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Computational Cognitive Modelling

COGS 511-Lecture 1General

Introduction

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Related ReadingsFrom 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) 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 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

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. 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

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:

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?

Not by looking at a model, but by building and manipulating it.

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Computer Science vs Cognitive Science 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.

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 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

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 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 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? Not necessarily! Weak AI: Can machines be made to act as if they were

intelligent?

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Some Philosophical Background 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 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 Church-Turing 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 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 “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 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)

ModelComputational = Computational

Subject

Algorithmic = AlgorithmicArchitectural = Architectural

Implementational = Implementational

One-to-many One-to-many

One-to-many One-to-many

(Dawson, 98)

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TheoryComputational Behavioural Experiments

Method

Algorithmic Computer SimulationsArchitectural

Implementational Cognitive and Computational Neuroscience

One-to-many

One-to-many

Adapted from (Brent, 96)

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The Function of Computational Models

Computational Cognitive Model

Generates

Behaviour

Explains

Theory

Cognitive Process

Simulates

Describes

Implements

Cooper (2002) – Ch.1

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Explanation by Computer Simulation (Fernandez, 2003)

Causal Explanation: 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?

Functional Analysis 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 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 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 Goodness-of-fit problems

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 Theory-model amalgamation

Complexity and understandability trade-offs Isolated modelling – not enough interaction with

different levels of theorizing and methods.

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TheoryComputational Behavioural Experiments

Method

Algorithmic Computer SimulationsArchitectural

Implementational Cognitive and Computational Neuroscience

One-to-many

One-to-many

Adapted from (Brent, 96)

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Paradigms in Computational Modelling 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 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.