Social skill deficits remain a defining feature of autism. One method to explain social behavior in autism is to explore specific antecedent-response relations. People with autism do not attend to social cues as readily as their typically developing peers thereby missing important cues that guide behavior during social interactions. The current study explored how children with autism learn antecedent-response relations using social and nonsocial stimuli as cues for reinforcement. A computerized task comprised pictures of social and non-social stimuli were presented on a computer screen. Participants were asked to respond to each picture by pressing a button if they thought pressing the button in the presence of the picture would earn them a reinforcer or to withhold pressing if they thought the picture would not earn them a reinforcer. Neither typically-developing children nor children diagnosed with ASD were able to reliably discriminate pictures. Developmental implications of these findings are discussed. / October 2015

No description available.

No description available.

A computerized program was designed to test whether arranging a common element in two, otherwise independent, 2-term correlations (stimulus-stimulus and response-stimulus) would result in emergent simple discriminative-stimulus properties for the antecedent stimulus relative to an arrangement with no common elements programmed. Data from 8 adult participants in this experiment indicate that common element arrangements led to relatively high rates of responding in the presence of the putative discriminative stimulus and relatively low rates or no responding in the presence of the putative s-delta during testing in extinction. Conversely, the uncommon element arrangements produced no clear discriminative control. The current data reflect a comparison of arrangements across subjects. These data support Sidman's (2000) suggestion that common elements among contingencies are sufficient to produce stimulus classes and cause class mergers. The data also have implications for thinking about the mechanism by which and the conditions under which discriminative control develops. Finally, these data have the potential to inform the programming and implementation of reinforcement contingencies in applied settings.

The purpose of this thesis is to determine what effects a dietary magnesium deficiency has on the discriminative avoidance behavior of rats. Three experiments were conducted. Experiment I compared two groups to determine the effects of magnesium deficiency on bar-press discriminative avoidance behavior. The results of Experiment l clearly illustrated that rats fed n diet deficient in magnesium began to lose their discriminative avoidance behavior after approximately five days with a steady decrease in performance over the remaining five days. Experiment II used a single subject design in an attempt to replicate Experiment I and to determine whether or not the magnesium deficiency effect could be reversed. Blood samples of serum magnesium for each rat were taken daily. The results confirmed Experiment I. A magnesium deficiency did cause a decrease in the performance of discriminative bar-press avoidance. Two of the four rats responded to the rehabilitation treatment with a corresponding increase in avoidance behavior with an increase in serum magnesium levels. The other two rats did not recover avoidance performance with rehabilitation, but did improve with regard to other behavioral measurements. Experiment III again employed two groups of rats in an attempt to determine the effects of a magnesium deficiency upon acquisition of a discriminative shuttle box avoidance performance. A pilot study to Experiment III showed a clear effect with normal controls displaying statistically more avoidance responses than the experimentals who received subnormal levels of magnesium. The results from Experiment III however showed no statistically significant difference between the controls and experimentals even though there was a statistical difference in serum magnesium concentration.

This thesis was concerned with the effects of shock intensity on discriminative escape conditioning. At the lowest shock intensity there was a bimodal distribution of nonresponding animals at one mode and responders at the other mode. Optimum performance occurred at the lowest shock intensity at which 100% of the animals responded. At higher shock intensities performance deteriorated. An attempt to test the Yerkes-Dodson Law failed to yield conclusive results. Finally, it was found that shock intensity affected performance rather than learning. / Thesis / Doctor of Philosophy (PhD)

Yes / Gait is a unique non-invasive biometric form that can be utilized to effectively recognize persons, even when they prove to be uncooperative. Computer-aided gait recognition systems usually use image sequences without considering covariates like clothing and possessions of carrier bags whilst on the move. Similarly, in gait recognition, there may exist unknown covariate conditions that may affect the training and testing conditions for a given individual. Consequently, common techniques for gait recognition and measurement require a degree of intervention leading to the introduction of unknown covariate conditions, and hence this significantly limits the practical use of the present gait recognition and analysis systems. To overcome these key issues, we propose a method of gait analysis accounting for both known and unknown covariate conditions. For this purpose, we propose two methods, i.e., a Convolutional Neural Network (CNN) based gait recognition and a discriminative features-based classification method for unknown covariate conditions. The first method can handle known covariate conditions efficiently while the second method focuses on identifying and selecting unique covariate invariant features from the gallery and probe sequences. The feature set utilized here includes Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), and Haralick texture features. Furthermore, we utilize the Fisher Linear Discriminant Analysis for dimensionality reduction and selecting the most discriminant features. Three classifiers, namely Random Forest, Support Vector Machine (SVM), and Multilayer Perceptron are used for gait recognition under strict unknown covariate conditions. We evaluated our results using CASIA and OUR-ISIR datasets for both clothing and speed variations. As a result, we report that on average we obtain an accuracy of 90.32% for the CASIA dataset with unknown covariates and similarly performed excellently on the ISIR dataset. Therefore, our proposed method outperforms existing methods for gait recognition under known and unknown covariate conditions. / This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1F1A1060668).

No description available.

Este trabalho de doutorado apresenta duas inovações: a Discriminative Paraconsistent Machine (DPM), que consiste em um novo classificador elaborado com base na lógica paraconsistente anotada (LPA) e a aplicação da DPM para a identificação de patologias na laringe, por meio de exames nos sinais de voz de um locutor. Não há relatos na literatura sobre o uso da LPA para construção de um classificador e sobre suas aplicações para a finalidade proposta. Os resultados obtidos são motivadores, indicando um avanço na área. / This PhD thesis presents two novelties: the Discriminative Paraconsistent Machine (DPM), which is a new classifier built on the basis of the annotated paraconsistent logic (APL), and the applications of DPM to identify larynx pathologies, by inspecting a voice signal. There is neither a comment on literature about the use of APL to built a classifier nor about its applications for the proposed application. The results obtained create motivation, showing a clear progress in the field.

Intensive Care Units (ICUs) host patients in critical condition who are being monitored by sensors which measure their vital signs. These vital signs carry information about a patient’s physiology and can have a very rich structure at fine resolution levels. The task of analysing these biosignals for the purposes of monitoring a patient’s physiology is referred to as physiological condition monitoring. Physiological condition monitoring of patients in ICUs is of critical importance as their health is subject to a number of events of interest. For the purposes of this thesis, the overall task of physiological condition monitoring is decomposed into the sub-tasks of modelling a patient’s physiology a) under the effect of physiological or artifactual events and b) under the effect of drug administration. The first sub-task is concerned with modelling artifact (such as the taking of blood samples, suction events etc.), and physiological episodes (such as bradycardia), while the second sub-task is focussed on modelling the effect of drug administration on a patient’s physiology. The first contribution of this thesis is the formulation, development and validation of the Discriminative Switching Linear Dynamical System (DSLDS) for the first sub-task. The DSLDS is a discriminative model which identifies the state-of-health of a patient given their observed vital signs using a discriminative probabilistic classifier, and then infers their underlying physiological values conditioned on this status. It is demonstrated on two real-world datasets that the DSLDS is able to outperform an alternative, generative approach in most cases of interest, and that an a-mixture of the two models achieves higher performance than either of the two models separately. The second contribution of this thesis is the formulation, development and validation of the Input-Output Non-Linear Dynamical System (IO-NLDS) for the second sub-task. The IO-NLDS is a non-linear dynamical system for modelling the effect of drug infusions on the vital signs of patients. More specifically, in this thesis the focus is on modelling the effect of the widely used anaesthetic drug Propofol on a patient’s monitored depth of anaesthesia and haemodynamics. A comparison of the IO-NLDS with a model derived from the Pharmacokinetics/Pharmacodynamics (PK/PD) literature on a real-world dataset shows that significant improvements in predictive performance can be provided without requiring the incorporation of expert physiological knowledge.