Factors predicting recommendations for civil commitment of sexually violent predators under Florida's Jimmy Ryce Act

This study investigated the factors considered by forensic examiners when evaluating sexually violent predators (SVP) for civil commitment under Florida's “Jimmy Ryce Act.” The project was funded by a pre-doctoral research grant awarded by the Association for the Treatment of Sexual Abusers (ATSA). ^ This study proposed two specific research questions. First, what is the direct relationship between actuarial risk assessment scores and recommendations for sex offender civil commitment? Second, which other variables are likely to influence SVP commitment decisions, and to what degree? The purpose of the study was to determine if risk assessment practices are evidence-based, and whether offenders selected for commitment meet statutory criteria. ^ The purposive sample of 450 SVPs was drawn from the population of sex offenders evaluated for civil commitment in Florida between July 1, 2000 and June 30, 2001. Data were extracted from SVP evaluations provided by the Florida Department of Children and Families. Using multivariate logistic regression, this correlational research design examined the relationship between the dependent variable, commitment decision, and several sets of independent variables. The independent variables were derived from a review of the literature, and were grouped conceptually according to their degree of correlation with sex offense recidivism. Independent variables included diagnoses, actuarial risk assessment scores, empirically validated static and dynamic risk factors, consensus based risk factors, evaluator characteristics, and demographics. This study investigated the degree to which the identified variables predicted civil commitment decisions. ^ Logistic regression results revealed that the statistically significant predictors of recommendations for sex offender civil commitment were actuarial risk assessment scores, diagnoses of Pedophilia and Paraphilia NOS, psychopathy, younger age of victim, and non-minority race. Discriminant function analysis confirmed that these variables correctly predicted commitment decisions in 90% of cases. ^ It appears that civil commitment evaluators in Florida used empirically-based assessment procedures, and did not make decisions that were heavily influenced by extraneous factors. SVPs recommended for commitment consistently met the criteria set forth by the U.S. Supreme Court in Hendricks v. Kansas (1997): they suffered from a mental abnormality predisposing them to sexual violence, and risk assessment determined that they were likely to reoffend. ^

A digital signal processing approach for affective sensing of a computer user through pupil diameter monitoring

Recent research has indicated that the pupil diameter (PD) in humans varies with their affective states. However, this signal has not been fully investigated for affective sensing purposes in human-computer interaction systems. This may be due to the dominant separate effect of the pupillary light reflex (PLR), which shrinks the pupil when light intensity increases. In this dissertation, an adaptive interference canceller (AIC) system using the H∞ time-varying (HITV) adaptive algorithm was developed to minimize the impact of the PLR on the measured pupil diameter signal. The modified pupil diameter (MPD) signal, obtained from the AIC was expected to reflect primarily the pupillary affective responses (PAR) of the subject. Additional manipulations of the AIC output resulted in a processed MPD (PMPD) signal, from which a classification feature, PMPDmean, was extracted. This feature was used to train and test a support vector machine (SVM), for the identification of stress states in the subject from whom the pupil diameter signal was recorded, achieving an accuracy rate of 77.78%. The advantages of affective recognition through the PD signal were verified by comparatively investigating the classification of stress and relaxation states through features derived from the simultaneously recorded galvanic skin response (GSR) and blood volume pulse (BVP) signals, with and without the PD feature. The discriminating potential of each individual feature extracted from GSR, BVP and PD was studied by analysis of its receiver operating characteristic (ROC) curve. The ROC curve found for the PMPDmean feature encompassed the largest area (0.8546) of all the single-feature ROCs investigated. The encouraging results seen in affective sensing based on pupil diameter monitoring were obtained in spite of intermittent illumination increases purposely introduced during the experiments. Therefore, these results confirmed the benefits of using the AIC implementation with the HITV adaptive algorithm to isolate the PAR and the potential of using PD monitoring to sense the evolving affective states of a computer user.

A Digital Signal Processing Approach for Affective Sensing of a Computer User through Pupil Diameter Monitoring

Recent research has indicated that the pupil diameter (PD) in humans varies with their affective states. However, this signal has not been fully investigated for affective sensing purposes in human-computer interaction systems. This may be due to the dominant separate effect of the pupillary light reflex (PLR), which shrinks the pupil when light intensity increases. In this dissertation, an adaptive interference canceller (AIC) system using the H∞ time-varying (HITV) adaptive algorithm was developed to minimize the impact of the PLR on the measured pupil diameter signal. The modified pupil diameter (MPD) signal, obtained from the AIC was expected to reflect primarily the pupillary affective responses (PAR) of the subject. Additional manipulations of the AIC output resulted in a processed MPD (PMPD) signal, from which a classification feature, PMPDmean, was extracted. This feature was used to train and test a support vector machine (SVM), for the identification of stress states in the subject from whom the pupil diameter signal was recorded, achieving an accuracy rate of 77.78%. The advantages of affective recognition through the PD signal were verified by comparatively investigating the classification of stress and relaxation states through features derived from the simultaneously recorded galvanic skin response (GSR) and blood volume pulse (BVP) signals, with and without the PD feature. The discriminating potential of each individual feature extracted from GSR, BVP and PD was studied by analysis of its receiver operating characteristic (ROC) curve. The ROC curve found for the PMPDmean feature encompassed the largest area (0.8546) of all the single-feature ROCs investigated. The encouraging results seen in affective sensing based on pupil diameter monitoring were obtained in spite of intermittent illumination increases purposely introduced during the experiments. Therefore, these results confirmed the benefits of using the AIC implementation with the HITV adaptive algorithm to isolate the PAR and the potential of using PD monitoring to sense the evolving affective states of a computer user.