Emergence of normative beliefs legitimizing antisocial behaviour in adolescents : the roles of monitoring, attachment, and temperament /

Beliefs about the rightness or wrongness of engaging in various antisocial acts, referred to here as nonnative beliefs legitimizing antisocial behaviour (nblab), have been shown to playa role in the emergence oflater antisocial behaviour. The current study represented an attempt to understand whether parental monitoring and parent-child attachment have differential relationships with these antisocial nonnative beliefs in adolescents of different temperaments. The participants, 7135 adolescents in 25 high schools (ages 10- 18 years, M = 15.7) completed a wide-ranging questionnaire as part of the broad Youth Lifestyle Choices - Community University Research Alliance project, whose goal is to identify and describe the major developmental pathways of risk behaviours and resilience in youth. Two aspects of monitoring (monitoring knowledge and surveillance/tracking), attachment security, and two measures of temperament (activity level and approach) were examined for main effects and in interactions as predictors of adolescent nonnative beliefs. All of these measures were based on adolescent self-ratings on either 3- or 4-point Likert-type scales. Several important results emerged from the study. Males were higher than females in nblab; parental monitoring knowledge and adolescent attachment security were negatively related to nblab; and temperamental activity level was positively related. Monitoring knowledge, the strongest of the predictors, was much more strongly related to nonnative beliefs than was parental surveillance/tracking, supporting the contention that it is how much parents actually know, and not their surveillance efforts, that predict adolescent nonnative beliefs. A surprising finding that is of the utmost importance was that, although several of the interactions tested were significant, none were considered to be of a meaningful magnitude (defined as sr^ > .01). The current study supported the suggestion that normative beliefs legitimizing antisocial behaviour are multiply determined, and the results were discussed with respect to the observed differential relations of parental monitoring, parent-child attachment, temperament, age, and gender to antisocial normative beliefs in adolescents. Also discussed were the need to test other parenting, temperament, and other variables that may be involved in the development of nblab; the need to directly test possible mechanisms explaining the links among the variables; and the usefulness of longitudinal research in determining possible directions of causality and developmental changes in the relationships.

From inference to affordance : the problem of visual depth-perception in the optical writings of Descartes, Berkeley, and Gibson

This thesis explores the debate and issues regarding the status of visual ;,iferellces in the optical writings of Rene Descartes, George Berkeley and James 1. Gibson. It gathers arguments from across their works and synthesizes an account of visual depthperception that accurately reflects the larger, metaphysical implications of their philosophical theories. Chapters 1 and 2 address the Cartesian and Berkelean theories of depth-perception, respectively. For Descartes and Berkeley the debate can be put in the following way: How is it possible that we experience objects as appearing outside of us, at various distances, if objects appear inside of us, in the representations of the individual's mind? Thus, the Descartes-Berkeley component of the debate takes place exclusively within a representationalist setting. Representational theories of depthperception are rooted in the scientific discovery that objects project a merely twodimensional patchwork of forms on the retina. I call this the "flat image" problem. This poses the problem of depth in terms of a difference between two- and three-dimensional orders (i.e., a gap to be bridged by one inferential procedure or another). Chapter 3 addresses Gibson's ecological response to the debate. Gibson argues that the perceiver cannot be flattened out into a passive, two-dimensional sensory surface. Perception is possible precisely because the body and the environment already have depth. Accordingly, the problem cannot be reduced to a gap between two- and threedimensional givens, a gap crossed with a projective geometry. The crucial difference is not one of a dimensional degree. Chapter 3 explores this theme and attempts to excavate the empirical and philosophical suppositions that lead Descartes and Berkeley to their respective theories of indirect perception. Gibson argues that the notion of visual inference, which is necessary to substantiate representational theories of indirect perception, is highly problematic. To elucidate this point, the thesis steps into the representationalist tradition, in order to show that problems that arise within it demand a tum toward Gibson's information-based doctrine of ecological specificity (which is to say, the theory of direct perception). Chapter 3 concludes with a careful examination of Gibsonian affordallces as the sole objects of direct perceptual experience. The final section provides an account of affordances that locates the moving, perceiving body at the heart of the experience of depth; an experience which emerges in the dynamical structures that cross the body and the world.

Automatic Inference of Graph Models for Complex Networks with Genetic Programming

Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.