The shape of the memory representation for a 1000 word text was measured for the text's author, 7 independent subject matter experts and 2 groups of novices (N = 83 Air Force recruits). To measure the shapes, w e chose the 12 most important concepts from the text, and then collected proximity data on all possible pairs of them. Then w e made maps of the mental representations from the proximities. In Experiment 1, results of empirical tests of text learning showed that the novices' mental representations after reading the Original Version of the text were correlated only +.1 with the author's or experts' representations. But for a Principled Revision the correlations were above +.5. In Experiment 2, the proximity data from Experiment 1 were used to diagnose specific misconceptions and missing conceptions in both the Original text and the Principled Revision. This revealed unsuspected cognitive misconceptions, as well as intrusions of affective and attitudinal factors into the novices' mental representations. These diagnoses were then used to revise both texts to repair the misconceptions and insert the missing conceptions. Results of empirical tests of these revisions (N = 160 Air Force Recruits) showed that novices' correlations with the author's and experts' representations were shifted close to ceiling (r = + .8 - + .9). These results show that novices' mental representations can be shifted to correspond with experts by using our methods to diagnose and repair misand missing conceptions.

This paper reports three experiments showing that 17 experts' mental representations had significantly higher harmony and faster settling rates than 638 novices' when activation was spread through the representations in a simulation of thinliing; that when coherent texts were read by novices, they produced mental representations with significantly higher harmony and faster settling rates than less coherent texts; and that novices whose representations matched the experts' mental representations had significantly higher harmony and faster settling rates. The results were found for declarative experts in history and procedural experts in literary interpretation, for novice groups including U.S. Air Force recruits and undergraduates, and for both history texts and literary texts. These results were consistent with our hypothesis that the quality of a person's prior knowledge determines the harmony and settling rates of their representations and that these can be measured by simulating the spread of activation trough the person's mental representation of a subject matter domain. Harmony may also be used as a metacognitive signal. In these studies, we investigated the quality of mental representations. To do this, first we measured each subject's mental representation for a domain, and then we