as an embodied process, tied to experience and action.