Several studies have used protocol analysis to determine what it is that artificial experts have learned. Perhaps the most ambitious attempts to study extended learning in complex environments were Klayman’s studies of cue discovery (Klayman, 1988; Klayman & Ha, 1985): Subjects looked at a com- plex computer display consisting of geometric shapes that affected the dis- tance traveled by ray traces from one point on the display to another. The true rule for travel distance was determined by a complex linear model consisting of seven factors that varied in salience in the display. None of Klayman’s subjects induced the correct rule over 14 half-hour sessions, but their perfor- mances improved steadily. Some improvement came from discovering correct cues (subjects correctly identified only 2.83 of 7 cues, on average). Subjects who systematically experimented, by varying one cue and holding others fixed, learned faster and better than others. Because the cues varied greatly in how much they affected distance, it was important to weight them differently, but more than four-fifths of the rules stated by subjects did not contain any numerical elements (such as weights) at all. In sum, cue discovery played a clear role in developing expertise in this task, but learning about the relative importance of cues did not.
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