Download Introduction to Cognitive Science

Computational Cognitive
Modelling
COGS 511-Lecture 1
General
Introduction
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Related Readings
From Course Pack
 Cooper, R. Chapter 1: Modelling Cognition
 McClelland (2009). The Place of Modeling in Cognitive
Science.
 References (extra and optional; given for a complete
reference list – not in the course pack)
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Carpenter and Just, Computational Modeling of High-Level
Cognition versus Hypothesis Testing in Sternberg (ed), The
Nature of Cognition, 1999.
Fernandez, J. Explanation by Computer Simulation in
Cognitive Science, Minds and Machines, 13: 269-284, 2003.
Steedman, Chap. 5, of Scarborough and Sternberg (eds).
Morgan, M.S., & Morrison, M. (1999). Models as mediators
(Ed). Cambridge: Cambridge University Press.
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Models
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A representation of something that may be used in place
of the real thing, abstracting away unimportant features
but retaining the essential. (Cooper).
A good model is complete (does not abstract out important
properties) and faithful (does not introduce features that
are not in the original) with respect to its specific purpose.
Helpful for understanding a complex system – cognition for
the case of cognitive science.
Computational cognitive modelling is the development of
computer models of cognitive processes and the use of
such models to simulate and predict human behaviour.
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Models in Philosophy of
Science
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The task of Philosophy of science is:
 Generate reflections on the theoretical and
methodological issues in scientific practice.
Models function in a variety of different ways
within sciences.
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Analog models: Molecules – Billiard-balls,
Mechanical model: DNA molecule - Metal-made helix
model
Scale models: Models in architecture, model airplanes,
etc.
Treated in relation with theory and phenomena.
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Models in Philosophy of Science
(cont.)

Semantic View
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Models are abstract idealized systems which
characterize how the phenomena would have
behaved if the idealized conditions were met
(Suppe, 1989).
Thus, a theory characterizes the model which
represents (certain aspects of) phenomena.
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Models in Philosophy of Science
(cont.)

Morrison and Morgan (1999)

Models are evaluated in response to
four questions:
• How are models constructed?
• What do they represent?
• What role do they have/how do they
function in scientific practices?
• How do we learn from models?
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Models in Philosophy of Science
(cont.)

Their general account based on case
studies in physics, chemistry and economy
proposes that:
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Models are autonomous agents, i.e.
they are only partially dependent on
theories and phenomena
Models serve as instruments for
investigation in science.
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Models in Philosophy of Science
(cont.)

How are models constructed?
Not derived entirely from theory or
phenomena
 Involve both, and also additional
“outside” elements (modeling
decisions).

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Models in Philosophy of Science
(cont.)

What do they represent?

Some aspect of the phenomena or
some aspect of theories
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Models in Philosophy of Science
(cont.)

What role do they have/how do they
function in scientific practices?

Function as tools or instruments.
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Models in Philosophy of Science
(cont.)

How do we learn from models?
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Not by looking at a model, but by
building and manipulating it.
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Computer Science vs
Cognitive Science
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Program: data structures
+ algorithms= running
programs
Representation: Implied
by the architecture,
mathematical definition of
the problem, design
specification of the task,
the software paradigm
used
Algorithms: Simplicity,
efficiency and complexity
trade-offs.
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Mind = mental
representation +
computational procedures
= cognition
Representation: Cognitive
Architecture or the
ontology of human mental
process is not given.
Hope: algorithms and
representations posited
will clarify the
architecture, too.
Algorithms: Performance
on realistic data, simplicity
in terms of plausibility
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Artificial Intelligence vs
Cognitive Science
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The study and automation of
intelligent behaviour (Luger &
Stubblefield)
Success:
Commercial/Performance – as
described by proposals such as
Turing test (?) or in a limited
domain
aI: the study of human
intelligence with computer as a
tool (Yeap, 97)
vs Ai: the study of machine
intelligence as artificial
intelligence
Theoretical, experimental or
applied (Rumelhart)
Failures (?): Frame problem,
syntax vs
semantics/intentionality
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The study of cognition, mental
activity involving acquisition,
storage transformation and use
of knowledge; study of mental
processes such as memory,
language, thought, perception,
consciousness ....
Success: “Competence” explanatory power of a cognitive
theory: pyschological and
neurological plausibility,
computational and
representational power, practical
applicability to education, design
etc.
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An Example from Chess
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Human experts use relatively shallow
searches, averaging only three or four
moves deep; perceptual patterns and
their recognition play an important part
in guiding the search.
Chess programs rely on extensive search
and optimization of search techniques.
Deep Blue evaluated 200 million moves
per second in 1997.
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Computational Models in
Cognitive Science
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A computer program which implements a theory
of some aspect of cognition (Green)
Representations and processes of some cognitive
theory made precise by analogy with data
structures and algorithms (Thagard)
Do computational models have to subscribe to
strong AI view (aim: building machines that
duplicate minds) to be useful as research tools in
cognitive science?

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Not necessarily!
Weak AI: Can machines be made to act as if they were
intelligent?
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Some Philosophical
Background
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Functionalism: Most general features of
cognition must be independent of neurology- the
physical system – and the embodiment of mind.
Mental states are abstract functions that get us
from a given input to a given output.
Cognitivism: All there is to cognition is in mental
states and thought.
Computational Theory of Mind ~Computational
Representational Understanding of Mind: Human
cognition can be best understood in terms of
representational structures in the mind and
computational procedures that operate on them.
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Computational Theory of
Mind
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Thought processes are computations on
representations.
The mind can be realized/implemented
outside of the brain eg. in a digital
computer.
Is the mind a digital computer?
Church-Turing Thesis: The Universal
Turing Machine can perform any
calculation that can be described by an
effective procedure.
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Misconceptions of ChurchTuring Thesis

It doesn’t say that given a standard
computer, you can compute any
rule-governed input-output
function.It doesn’t rule out machines
(or brains) that compute non-Turing
computable functions. Thus, it does
not entail that brains can be
simulated by a Universal Turing
Machine.
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Questions
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Can a certain approach contradict with
Computational Theory of Mind (mental
representation + computational processes
= cognition) and still involve
computational modelling ? (Yes –see
dynamical approaches)
Do you have to ascribe to a functionalist
view (mental states are abstract functions
– can be described independent of brain
states) to do computational modelling ?
(No – see computational neuroscience)
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The Status of a
Computational Model
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“It is not the computer program that is
the theory, at best they inspire the
construction of a theory.” (Scheutz)
“Simulation is not a reasonable goal for
cognitive science.” (Fodor)
“AI is to psychology as Disneyland is to
physics.” (Green)
“Artificial Intelligence is to cognitive
science as mathematics is to physics.”
(Rumelhart)
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Marr’s Levels of Analysis
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Computational: What information
processing is being solved, and why?
Algorithmic: Representation and
Programming. How is the problem being
solved?
Implementational: What physical
properties are required to build such a
system? Hardware (e.g. brainstates)
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Subject
Model
Computational
=
One-to-many
Algorithmic
Architectural
One-to-many
Computational
One-to-many
=
=
Algorithmic
Architectural
One-to-many
Implementational = Implementational
(Dawson, 98)
Method
Theory

Behavioural Experiments
Algorithmic

Architectural

Computer
Simulations
Computational
One-to-many
One-to-many
Implementational

Cognitive and Computational
Neuroscience
Adapted from (Brent, 96)
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The Function of
Computational Models
Computational Cognitive Model
Simulates
Generates
Cognitive Process
Implements
Describes
Behaviour
Explains
Theory
Cooper (2002) – Ch.1
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Explanation by Computer Simulation
(Fernandez, 2003)
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Causal Explanation:
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The system uses a
program in order to
compute a certain
input-output mapping.
Explaining how you
cooked a tasty dish
Do you have enough
justification for that?
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Functional Analysis
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The system executes
a program which
amounts to computing
a certain mapping.
Explaining how an car
manufacturing
assembly line works
Multiple realizability?
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Advantages of
Computational Modelling
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Clarify, formally and unambiguously
specify a certain cognitive theory
Create experimental participants that are
durable, flexible etc. – in silico
Allow detailed evaluation and exploration
of cognitive theories by means of raising
new hypotheses
Enable interaction between studies in
different disciplines
Not THE method, but a complementary
method
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Strategies
Develop a model of some task or behaviour in
order to learn more about it: “a fishing trip”
 Implement a pre-existing, verbally specified
highly complex theory to see if its theoretical
assumptions are sufficient/necessary to account
for the target behaviour.
 Generate predictions/hypotheses to be then
tested by behavioural experiments.
Platform: Cognitive models of individual processes
vs “unified” approach – cognitive architectures
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Evaluation of Models
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Behavioural Outcome Modelling: Roughly
showing similar behaviours as human beings
Qualitative Modelling: Same qualitative
behaviours that characterize human behaviour,
e.g. similar improvement, deteoriation
Quantitative Modelling: Similar quantitative
behaviour as exhibited by humans, indicated by
quantitative performance measures
A combination of the above (Sun, 98)
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Practical Problems with
Cognitive Modelling
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Goodness-of-fit problems
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Theory-model amalgamation
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Individual Differences
Incidental Details Problems- scalability and sensitivity
analysis needed
Problematic Predictive Power
Statistical interpretation varies as compared to
hypothesis-testing statistics usage in psychology
Complexity and understandability trade-offs
Isolated modelling – not enough interaction
with different levels of theorizing and methods.
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Method
Theory

Behavioural Experiments
Algorithmic

Architectural

Computer
Simulations
Computational
One-to-many
One-to-many
Implementational

Cognitive and Computational
Neuroscience
Adapted from (Brent, 96)
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Paradigms in Computational
Modelling
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Symbolic systems – best for accounting
for rationality, systematicity etc. of
symbol systems?
Connectionism – biologically plausible ?
Dynamicisim – best for exploring
embodied, situated, temporal cognition?
Hybrid approaches
Similar Division in AI: GOFAI – Good, Old
Fashioned AI vs NFAI – New Fangled AI
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Achievements for Cognitive
Modelling
Shaping theories for various
cognitive domains: language and
skill acquisition, individual
differences in working memory,
cognitive lesioning simulations and
neuropsychology.
 Applied areas: Human-computer
interaction, intelligent-tutoring
systems
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Future for Cognitive
Modelling
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Integration of Computational
Neuroscience and more abstract forms of
cognitive modelling – e.g. Blue Brain
project
More interaction between Artificial
Intelligence and Cognitive Modelling – esp
in Cognitive Architectures
More emphasis in hybrid models –
symbolic, dynamic, connectionist,
bayesian etc.
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Lecture 2
Unified Theories of Cognition
 Cognitive Architectures
 Sample Architectures vs Frameworks
 Reading: Langley, Laird and Rogers
(2009) Cognitive Architectures
 Start Readings for the project and think
about your project groups.
 Check Forum for online activity.

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