Dreyfus contends that there is no evidence humans solve analogy problems via heuristic search over e… — Hubert L. Dreyfus

Dreyfus contends that there is no evidence humans solve analogy problems via heuristic search over explicit transformation rules, and that descriptive psychological evidence instead indicates a quite different, Gestalt‑like mode of reasoning, so the optimistic expectation that Evans’s techniques can be generalized rests on an unsupported hypothesis about human cognition.

By Hubert L. Dreyfus, from What Computers Can't Do

Key Arguments

He grants that 'if human beings did solve analogy problems in this way, there would be every reason to expect to be able to improve and generalize Evans' program, since human beings certainly surpass the machines' present level of performance.'
However, he immediately undercuts this conditional: 'But, as in the case of GPS, there is no evidence that human beings proceed in this way, and descriptive, psychological evidence suggests that they do not.'
To support this, he introduces Rudolph Arnheim’s detailed phenomenological description of how humans actually experience such problems, emphasizing that it 'has all the aspects of genuine thinking' and looks nothing like combinatorial search and rule weakening.
Dreyfus later generalizes the point: 'Obviously Minsky and Evans think that analogies are solved by human beings by applying transformation rules, because the prospects for AI are only encouraging if this is how humans proceed.'
He concludes that 'it is clearly circular to base one's optimism on an hypothesis which, in turn, is only justified by the fact that if the hypothesis were true, one's optimism would be justified,' accusing them of circular reasoning.

Source Quotes

Stated very briefly, given a problem of this type, the program uses various heuristics to select a "correct" rule (in a reasonable time) from a very extensive class of possible rules. 32 It is true that if human beings did solve analogy problems in this way, there would be every reason to expect to be able to improve and generalize Evans' program, since human beings certainly surpass the machines' present level of performance. But, as in the case of GPS, there is no evidence that human beings proceed in this way, and descriptive, psychological evidence suggests that they do not.

32 It is true that if human beings did solve analogy problems in this way, there would be every reason to expect to be able to improve and generalize Evans' program, since human beings certainly surpass the machines' present level of performance. But, as in the case of GPS, there is no evidence that human beings proceed in this way, and descriptive, psychological evidence suggests that they do not. Rudolph Arnheim, professor of psychology at Harvard University, in discussing Evans' work, has described the different way in which human beings approach the same sort of problem.

33 As in the case of chess, it turns out that global perceptual grouping is a prior condition for the rule-governed counting outthe only kind of procedure available to the machine. As Arnheim puts it, "Topology was discovered by, and relies on, the perceptual powers of the brain, not the arithmetical ones."34 Obviously Minsky and Evans think that analogies are solved by human beings by applying transformation rules, because the prospects for AI are only encouraging if this is how humans proceed. But it is clearly circular to base one's optimism on an hypothesis which, in turn, is only justified by the fact that if the hypothesis were true, one's optimism would be justified.

As Arnheim puts it, "Topology was discovered by, and relies on, the perceptual powers of the brain, not the arithmetical ones."34 Obviously Minsky and Evans think that analogies are solved by human beings by applying transformation rules, because the prospects for AI are only encouraging if this is how humans proceed. But it is clearly circular to base one's optimism on an hypothesis which, in turn, is only justified by the fact that if the hypothesis were true, one's optimism would be justified. Quillian's Semantic Memory Program The final program we shall consider from Phase II, Ross Quillian's Semantic Memory Program, is the most interesting, because most general; and the most modest, in that its author (working under Simon rather than Minsky) has made no sweeping promises or claims.35* This program confirms a general evaluation heuristic already apparent in Samuel's modesty and success and Simon's and Gelernter's claims and setbacks, namely that the value of a program is often inversely proportional to its programmer's promises and publicity.

Key Concepts

It is true that if human beings did solve analogy problems in this way, there would be every reason to expect to be able to improve and generalize Evans' program, since human beings certainly surpass the machines' present level of performance.
But, as in the case of GPS, there is no evidence that human beings proceed in this way, and descriptive, psychological evidence suggests that they do not.
Obviously Minsky and Evans think that analogies are solved by human beings by applying transformation rules, because the prospects for AI are only encouraging if this is how humans proceed.
But it is clearly circular to base one's optimism on an hypothesis which, in turn, is only justified by the fact that if the hypothesis were true, one's optimism would be justified.

Context

After presenting Evans’s and Minsky’s search‑based account, Dreyfus explicitly contrasts it with psychological descriptions like Arnheim’s and uses this to argue that their optimism about generalizing Evans’s program rests on an unverified and self‑serving hypothesis about human analogy solving.