Statement of the problem: how do people learn to solve complex problems.
Newell's twenty questions paper. Recommendations: 1) use a unified theory
2) use a single complex problem and study all its aspects. To comply with
recommendation 1, we'll use an architecture for cognition as a vehicle.
To comply with recommendation 2, we'll study a class of problems that is
interesting because of formal reasons: NP-complete problems. Background
knowledge about Complexity Theory, examples of complex problems, introduction
of the research methods: formal analysis, empirical research through protocol
analysis and computer simulation using cognitive architectures. Proof why
learning is necessary to give a full account of problem solving.
Chapter 2: Architectures for Cognition
The main research goal of this thesis is a cognitive model of how people
learne to solve complex problems, justified by empirical data and a formal
analysis. Cognitive models can be constructed using architectures of cognition.
Architectures in general will be discussed in this chapter, as well as
some examples, Soar, ACT-R, 3CAPS and EPIC. Some comparisons will be made
and the choice of ACT-R will be justified. Also a number of approaches
to learning will be discussed in detail, and a taxonomy of learning methods
will be made.
Chapter 3: Scheduling
Description
and analysis of the scheduling experiment. (At least) two conclusions will
be made: people use rehearsal if the plan by heart, and people revise their
strategies. Other desired conclusion: subjects develop a different viewpoint
on the problem with experience.
Idea
that implicit learning is learning through the standard mechanisms of the
architecture, and explicit learning is learning through learning strategies.
- Model of the Tulving experiment
- Model of rehearsal and free-recall
When
subjects are solving problems, their behavior can be classified into two
rough categories: search and reflection. Both types of behaviour have different
effect on the type of knowledge that is learned. A model based on dynamical
growth is presented, that shows that alternating search and reflection
is a rational thing to do. Some of the predictions are tested in an experiment
in which subjects have to use grammars to contruct a certain word. Some
developmental theories are discussed, since they may shed light on the
how learning strategies themselves are learned. Fischer's theory, Karmiloff-Smith's
theory, Siegler's Theory. ACT-R model of strategy creation and revision.
Example for the labeled-beam task. Extension to reversal-shift learning
and developmental theories
Some
general conclusions: a general theory of skill learning, some remarks on
individual differences and development, and practical implications, mainly
in the field of cognitive ergonomics and education. The usefulness of ACT-R
for this topic will also be evaluated.
Statement of the problem: how do people learn to solve complex problems.
Newell's twenty questions paper. Recommendations: 1) use a unified theory
2) use a single complex problem and study all its aspects. To comply with
recommendation 1, we'll use an architecture for cognition as a vehicle.
To comply with recommendation 2, we'll study a class of problems that is
interesting because of formal reasons: NP-complete problems. Background
knowledge about Complexity Theory, examples of complex problems, introduction
of the research methods: formal analysis, empirical research through protocol
analysis and computer simulation using cognitive architectures. Proof why
learning is necessary to give a full account of problem solving.
The main research goal of this thesis is a cognitive model of how people
learne to solve complex problems, justified by empirical data and a formal
analysis. Cognitive models can be constructed using architectures of cognition.
Architectures in general will be discussed in this chapter, as well as
some examples, Soar, ACT-R, 3CAPS and EPIC. Some comparisons will be made
and the choice of ACT-R will be justified. Also a number of approaches
to learning will be discussed in detail, and a taxonomy of learning methods
will be made.
Description
and analysis of the scheduling experiment. (At least) two conclusions will
be made: people use rehearsal if the plan by heart, and people revise their
strategies. Other desired conclusion: subjects develop a different viewpoint
on the problem with experience.
Idea
that implicit learning is learning through the standard mechanisms of the
architecture, and explicit learning is learning through learning strategies.
When
subjects are solving problems, their behavior can be classified into two
rough categories: search and reflection. Both types of behaviour have different
effect on the type of knowledge that is learned. A model based on dynamical
growth is presented, that shows that alternating search and reflection
is a rational thing to do. Some of the predictions are tested in an experiment
in which subjects have to use grammars to contruct a certain word. Some
developmental theories are discussed, since they may shed light on the
how learning strategies themselves are learned. Fischer's theory, Karmiloff-Smith's
theory, Siegler's Theory. ACT-R model of strategy creation and revision.
Example for the labeled-beam task. Extension to reversal-shift learning
and developmental theories
Model
of the Sugar Factory and the Fincham task.
Model
will combine the results of chapter 4, 5 and 6. (& will model some
mental imagery). Predictions of the model will be compared to empirical
data. 
Some
general conclusions: a general theory of skill learning, some remarks on
individual differences and development, and practical implications, mainly
in the field of cognitive ergonomics and education. The usefulness of ACT-R
for this topic will also be evaluated.