Thesis (Ph. D.)--University of Adelaide, Dept. of Psychology, 1994. / Includes bibliographical references (leaves 290-320).

Issued also as thesis - Univ. of Chicago. / Also available on the Internet.

Thesis (M.S.)--University of Missouri-Columbia, 2003. / Typescript. Vita. Includes bibliographical references (leaves 68-69). Also available on the Internet.

Thesis (M.S.)--University of Missouri-Columbia, 2003. / Typescript. Vita. Includes bibliographical references (leaves 68-69). Also available on the Internet.

Spatial relationalleaming can be defined as the use of the spatial (geometric) relationship between two or more cues (landmarks) in order to locate additional points in space (O'Keefe and Nadel, 1979). An internal spatial representation enables an animal to compute novel locations and travel routes from familiar landmarks and routes (Dyer, 1993). A spatial representation is an internal construct mediating between perceived stimuli in the environment and the behaviour of the animal (Tolman, 1948). In this type of spatial representation the information encoded must be isomorphic with the physical environment such that the geometric relations of distance, angle and direction are maintained or can be computed from the stored information (Gallistel, 1990). A series of spatial and foraging task experiments were conducted to investigate the utilisation of spatial relational learning as a spatial strategy available to cotton-top tamarins (Sag uinus oedipus oedipus). The apparatus used was an 8x8 matrix of holes set in an upright wooden board to allow for the manipulation of visual cues and hidden food items such that the spatial configuration of cues and food could be transformed (translated or rotated) with respect to the perimeter of the board. The definitive test of spatial relational learning was whether the monkeys relied upon the spatial relationship between the visual cues to locate the position of the hidden food items. In a control experiment testing for differential use of perceptual information the results showed that if given the choice, tamarins relied on visual over olfactory cues in a foraging task. Callitrichids typically depend on olfactory communication in socio-sexual contexts so it was unusual that olfaction did not also play a significant role in foraging. In the first spatial learning experiment, the tamarins were found to rely on the three visually presented cues to locate the eleven hidden food items. However, their performance was not very accurate. In the next experiment the task was simplified so that the types of spatial strategies the monkeys were using to solve the foraging task could be clearly identified. In this experiment, only two visual cues were presented on either end of a line of four hidden food items. Once the monkeys were trained to these cues, the cues and food were translated and/or rotated on the board. Data from the beginning and middle of each testing session were used in the final analysis: in a previous analysis it was found that the monkeys initially searched the baited holes in the beginning of a testing session and thereafter predominantly searched unbaited holes. This suggests that they followed a win-stay/lose-shift foraging strategy, a finding that is supported by other studies of tamarins in captivity (Menzel and Juno, 1982) and the wild (Garber, 1989). The results also showed that the monkeys were searching predominately between the cues and not outside or around of them, indicating that they were locating the hidden food by using the spatial relationship between the visual cues. This provides evidence for the utilisation of spatial relational learning as a foraging strategy by cotton-top tamarins and the existence of complex internal spatial representations. Further studies are suggested to test captive monkeys' spatial relational capabilities and their foraging strategies. In addition, comparative and field studies are outlined that would provide information regarding New World monkeys' spatial learning abilities, neurophysiological organisation and the evolution of complex computational processes.

The purpose of this dissertation is to apply behavior learning concepts to incomplete-information continuous time games. Realistic game scenarios are often incomplete-information games in which the players withhold information. A player may not know its opponent’s objectives and strategies prior to the start of the game. This lack of information can limit the player’s ability to play optimally. If the player can observe the opponent’s actions, it can better optimize its achievements by taking corrective actions. In this research, a framework to learn an opponent’s behavior and take corrective actions is developed. The framework will allow a player to observe the opponent’s actions and formulate behavior models. The developed behavior model can then be utilized to find the best actions for the player that optimizes the player’s objective function. In addition, the framework proposes that the player plays a safe strategy at the beginning of the game. A safe strategy is defined in this research as a strategy that guarantees a minimum pay-off to the player independent of the other player’s actions. During the initial part of the game, the player will play the safe strategy until it learns the opponent’s behavior. Two methods to develop behavior models that differ in the formulation of the behavior model are proposed. The first method is the Cost-Strategy Recognition (CSR) method in which the player formulates an objective function and a strategy for the opponent. The opponent is presumed to be rational and therefore will play to optimize its objective function. The strategy of the opponent is dependent on the information available to the opponent about other players in the game. A strategy formulation presumes a certain level of information available to the opponent. The previous observations of the opponent’s actions are used to estimate the parameters of the formulated behavior model. The estimated behavior model predicts the opponent’s future actions. The second method is the Direct Approximation of Value Function (DAVF) method. In this method, unlike the CSR method, the player formulates an objective function for the opponent but does not formulates a strategy directly; rather, indirectly the player assumes that the opponent is playing optimally. Thus, a value function satisfying the HJB equation corresponding to the opponent’s cost function exists. The DAVF method finds an approximate solution for the value function based on previous observations of the opponent’s control. The approximate solution to the value function is then used to predict the opponent’s future behavior. Game examples in which only a single player is learning its opponent’s behavior are simulated. Subsequently, examples in which both players in a two-player game are learning each other’s behavior are simulated. In the second part of this research, a reorientation control maneuver for a spinning spacecraft will be developed. This will aid the application of behavior learning and differential games concepts to the specific scenario involving multiple spinning spacecraft. An impulsive reorientation maneuver with coasting will be analytically designed to reorient the spin axis of the spacecraft using a single body fixed thruster. Cooperative maneuvers of multiple spacecraft optimizing fuel and relative orientation will be designed. Pareto optimality concepts will be used to arrive at mutually agreeable reorientation maneuvers for the cooperating spinning spacecraft.

Thesis (Ph. D.)--Duke University, 2007.

Issued also as Thesis--University of Chicago.

Issued also as Thesis--University of Chicago.

Introduction: Children with a learning disability (LD) and/or Autism Spectrum Disorder (ASD) are known to suffer from significantly more sleep problems, anxiety and challenging behaviour (CB) than typically developing children, yet little is known about the relationships between these factors in the child LD/ASD population. Aims and Hypotheses: The aim of the current study was to examine the relationships between sleep problems, anxiety and CB in children with LD and/or ASD. It was hypothesised that there would be differences between levels of sleep problems, anxiety and CB in children with LD alone, LD and ASD, and ASD alone. It was further hypothesised that there would be significant positive correlations between the three factors and that sleep problems and anxiety would predict a significant amount of the variance in levels of CB. Method: Postal questionnaires were returned by parents of one hundred and sixty seven parents of children with LD and/or ASD. Questionnaires consisted of parental report measures of sleep problems, anxiety and CB, in addition to general demographic variables. Results and Discussion: Statistical analysis revealed no difference between groups (LD, LD+ASD, ASD) in relation to sleep problems, however, some differences were found between the groups in relation to anxiety and CB. Correlational analysis revealed significant positive associations between the three factors. A hierarchical multiple regression showed that medication, sleep problems and anxiety accounted for 42% of the variance in CB, with a large effect size. These findings suggest that the relationships between sleep, anxiety and CB found in the TD child and adult LD/ASD populations are also evident in the child LD/ASD population and that these relationships should be considered during clinical practice, particularly in the case of CB interventions where sleep problems and/or anxiety are also present.