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task 5 Act-R (Act-R --> has serial processing ( searching problem…

- - - - if part = condition
        then part = action
        product = solution
  - - - analogy to human brain:
        --> many rules in long term memory BUT
        --> only few accessible at a given time cause they are more accessible in short term memory thus through heuristics these get choosen more #
  - - - --> inductive generalisation = rule is created through summary of many experiences/ examples
      - --> chunking (SOAR)/composition(ACT old one) = rule is formed by other rules (or many rules/options chunked together into a more general one!) generalizing the rule !!
      - --> specialization, in which an existing rule is modified to deal with a specific situation
      - --> abductive learning, in which a rule is run backwards to provide a possible explanation of what happened. 
      - --> slow incremental learning = the more successful a rule operates the more like to be used again in future :3 !!
  - - - Power law
        --> learning at first fast then slower
        --> SOAR can explain cause first chunks can be build pretty quickly, but then at some point they become so specialized, that they are rarely applied, simply cause the situations happen rarely and performance of learning seemingly slows down a bit :3 !!
        --> also cause if less used then chunks = less active and thus less salient and less accessible :3 !!
      - Conditioning
        --> rules activated if they hear a tone (activation of chunks which spread activation to other chunks etc and response elicited)
      - Tick tack go or any other game lol
      - Human language !! (anderson)
        
        IF the goal is to communicate a meaning structure of the form (relation, agent, object), THEN set as subgoals
        
        to describe agent
        
        to describe relation
        
        to describe object.
    - - basic relation of rules on neurons
        --> if input strong enough then fire
      - ACT-R Brain correlates
        --> production rules (just rules cause production is same as rules) = applied / used by basal ganglia !
        --> stored in PFC buffer :3 !!
      - Connectionism has the best neurological plausability though !!
- - - - Declerative memory
        
        --> things that we know and were conscious of at a given moment
        --> consists of chunks with 2 to 6 elements per chunk
        --> Hippocampus
        
        .
        
        Chunks
        
        chunks are connectect if elements of one chunk also present in another chunk
        
        spread activation
        = one chunk activates an element of that chunk also present as element in other chunk so it get activated as well # #
        
        Leads to parallel processing !!
        
        activation
        --> = the more elements in a chunk match the stimulus or rule or somethign the stronger it activates
        --> the stronger active a chunk the more salient in becomes !!
        
        strong active chunks are more salient = same as short term memory (salient at forefront of attention chunk / rules)
        --> important for heuristics
        
        weak active chunks = less salient = same as long term memory
        
        activation = NOT constant !!
        --> if nothing happens activation slowly decreases until chuk in LTM and not acesible until reactivated :p
        ---> but if activated again = increases proportional
        -------> explains priming effect (if chunk was active and then cues hortly later its more likely to activate again)
        
        important for priming effects cause of chunk activation and spread activation !!
      - Procedual memory
        
        --> things that we know how to do at any given time
        -----> e.g. like tying your shoe laces :3 !!
        --> basal ganglia / PFC
        
        Connected by:
        
        --> retrival request: production rule in procedual memory needs retrival from declerative memory
        --> production complication: new production rules can be created in procedural memory from ‘chunks’ in declarative memory. This is especially important in ACT-R models of learning, where new rules are formed as learners become more skilled at solving problems.
        
        Production rule
        --> can only be used when condition part (IF) matches the current goal AND use chunks already available in declarative memory. # #
        
        if part = condition
        then part = action
        product = solution
        
        activation also applies here the more activated the more likely to be used ,
        --> the less activation the less likely its gonna be used even if applicable
        
        learning in 3 stages:
        
        understanding
        
        production compilation
        --> new production rules can be created in procedural memory from ‘chunks’ in declarative memory.
        
        practice
      - Goal module (most up to date model)
        --> represents what ACT-R currently wants to do so the actual goal :3 !! thsi is used as reference point every step along the way and possible nodes in problem spaced get compared to this :3 !!
        
        Current goal
        --> goal that is the focus of interest at the moment
        
        Goal stack
        --> where other goals are stored that we are currently not working on but may in the future
        
        Push and Pop
        Push = from current goal to stack
        Pop = from goal stack to current goal :3 !!
      - ACT-R also has a range of very simple perceptual and motor components, that allow it to model visual stimuli, auditory stimuli, and actions. These components are not directly linked to production memory, but, like declarative and goal modules, are connected through small shared buffers. Only the buffer contents affect production.
    - - HAM 1973
        --> declerative memory = collection of chunks/declerative memory elemts WHICH contain between 2 and 4 elements
    - - Symbolic
        --> something that has actual representation in the real world
      - Subsymbolic
        --> something that has no real physical representation in the real world :3 !!