Advisor(s)
Richard H. Melloni
Contributor(s)
Daniel F. Connor, Carey E. Priebe, Adam J. Reeves, James R. Stellar
Date of Award
2008
Date Accepted
7-2008
Degree Grantor
Northeastern University
Degree Level
Ph.D.
Degree Name
Doctor of Philosophy
Department or Academic Unit
College of Arts and Sciences. Department of Psychology
Keywords
Psychology, Aggression, Reactive aggression, Neural networks, Machine learning, Pattern recognition
Subject Categories
Aggressiveness, Aggressiveness in youth, Aggressiveness in adolescence, Conduct disorders in adolescence, Violence in adolescence
Disciplines
Psychology
Abstract
Maladaptive aggression is a serious, growing, and ill-understood problem for today's society. This is due, at least in part, to a lack of knowledge regarding how economic, social, environmental, and/or psychiatric factors in?uence the incidence of maladaptive aggression at the individual patient level. Standard statistics have teased out the etiological factors that correlate with the incidence of maladaptive aggression in the population as a whole, but have proven ine?ective at predicting which patients will display maladaptive aggression and which will not. This failing is likely due to the high number of interactions implicated in the development of maladaptive aggression, the heterogeneous nature of maladaptive aggression, a distinct lack of adequate data sets, or some combination thereof. Thus, the most comprehensive data set on maladaptive aggression available to date was examined with a variety of techniques to overcome some of the di?culties inherent in predicting maladaptive aggression. The techniques employed were: adapted standard statistics, statistical pattern recognition, machine learning, and a suite of novel predictive analysis tools developed during the process of this dissertation. The results of this investigation provide a method capable of illuminating the complex causes and correlates of maladaptive aggression with both expected and unexpected factors implicated by the current data set. Notably, this method is easily adapted for use with other data sets and a broad range of predictive problems, especially the investigation of mental illnesses.
Document Type
Dissertation
Rights Information
Copyright 2008
Rights Holder
Glen Anthony Coppersmith
Permanent URL
Recommended Citation
Coppersmith, Glen Anthony, "A computational investigation into maladaptive aggression" (2008). Psychology Dissertations. Paper 1. http://hdl.handle.net/2047/d10016375
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