<p> The role of verbal hypotheses in concept identification was explored by manipulating three variables affecting the relation between verbalized rules and classification performance. (i) Verbalizing rules before and after classification changed subjects' cue-sampling strategies and the control of verbal hypotheses over sorting performance. (ii) The difficulty of stimulus description affected how subjects utilized verbal hypotheses, and whether verbalized rules completely specified the cues used for classification. (iii) The number of irrelevant attributes changed the relative efficiency of stimulus-learning over rule-learning for concept identification.</p> <p> These investigations demonstrate effective techniques for varying and evaluating the importance of verbal rules for classification; and suggest that subjects' prior verbal habits markedly affect the degree of reliance placed on verbal hypotheses in concept attainment.</p> / Thesis / Doctor of Philosophy (PhD)

Abstraction is an inductive process through which specific details become united by a general concept. Abstraction incorporates two sub-skills: concept identification which involves recognizing patterns created by an external agent, and concept formation which is more difficult, requiring independent creation of a schema to organize information. Impairments in concept identification and formation are theorized to underlie a variety of practical difficulties of individuals with autism spectrum disorder (ASD; e.g., failure to generalize learning in one context to a similar, but new context). However, past research has yielded mixed results, with some finding significant impairment and others finding intact concept identification and formation. Contradictory findings may be due to differences in assessment methodology. We assessed concept identification and formation abilities using the Delis-Kaplan Executive Function System (D-KEFS) Sorting task. We hypothesized that (1) we would replicate previous findings of intact concept identification but impaired concept formation in individuals with ASD (Minshew et al., 2002); (2) impairments in concept formation would remain even after accounting for differences in IQ, working memory ability, and test anxiety; and (3) worse impairments would be associated with more severe autism symptoms. The sample consisted of 27 high-functioning (IQ > 80) adolescents with ASD and 27 age- (M 14.8 years) and IQ- (M 102.8) matched typically-developing controls. One-way ANOVAs explored group differences on task performance variables. As hypothesized, our sample demonstrated intact concept identification abilities, F(1, 52) = 2.90, p = 0.095, but impaired concept formation abilities, F(1, 52) = 6.53, p = 0.01. A linear regression analysis revealed that working memory ability and test anxiety were not significant predictors of concept formation abilities. After accounting for IQ in a regression model, our hypothesis was partly borne out in that individuals with ASD continued to show impairment in concept formation, yet at trend-level significance (p = 0.058). Two-tailed Pearson correlations revealed no significant correlations between a measure of autism symptomatology and concept formation or concept identification ability. Our findings add to a growing body of research showing a dissociation between concept identification and concept formation abilities in individuals with ASD. This dissociation existing at trend-level significance after statistically controlling for IQ suggests that it may exist across levels of cognitive functioning in ASD. Our finding that concept formation ability was not significantly associated with a measure autism symptomatology somewhat weakens the theoretical significance of concept formation deficits in ASD.

abstract: The complexity of the systems that software engineers build has continuously grown since the inception of the field. What has not changed is the engineers' mental capacity to operate on about seven distinct pieces of information at a time. The widespread use of UML has led to more abstract software design activities, however the same cannot be said for reverse engineering activities. The introduction of abstraction to reverse engineering will allow the engineer to move farther away from the details of the system, increasing his ability to see the role that domain level concepts play in the system. In this thesis, we present a technique that facilitates filtering of classes from existing systems at the source level based on their relationship to concepts in the domain via a classification method using machine learning. We showed that concepts can be identified using a machine learning classifier based on source level metrics. We developed an Eclipse plugin to assist with the process of manually classifying Java source code, and collecting metrics and classifications into a standard file format. We developed an Eclipse plugin to act as a concept identifier that visually indicates a class as a domain concept or not. We minimized the size of training sets to ensure a useful approach in practice. This allowed us to determine that a training set of 7:5 to 10% is nearly as effective as a training set representing 50% of the system. We showed that random selection is the most consistent and effective means of selecting a training set. We found that KNN is the most consistent performer among the learning algorithms tested. We determined the optimal feature set for this classification problem. We discussed two possible structures besides a one to one mapping of domain knowledge to implementation. We showed that classes representing more than one concept are simply concepts at differing levels of abstraction. We also discussed composite concepts representing a domain concept implemented by more than one class. We showed that these composite concepts are difficult to detect because the problem is NP-complete. / Dissertation/Thesis / Ph.D. Computer Science 2013

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