The present study used a Microsoft Visual Basic computer program to examine the effects of changing reinforcement schedule components on response allocation following previously extinguished responding. In Experiment 1, participants allocated responses to three different colored buttons that moved around the screen after each successive click. Components were arranged such that clicking on one button resulted in reinforcer delivery on a programmed variable-interval (VI) 10 s schedule while clicking either of the other two buttons did not result in programmed reinforcer deliveries. Results of Experiment 1 may have been confounded by an unintended signaling of component changes, so an identical experiment was repeated without a point counter visible to the participants. The results of Experiment 2 indicated an induced responding on the button most recently associated with reinforcement when the reinforcement schedule changed. This induction effect is discussed in relation to current conceptions of relapse effects in the scientific literature and implications for treatment of challenging behavior.

The present study sought to compare the relationship between the generalization skills and performance on a standardized IQ assessment on 30 individuals with developmental or intellectual disabilities (73% had a diagnosis of autism). Participants’ generalization skills were tested using the Promoting the Emergence of Advanced Knowledge Generalization Module (PEAK-G), and IQ was assessed using either the WISC-IV Short Form assessment or the WPPSI-III Short Form assessment. The data indicated a strong, significant correlation between scores on the PEAK-G and IQ using both Raw IQ (r = .839, p > .01) and Full Scale IQ (r = .628, p > .01). Both Raw IQ and Full Scale IQ were further analyzed by comparing them each to the three subtests of the PEAK-G (Foundational Learning and Basic Social Skills, Verbal Comprehension, Memory and Advanced Social Skills and Verbal Reasoning, Problem Solving, and Advanced Mathematical Skills). The results help to provide a better understanding of how closely participants’ IQ scores correlate to their PEAK-G scores.

Thesis (Ph.D.)--Boston University / The purpose of this experiment was to investigate the relationship between stimulus generalization ani the variables of stress and defense style. Accordingly, performance on an auditory stimulus generalization task under no stress and stress conditions was studied among a group of college students. Half of the group was composed of subjects who were classified as repressors while the other half consisted of subjects classified as intellectualizers. Predictions were generated from research related to a) the relationship between stress induced by a noxious stimulus and performance on stimulus generalization tasks and b) the effectiveness of defense styles in the management of anxiety aroused by a stressful situation.

The next generation of artificial intelligence is based on human knowledge and experience that can assist the evolution of artificial intelligence towards learning the capability of planning and reasoning. Although knowledge collection and organiza- tion have achieved tremendous progress, it is non-trivial to construct a comprehen- sive knowledge graph due to different data sources, various construction methods, and alternate entity surface forms. The difficulty motivates the study of knowledge association. Knowledge association has attracted the attention of researchers, and some solutions have been proposed to resolve the problem, yet these current solutions of knowledge association still suffer from two primary shortages, i.e., generalization and robustness. Specifically, most knowledge association methods require a sufficient number of labeled data and ignore the effective exploration and utilization of complex relationships between entities. Besides, prevailing approaches rely on clean labeled data as the training set, making the model vulnerable to noises in the given labeled data. These drawbacks motivate the research on generalization and robustness of knowledge association in this dissertation. This dissertation explores two kinds of knowledge association tasks, i.e., entity alignment and entity synonym discovery, and makes innovative contributions to ad- dress the above drawbacks. First, semi-supervised entity alignment frameworks, which take advantage of both labeled with unlabeled entities, are proposed. One em- ploys an entity-level loss that is based on the cycle-consistency translation loss, and another one dually minimizes both entity-level and group-level loss by utilizing opti- mal transport theory to ease the strict constraint imposed by the cycle-consistency loss and match the whole picture of labeled and unlabeled data in different data sources. Second, robust entity alignment methods are proposed to solve the draw- back of robustness. One is designed by following adversarial training principle and leveraging graph neural network, and is optimized by a unified reinforced training strategy to combine its two components, i.e., noise detection and noise-aware entity alignment. Another one resorts to non-sampling and curriculum learning to address the negative sampling issue and the positive data selection issue remaining in the previous method. Lastly, a set-aware entity synonym discovery model that enables a flexible receptive field by making a breakthrough in using entity synonym set informa- tion is proposed to explore the complex relationship between entities. The contextual information of entities and entity synonym sets are arranged by a two-level network from which both of them can be mapped into the same space to facilitate synonym discovery by encoding the high-order contexts from flexible receptive fields.

No description available.

No description available.

No description available.

Matrix training techniques arrange instruction for stimulus relations that facilitate emergent responding to novel stimulus arrangements, which is a phenomenon known as recombinative generalization. The current study compared two common matrix training approaches, an overlapping (OV) design and a non-overlapping (NOV) design, with respect to arranging relations targeted for training. Two, typically-developing toddlers were taught compound action-object labels in either an OV or NOV matrix training design. Results suggest that an OV matrix design facilitates recombinative generalization more effectively than a NOV design.

Neural networks can be looked as a kind of intruments which is able to learn. For making the fruitful results of neural networks' learning possess parctical applied value, the thesis makes use of linear regression technics to strengthen the extended capability of RBF neural networks. The thesis researches the training methods of RBF neural networks, and retains the frame of OLS(orthogonal least square) learning rules which is published by Chen and Billings in 1992. Besides, aiming at the RBF's characteristics, the thesis brings up improved learning rules in first and second phases, and uses " early stop" to be the condition of training ceasing. To sum up, chiefly the thesis applies some technics of statistic linear regression to strenthen the extended capability of RBF, and using different methods to do computer simulation in different noise situations.

Thesis (Ed. D.)--West Virginia University, 2005. / Title from document title page. Document formatted into pages; contains xiii, 111 p. : ill. Includes abstract. Includes bibliographical references (p. 103-107).