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ACT-R (most important assumption is that human knowledge can be divided…

- - - - collection of chunks that contain few elements that are linked together through spreading activation-->highly active chunks are kept in short- term memory and rest in long- term memory
      - Chunks: schema like structures
        
        Each row in a chunk holds a value in a different slot (e.g. one saying which kind of chunk it is)
        
        Chunks are linked another through values in a kind of network and influence another through these by limited activation spreading
        
        Activation is not constant for a chunk and allows one to model effects as priming
        
        Each chunk has a level of activation that adds to all other chunks it is connected to through its values
        
        Low activation – hard to retrieve, LTM
        
        High activation – easily to retrieve, STM
        
        sensationalist theory: knowledge structures result from environmental encodings
        
        ACT-R can move its attention over the visual array and recognize objects so that the features within spotlight cab by synthesised into recognizing objeczs
        
        Categorization: various features in chunks spread activation to various categories according to the likelihood of feature given the category: BASE LEVEL ACTIVATION to reflect prior frequencies
        
        chunks can be created by the activation of production rules -->produciton rules originate from the encoding of chunks
  - - - a specific production rule can only apply when rule´s conditions are satisfied by knowledge currently available in declarative memory
        
        greater activation of a chunk in declarative memory= the faster productions that use it (model human response time)
        
        The more active a production rule, the more likely its use in the current state
        
        for production rule to be used, its condition part (IF) must match the current goal and use chunks that are already available in dec. memory
      - New rules can be learned through Production compilation (= transfer knowledge from declarative to procedural memory)
        
        1. Understanding – understand instructions and encode this knowledge as chunks in declarative memory
        
        2. Production compilation – solve problems by applying instructions & generalize from experience
        
        through the application, declarative representation of the instruction becomes transformed into a production rule > more general than the declarative chunks (specific values are replaced by variables)
        
        3. Practice: Solve problems with increasing speed & accuracy
        
        Decrease use of declarative instruction, increase use of production rules
  - - - INSTRUCTIONS OF HOW to PERFORM TASK START OUT WITH CUNKS AND AS PERFORMANCE IMPROVES TROUGH PRACTICE; TURNED NTO PROCEDURAL MEMORY ; WHICH SAME AS CHUNKS BUT AUTOMATIC
  - - - KNOWLEDGE IS MADE AVAILABLE ACCORDING TO ITS ODDS OF BEING USED IN A PARTICULAR CONTEXT-->MIND KEEPS TRACK OF GENERAL USEFULNESS AND COMBINES THIS WITH CONTEXTUAL APPROPRIATE TO MAKE SOME INFERENCE ABOUT WHAT KNOWLEDGE TO MAKE AVAILABLE TO CONTEXT
        
        activation level= basel level+ contextual priming
        
        activation level: being appropriate in the current context; base level: odds that has been useful in past; contextual priming: likelihood that it will be useful in given context
- - - - allows storing many items & recalling them perfectly
        
        HUMANS CANNOT DO THIS