computational cognitive modelling
DESCRIPTION
Computational Cognitive Modelling. COGS 511-Lecture 1 General Introduction . Related Readings. From Course Pack Cooper, R. Chapter 1: Modelling Cognition McClelland (2009). The Place of Modeling in Cognitive Science. - PowerPoint PPT PresentationTRANSCRIPT
22.04.23 COGS 511 - Bilge Say 1
Computational Cognitive Modelling
COGS 511-Lecture 1General
Introduction
22.04.23 COGS 511 - Bilge Say 2
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.
22.04.23 COGS 511 - Bilge Say 3
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.
22.04.23 COGS 511 - Bilge Say 4
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.
22.04.23 COGS 511 - Bilge Say 5
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.
22.04.23 COGS 511 - Bilge Say 6
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?
22.04.23 COGS 511 - Bilge Say 7
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.
22.04.23 COGS 511 - Bilge Say 8
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).
22.04.23 COGS 511 - Bilge Say 9
Models in Philosophy of Science (cont.) What do they represent?
Some aspect of the phenomena or some aspect of theories
22.04.23 COGS 511 - Bilge Say 10
Models in Philosophy of Science (cont.) What role do they have/how do they
function in scientific practices? Function as tools or instruments.
22.04.23 COGS 511 - Bilge Say 11
Models in Philosophy of Science (cont.) How do we learn from models?
Not by looking at a model, but by building and manipulating it.
22.04.23 COGS 511 - Bilge Say 12
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
22.04.23 COGS 511 - Bilge Say 13
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.
22.04.23 COGS 511 - Bilge Say 14
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.
22.04.23 COGS 511 - Bilge Say 15
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?
22.04.23 COGS 511 - Bilge Say 16
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.
22.04.23 COGS 511 - Bilge Say 17
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.
22.04.23 COGS 511 - Bilge Say 18
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.
22.04.23 COGS 511 - Bilge Say 19
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)
22.04.23 COGS 511 - Bilge Say 20
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)
22.04.23 COGS 511 - Bilge Say 21
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)
22.04.23 COGS 511 - Bilge Say 23
TheoryComputational Behavioural Experiments
Method
Algorithmic Computer SimulationsArchitectural
Implementational Cognitive and Computational Neuroscience
One-to-many
One-to-many
Adapted from (Brent, 96)
22.04.23 COGS 511 - Bilge Say 24
The Function of Computational Models
Computational Cognitive Model
Generates
Behaviour
Explains
Theory
Cognitive Process
Simulates
Describes
Implements
Cooper (2002) – Ch.1
22.04.23 COGS 511 - Bilge Say 25
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?
22.04.23 COGS 511 - Bilge Say 26
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
22.04.23 COGS 511 - Bilge Say 27
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
22.04.23 COGS 511 - Bilge Say 28
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)
22.04.23 COGS 511 - Bilge Say 29
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.
22.04.23 COGS 511 - Bilge Say 30
TheoryComputational Behavioural Experiments
Method
Algorithmic Computer SimulationsArchitectural
Implementational Cognitive and Computational Neuroscience
One-to-many
One-to-many
Adapted from (Brent, 96)
22.04.23 COGS 511 - Bilge Say 31
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
22.04.23 COGS 511 - Bilge Say 32
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
22.04.23 COGS 511 - Bilge Say 33
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.
22.04.23 COGS 511 - Bilge Say 34
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.