Global ETD Search

51	Exploring The Development of Social Responses in Children with Callous and Unemotional Traits: An Examination of The Impact of Hypothesized Reinforcing and Aversive Stimuli Maharaj, Andre 28 March 2014 (has links) Callous and unemotional (CU) traits in children with conduct problems have been indicated as precursors to adult psychopathy. The analysis of the sensitivity to rewards and punishment in this population may be useful in the identification of effective behavior modification programs and particularly the delineation of ineffective punishment procedures. Scores on the Child Psychopathy Scale, Inventory of Callous and Unemotional Traits, Contingency Response Rating Scale and the Sensitivity to Reward Sensitivity to Punishment – Children Revised scale were used to evaluate 20 children, aged 7-13, recruited from FIU’s Center for Children and Families. The sample comprised 14 males and 6 females displaying a range of psychopathic traits measured by the CPS, with scores from 9 to 46 (M = 28.45, SD = 10.73). Sensitivity to punishment was examined using a behavioral task in which children endured various amounts of either white noise (type I punishment) or time-out from positive reinforcement (type II punishment) in order to gain access to a demonstrated reinforcer. The sample was stratified on the basis of the magnitude of psychopathy scores, and sensitivity to rewards and punishment were evaluated using a Behavioral Activation / Behavioral Inhibition framework by examining task performance: the frequency and duration of punishment conditions selected, electrodermal activity (skin conductance response), and parent-reported measures of child sensitivity to reward and punishment. Results indicated that the magnitude of CU traits was directly proportional to hyposensitivity to punishment and hypersensitivity to reward. Children with elevated levels of CU traits elected to endure a greater frequency and duration type I punishment in order to maintain continued access to the reinforcer. Significant differences were not found between high- and low-psychopathy children in the selection of type II punishment. The findings indicate that although there may be a hyporeactivity to type I punishment in children with CU traits, the use of a type II punishment by the removal of a positive stimulus has demonstrated treatment efficacy. The difference in sensitivity to rewards and the types of effective punishment in children with CU traits may affect reinforcement based learning, leading to the ineffectiveness of traditional methods informing the development of social responses. Callous and Unemotional Punishment Electrodermal Activity Aversive Stimuli Skin Response Psychopath Applied Behavior Analysis Child Psychology Clinical Psychology Cognitive Behavioral Therapy Developmental Psychology Experimental Analysis of Behavior Medicine and Health Sciences Psychiatric and Mental Health Psychiatry Psychology
52	Off-line and On-line Affective Recognition of a Computer User through A Biosignal Processing Approach Ren, Peng 29 March 2013 (has links) Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment (“relaxation” vs. “stress”) are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the “relaxation” vs. “stress” states. Affective Computing Autonomic Nervous System (ANS) Pupil Diameter (PD) Galvanic Skin Response (GSR) Wavelet denoising Kalman filtering Moving Average Window Backward Differentiation Mathematical Morphology
53	A theoretical basis for understanding and researching the relationship between music, stress, and biofeedback Wang, Frederick 13 July 2023 (has links) No description available. Clinical Psychology Music Psychology Psychobiology music rhythm tempo harmony music preference stress measurement biofeedback psychophysiology psycho-physiology heart rate variability HRV EMG electromyography skin conductance SCL galvanic skin response GSR electrodermal activity EDA research design
54	Enhanching the Human-Team Awareness of a Robot Wåhlin, Peter January 2012 (has links) The use of autonomous robots in our society is increasing every day and a robot is no longer seen as a tool but as a team member. The robots are now working side by side with us and provide assistance during dangerous operations where humans otherwise are at risk. This development has in turn increased the need of robots with more human-awareness. Therefore, this master thesis aims at contributing to the enhancement of human-aware robotics. Specifically, we are investigating the possibilities of equipping autonomous robots with the capability of assessing and detecting activities in human teams. This capability could, for instance, be used in the robot's reasoning and planning components to create better plans that ultimately would result in improved human-robot teamwork performance. we propose to improve existing teamwork activity recognizers by adding intangible features, such as stress, motivation and focus, originating from human behavior models. Hidden markov models have earlier been proven very efficient for activity recognition and have therefore been utilized in this work as a method for classification of behaviors. In order for a robot to provide effective assistance to a human team it must not only consider spatio-temporal parameters for team members but also the psychological.To assess psychological parameters this master thesis suggests to use the body signals of team members. Body signals such as heart rate and skin conductance. Combined with the body signals we investigate the possibility of using System Dynamics models to interpret the current psychological states of the human team members, thus enhancing the human-awareness of a robot. / Användningen av autonoma robotar i vårt samhälle ökar varje dag och en robot ses inte längre som ett verktyg utan som en gruppmedlem. Robotarna arbetar nu sida vid sida med oss och ger oss stöd under farliga arbeten där människor annars är utsatta för risker. Denna utveckling har i sin tur ökat behovet av robotar med mer människo-medvetenhet. Därför är målet med detta examensarbete att bidra till en stärkt människo-medvetenhet hos robotar. Specifikt undersöker vi möjligheterna att utrusta autonoma robotar med förmågan att bedöma och upptäcka olika beteenden hos mänskliga lag. Denna förmåga skulle till exempel kunna användas i robotens resonemang och planering för att ta beslut och i sin tur förbättra samarbetet mellan människa och robot. Vi föreslår att förbättra befintliga aktivitetsidentifierare genom att tillföra förmågan att tolka immateriella beteenden hos människan, såsom stress, motivation och fokus. Att kunna urskilja lagaktiviteter inom ett mänskligt lag är grundläggande för en robot som ska vara till stöd för laget. Dolda markovmodeller har tidigare visat sig vara mycket effektiva för just aktivitetsidentifiering och har därför använts i detta arbete. För att en robot ska kunna ha möjlighet att ge ett effektivt stöd till ett mänskligtlag måste den inte bara ta hänsyn till rumsliga parametrar hos lagmedlemmarna utan även de psykologiska. För att tyda psykologiska parametrar hos människor förespråkar denna masteravhandling utnyttjandet av mänskliga kroppssignaler. Signaler så som hjärtfrekvens och hudkonduktans. Kombinerat med kroppenssignalerar påvisar vi möjligheten att använda systemdynamiksmodeller för att tolka immateriella beteenden, vilket i sin tur kan stärka människo-medvetenheten hos en robot. / <p>The thesis work was conducted in Stockholm, Kista at the department of Informatics and Aero System at Swedish Defence Research Agency.</p> robotics Artificial Intelligence AI System Dynamics Wireless Body Area Network WBAN sensors Psychology physiology human behavior Human-Robot-Interaction HRI robot Human-Team awareness human-robot RapidMiner Vensim JaHMM hidden markov models HMM eclipse rcp ARFF recognizer classification cross-validation human behavior stress detection Galvanic Skin Response Heart Rate Skin Temperature Finger Temperature Electrocardiogram ECG GSR ST Pupil Diameter PD Patient monitoring GPS Data acquisition dataset data set MODERE Yerkes-Dodson SD model SD Confusion matrix spatial MOUT Java Agent robotic agent Machine Learning activity recognition team oriented robot Evaluation Decoding Learning Forward Algorithm Backward Algorithm Baum-Welch feedback loop Situation Awareness SA Shared Mental Models SMM

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