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 modelling
3 Cognitive modelling
4 Cognitive modelling
5 Cognitive modelling
6 The development of concepts
7 Learning word meanings
8 Ambiguous words
9 Compositionality and word meaning
10 Common-sense reasoning
Cognitive Modelling
Project – construct a model of adjectivenoun combination
red apple
fake gun
heavy baby / heavy elephant
Cognitive Modelling
Learns by training over and over
Heavy baby
NODES:
Distributed
nodes = 4
3
inputs = 6
.7 .3 .4 .6
outputs = 2
.5 .9 .4 .6
output nodes are 1-4
.6 .5 .2 .2
CONNECTIONS:
1-4 from i1 – i6
Heavy
Baby
Distributed
3
.2 .3 .7 .2 .4 .6
.2 .3 .5 .8 .4 .6
.2 .3 .6 .4 .2 .2
<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 simple
e.g. Role of existing background knowledge
Learning word meanings
Gavagai
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, VP
NP  determiner, noun
VP  verb, NP
determiner: {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
Clinton is impeached by Congress
Charlie impeaches a shoe
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?
Meteo (1977)
English  French
Use existing materials (style sheets)
Translators involved
quality?
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
Interdisciplinary
Psychology, linguistics, philosophy, computer
science, brain sciences, anthropology, ….
Use formal / explicit models
Computational 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