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ACT-R (Adaptive Control of Thought-Rational) (CRUM (Computational power:…
ACT-R (Adaptive Control of Thought-Rational)
Cognitive modelling
Components
Procedural memory
Production rule
Conflict set
Conflict resolution: pG-C
Declarative memory
Chunks
Activation level
Processes
Retrieval result: declarative memory => goal stack
Popped goal: current goal => declarative memory
Transform goal: procedural memory => current goal
Production compilation: knowledge transfer declarative memory => procedural memory
Retrieval request: procedural memory => declarative memory
Goal stack: last in, first out principle
CRUM
Representational power: low
IF-THEN rules
Computational power: high
Problem solving
Search
Heuristic search
Problem space
Means ends analysis
Minimax
Planning
Reasoning
Bidirectional search
Decision
in addition to other processes
Explanation
Deductive process, hypotheses
Learning
Inductive generalisation: from examples
Chunking/composition: from other rules
Specialization: modify an existing rule for specific situation
Abductive learning: backward looking at the rule
Slow incremental learning: numerical value for utility
Psychological plausibility: high
Neurological plausibility: low to intermediate
Practical applicability: high
Cognitive architecture
3 streams
Information processing approach: neglecting the brain
Eliminative connectionism: neglecting mental functions
Rational analysis: neglecting the architecture
Hybrid model
Symbolic level: all or nothing; symbol is there or not
Subsymbolic level: degree of activation
Learning
Modify production rules
Create new chunks
Create new production rules
Increase or decrease probabilty of the activation of a production rule
Increase or decrease baseline activity and connections between chunks
Mistakes
Acquire wrong production rules or chunks
Appropriate production rules don't fire
Global neuronal workspace (Zyllerberg)
Similarities to ACT-R
Parallel processing
Production selection
Cognitive model according to which conscious access occurs when incoming information is made globally available to multiple brain systems through a netweork of neurons with longrange axons densely distributed in prefrontal, parietotemporal and cingulate cortices
SNIF-ACT (Pirolli)
Information Foraging Theory
Scent
User tracing methods
ANTZ versus CITY task
Link following behavior
Principles
knowledge representation
Performance
Knowledge aqcuisition (learning)
Search hints (Savenkov)
Task-specific search hints: effectively improve searcher success rates and reduce perceived effort - lower number of incorrect attempts - positive feedback
Generic search hints: detrimental in search effectiveness and user satisfaction - harder to follow - frustrated - increased number of incorrect attempts - higher perceived task complexity