Learning to program in LISP

Anderson, John R.; Farrell, Robert; Sauers, Ron

doi:10.1016/s0364-0213(84)80013-0

Cited by 177 publications

(163 citation statements)

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“…This tendency to subgoaling has been found by Klahr and Robinson (1981) in children's solving of a number of puzzles. It appears in numerous academic problem-solving activities such as mathematical problem solving (Catrambone, 1995) August 20, 2002 6 and programming (Anderson, Farrell, & Sauers, 1984). It is a basic mechanism in problems studied in Newell and Simon (1972) and indeed the phrase "means-end problem-solving" comes from the general observation that humans sometimes temporarily adopt the means as their end.…”

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confidence: 99%

Tower of Hanoi: Evidence for the cost of goal retrieval.

Anderson

Douglass²

2001

Journal of Experimental Psychology: Learning, Memory, and Cogni

119

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Past research on the Tower of Hanoi problem has provided clear evidence for the importance of goal-subgoal structures in problem solving. However, the nature of the traditional Tower of Hanoi problem makes it impossible to determine whether there is any special cost associated with storing or retrieving goals. A variation of the Tower of Hanoi problem is described that allows one to determine separately if there is an effect of how long a goal has to be retained on storage time or how long ago it was formed on retrieval time. This paradigm provides evidence for an effect of retention interval on retrieval time and not on storage time. An ACT-R (Adaptive Control of Thought-Rational; J. R. Anderson & C. Lebiere, 1998) simulation of these data is described, which treats goal memory as no different from other memories.The Tower of Hanoi problem has become a tool in cognitive psychology to understand planning both from an informationprocessing perspective (e.g., Simon, 1975) and a cognitive neuroscience perspective (e.g., Goel & Grafman, 1995). A simple version of this problem is illustrated in Figure 1. There are three pegs and four disks of differing sizes. The disks have holes in them, so they can be stacked on the pegs. The disks can be moved from any peg to any other peg. Only the top disk on a peg can be moved, and it can never be placed on a smaller disk. The disks all start out on Peg A, but the goal is to move them all to Peg C, one disk at a time, by means of transferring disks among pegs.Until the 1970s, the Tower of Hanoi was a puzzle of mild interest to mathematicians (e.g., Claus, 1884;Domoryad, 1964;Hinz, 1992) and just one of a stock of problems used by researchers interested in the effects of various manipulations on problem solving (e.g., Ewert & Lambert, 1932;Gagne & Smith, 1962). Then, reflecting the rise of the information-processing perspective, its structure became a task of special interest to psychologists (e.g., Egan & Greeno, 1974;Hayes & Simon, 1974;Simon, 1975). In Simon's (1975) paper, he described a number of methods for solving the problem that required a goal-subgoal structure. One of these methods was the goal-recursion strategy, which prescribed that in order to move a pyramid of n disks from Peg A to Peg C, one moved a pyramid of n -1 disks from Peg A to Peg B, the nth (largest) disk to Peg C, and then the pyramid of n -1 disks from Peg B to Peg C. The strategy applied recursively to moving the smaller pyramids. He also described a variety of "sophisticated perceptual" strategies that basically prescribed focusing on the largest disk out of place, finding the largest disk blocking its move,

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confidence: 99%

Tower of Hanoi: Evidence for the cost of goal retrieval.

Anderson

Douglass²

2001

Journal of Experimental Psychology: Learning, Memory, and Cogni

119

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“…Experiment 2 demonstrated that ifthe conditions for applying a method are highlighted in examples, learners are more likely to appropriately adapt that method in a novel problem, perhaps because they recognize that the conditions do not fully match those required for any of the old methods. Overall, the results indicate that the subgoal/method representational scheme may be useful in predicting transfer performance.A consistent finding in the problem-solving literature is that learners are often unable to make appropriate use of prior information to solve new problems if the new problems differ from training examples in more than minor ways (e.g., Anderson, Farrell, & Sauers, 1984;Gick & Holyoak, 1980Reed, Dempster, & Ettinger, 1985; Spencer & Weisberg, 1986). Learners often seem to acquire primarily superficial knowledge from training examples, consisting of little more than aseries of memorized steps.…”

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Learning subgoals and methods for solving probability problems

Catrambone

Holyoak

1990

Memory & Cognition

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We hypothesize that typical example problems used in quantitative domains such as algebra and probability can be represented in terms of subgoals and methods that these problems teach learners. The "quality" ofthese subgoals and methods can vary, depending on the features ofthe examples. In addition, the likelihood of these subgoals' being recognized in novel problems and the likelihood of learners' being able to modify an old method for a new problem may be functions of the training examples learners study. In Experiment 1, subjects who studied examples predicted to teach certain subgoals were often able to recognize those subgoals in nonisomorphie transfer problems. Subjects who studied examples demonstrating two methods rather than one exhibited no advantages in transfer. Experiment 2 demonstrated that ifthe conditions for applying a method are highlighted in examples, learners are more likely to appropriately adapt that method in a novel problem, perhaps because they recognize that the conditions do not fully match those required for any of the old methods. Overall, the results indicate that the subgoal/method representational scheme may be useful in predicting transfer performance.A consistent finding in the problem-solving literature is that learners are often unable to make appropriate use of prior information to solve new problems if the new problems differ from training examples in more than minor ways (e.g., Anderson, Farrell, & Sauers, 1984;Gick & Holyoak, 1980Reed, Dempster, & Ettinger, 1985; Spencer & Weisberg, 1986). Learners often seem to acquire primarily superficial knowledge from training examples, consisting of little more than aseries of memorized steps. For instance, some of the examples used in the present experiments involve finding the average number of times an event OCCUfS per trial (such as the average number of errors per game made by the Detroit Tigers' infield) given the frequencies of the various subevents (e.g., the number of games in which the infield made°errors, the number of games in which they made exactly I error, and so on). Learners are good at memorizing, from examples, the steps of multiplying each subevent (0, I, etc.) by its observed frequency, summing the results, and dividing by the number of trials in order to achieve the subgoal of finding the average frequency. However, they rarely notice that the steps of multiplying each subevent by its frequency and then summing them could be viewed as a way of finding the total frequency of the event. As a result, these learners are usually unable to find the average frequency of an event when a problem provides the total frequency of that event directly, rather than requiring that it be derived from the frequencies of the various subevents.In the present paper, we will argue that the knowledge people gain from examples in domains that emphasize solving problems, such as probability , can be represented by subgoals and methods. Furthermore, manipulations of examples can affect which subgoals and methods a learner acquires, th...

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“…Having acquired abody of rules, experts predominantly engage in rule-based processing, unless problems come up that are extraordinarily hard or exceptional. In contrast, several investigators have demonstrated that novices make extensive use of examples (e.g., Anderson, Farrell & Sauers, 1984, Reed, Dempster & Ettinger, 1985. Ross (1984) suggests that preference of case-based over rule-based reasoning results from the extent of similarity between the current and a previously solved problem.…”

Section: Conditions Of Learningmentioning

confidence: 99%