cognitive science introduction. overview aims and learning outcomes assessment programme cognitive...

Cognitive Science

Introduction

Overview

• Aims and learning outcomes• Assessment• Programme

• Cognitive science is interdisciplinary• Cognitive science uses formal models

Beware• This strategy might not succeed (!)• Fashion can influence the perception of research

Aims

• To introduce interdisciplinary approaches to the study of higher cognitive processes

• To familiarise you with computational and other formal modelling

• To illustrate the application of modelling to cognitive processes

Learning outcomes

• To gain direct experience of computational and other formal modelling techniques.

• To integrate material across areas within psychology and across traditional subject disciplines.

• To compare and critically evaluate formal techniques in relation to empirical findings.

• To tackle key theoretical problems in cognitive science, particularly problems linked by the theme of common sense reasoning.

Assessment

• Two hour examination in June, which counts for two thirds of the mark.

• Three pieces of coursework (counting for 4%, 4%, and 25% respectively of the course mark)

• Coursework assesses the first and, to a lesser degree, the third learning objectives.

• The exam will assess learning objectives two, three and four.

Coursework

AW 1 - Connectionist modelling 1 (4%)

AW 2 - Connectionist modelling 2 (4%)

Modelling project (25%)

Programme

1 Introduction - why cognitive science?2 Cognitive modelling3 Cognitive modelling4 Cognitive modelling5 Cognitive modelling6 The development of concepts7 Learning word meanings8 Ambiguous words9 Compositionality and word meaning10 Common-sense reasoning

Cognitive Modelling

Project – construct a model of adjective-noun combination

red apple

fake gun

heavy baby / heavy elephant

Cognitive Modelling

Heavy baby

Heavy Baby

Learns by training over and over

Distributed

3

.2 .3 .7 .2 .4 .6

.2 .3 .5 .8 .4 .6

.2 .3 .6 .4 .2 .2

Distributed

3

.7 .3 .4 .6

.5 .9 .4 .6

.6 .5 .2 .2

NODES:

nodes = 4

inputs = 6

outputs = 2

output nodes are 1-4

CONNECTIONS:

1-4 from i1 – i6

<weight feature>

The development of concepts

What do we mean concept?

Why is concept learning tricky to understand?

Connectionist nets as a simple model of concept learning

Some features of natural concept learning that make the picture less simplee.g. Role of existing background knowledge

Gavagai

Learning word meanings

Ambiguity and vagueness

Complex links between words and concepts

Bank

Newspaper

To paint

Combining concepts

Compositionality is key to language

red apple, red brick, red mist

Watergate, blood gate, Stargate

Commonsense reasoning

Which information is relevant to drawing a conclusion?

Which facts are affected by an event?

• Yale shooting problem

• Property inheritance

Tweety is a bird. So, Tweety can fly?

A little history – the Cognitive Revolution

Skinner (1957) Children learn words (language) through operant

conditioning - stimulus controls response

Chomsky's (1959) review of Verbal Behavior(link on course web pages)

"Dutch" - what stimulus? proliferate "stimuli”

but role of attention etc. mind'Creativity' of language compositionality

Technical concepts of Skinner's behaviorism (stimulus, reinforcement, operant etc.) were used non-technically in "Verbal Behavior“

Eg. the artist is reinforced by the effects his work may have on others

… but the artist's (often) not there when these effects occur. It's not like reinforcement in a Skinner box.

"I now believe that mind is something more than a four letter Anglo-Saxon word - human minds exist and it is our job as psychologists to study them."

Miller (1962) in American Psychologist, 17, p. 761

Nb Piaget, even Freud, were always cognitively oriented

Chomsky (1957; 1965)Transformational Generative Grammar

Account for syntactic facts (linguistics)

e.g. active and passive have same meaning

Judge facts using 'intuitions' (psychology)

the resulting grammars are related to something people know

(linguistic competence)

A small transformational generative grammar S NP, VPNP determiner, nounVP verb, NPdeterminer: {the, a} noun: {boy, dog} verb: {eat, kick, bite, occur}

Passive transformation (simplified):NP1, V, NP2 NP2, BE, V, EN, by, NP1

Captures the fact that selection restrictions match

Congress impeaches Clinton Charlie impeaches a shoeClinton is impeached by Congress A shoe is impeached…

Congress impeaches Clinton

NP1 V NP2

Rule

NP1, V, NP2 NP2, BE, V, EN, by, NP1

Clinton is impeached by Congress

More history – early machine translation

Weaver (1949) memorandum

Georgetown (1954-66)250 words & 6 rules at start

Alpac Commission (1966)speed? cost? quality?

Meteo (1977)English FrenchUse existing materials (style sheets)Translators involved

Fashion and the life cycle of (some) AI projects

Oblivion, fading Rebirth Excitement Claims More excitement Wild claims Unmet expectations

Fading, oblivion.

Cognitive science now

"higher" cognitive functions; processes & representations

InterdisciplinaryPsychology, linguistics, philosophy, computer science, brain sciences, anthropology, ….

Use formal / explicit modelsComputational metaphor

strong v. weak

The original question "Can machines think?“ I believe to be too meaningless to deserve discussion.

Alan Turing

www.warwick.ac.uk/~psrex/cogsci.html

The end