Cognitive Science: Definition, Status, and Questions. Hunt. Annual Review of Psychology 1989.

  1. <Much of this is a description / justification of cognitive modelling, which I won’t be taking notes on>
  2. Newell & Simon’s Production System Programming
    1. Some representation of current state
    2. A policy
    3. Called a programming notation
    4. Problems can have different “grain sizes” which seems to mean level of abstraction (of atoms)
  3. ACT*
    1. Distinction between declarative and procedural information
    2. Learning is part of computation / program execution “… in which procedural and declarative information in long-term memory is used tor construct new productions.”
    3. <next P>”If learning is production construction, then transfer of learning from one task to another will be determined by the number of productions the two tasks share, rather than the number of common actions required by the originally learned and transfer tasks.”
  4. Studies of thought at the Representational Level: Reasoning
  5. “… psychometric studies have found that people who do well on pure deductive problems do not always show superior inductive reasoning, and vice versa (…).”
  6. “Newell & Simon’s (…) work on a domain independent deductive reasoning program, the General Problem Solver (GPS), is generally considered the foundation of cognitive science.  To use GPS a person provided the program with a set of known statements, rules for deriving new statements from old statements, and the description of a desired (goal) statement.  The program contained heuristics for problem solving, defined in its own internal language, that allowed it to use the domain-specific rules to develop a chain of inferences linking the known statements to the goal statement.  Three aspects of the GPS approach recur in varying forms in all modern studies of representational thought: searching a problem space, goal-directed problem solving, and reliance on weak (context-free) problem-solving methods.”
  7. “GPS’ heuristic procedure was means-end (or backward-driven) problem solving.  The program compared the goal to the current state, determined the differences between them, and then searched for an operator that would reduce the difference… A slightly different technique, which emphasizes the backward nature of the method somewhat more, is to find those states from which the goal state can be reached, and then establish the GPS problem of reaching one of those states… In forward driven problem solving, on the other hand, rules of inference are chosen by an examination of the current state of knowledge, without regard to the goal state.”
  8. “Forward-driven problem solving is riskier than goal-based problem solving, because operations are executed … without first checking to see if these operations are likely to be an advance toward the goal.  On the other hand, forward-driven reasoning is cheaper, because operator selection is made without contrasting the present state of knowledge to the goal state. Thus, forward-driven problem solving is preferable if the problem solver knows enough about the problem-solving domain to recognize when certain actions should be taken.  This implies that a rational problem solver would use forward-driven reason in those (limited) domains with which he or she was familiar.  This turns out to be true.”
  9. <Still not really grokking the difference between forward and backward here.  How do you make forward progress if not in comparison to a goal?>
  10. Weak problem solving methods only place weak constraints on the problem.  GPS is weak because it is general, so the same heuristics can be applied to all sorts of problems if defined properly (such as minimize distance between state and goal).  “Strong” problem solving, on the other hand, are strongly tied to a particular domain, such as minimize king exposure in chess.
  11. Although there is not a necessary connection between  forward/backward and weak/strong, in general forward planning goes with strong because it is necessary to know how to behave without focusing explicitly on the goal, which is mostly applicable when there is strong knowledge of the domain
  12. “This has been shown in numerous studies contrasting ‘expert’ and ‘novice’ problem solving in domains ranging from physics (Larkin et al 1980; Larkin 1983) to economics (Voss et al 1983) and the law (O’ Neil 1987).  In all these fields experts appear to utilize forward-driven reasoning.  They recognize a situation and ‘immediately’ apply the appropriate rules for extracting information about that situation.”
  13. “In the terminology of cognitive science, the experts seemed to have memorized ‘schemas’ that function like ‘fill in the blanks’ forms for solving certain classes of problems.  Once the experts recognized the problem type they could apply a schema to guide further problem solving.  It is easy to see that schema-based problem solving is compatible with the production system architecture.  A schema can be thought of as a set of productions that are triggered when the schema’s preconditions are satisfied.  Just as in production execution, thinking is driven by pattern recognition. “
  14. <Oh, so it seems like forward reasoning isn’t really like forward search (how the planing community would call it) with heuristics; that is actually backwards reasoning.>
  15. So its a bit of a bummer because most expert behavior seems to be based on rules that are domain specific, and therefore hard to study, and hard to draw meaningful conclusions from.
  16. Applying a schema is deductive (top down), whereas choosing one is inductive (bottom up).  Schema creation is also inductive.
  17. <Inductive reasoning and classification based on prototypes, skimming>
  18. Experts and novices may make different classifications (the example of physics where experts categorize based on deeper aspects of the problem, whereas novices do so based on superficial aspects)
  19. <For some reason I’m missing a bit of the article, but I think I read the relevant part.>
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