Adaptive behavior refers to an individual's independent display of behaviors associated with meeting his or her daily personal and social needs, including behaviors expected in domestic and social environments (Nihira, Leland, & Lambert, 1993). The behaviors that make up the construct of adaptive behavior have a pervasive effect on people's quality of life, including the ability to function independently at school, work, and home and in the community. This chapter describes adaptive behavior, traces its history, and summarizes professional and legal standards that govern its assessment and use. 1 Considerable attention is given to theories of adaptive skill development in children from birth through age 5 and to developmental data derived from three standardized tests. Research on the impact of intellectual and other disabling conditions on adaptive behavior is summarized. General guidelines for the assessment of adaptive behavior are discussed, followed by a review of three comprehensive measures of adaptive behavior. GENERAL DESCRIPTION AND HISTORY OF ADAPTIVE BEHAVIORThink for a moment of the adaptive skills you may have displayed at one time or another today. After arising, you may have bathed, dressed, eaten, taken vitamins or other medications, and planned your day. You may then have communicated and socialized with others; used your previous schoolacquired knowledge; worked at home or elsewhere; cared for your home, family members, and office; and used community resources (e.g., taxis, buses, post office). These combined adaptive skills are both time-tested and universal indicators of how well you take personal responsibility for your welfare and engage your environment.These and other adaptive behaviors have historically been used to judge people's applied intelligence or ability to adapt to their environment. The ancient Greek civilization may have been the first to formally consider diminished adaptive behavior to reflect mental retardation 2 -a tradition that continues and has been formalized as part of the diagnostic Sara S. Sparrow, PhD, professor emerita of psychology and chief psychologist at Yale University's Child Study Center from 1977 to 2002, was to write this chapter. Sadly, she passed away on June 10, 2010, after a long illness. Sparrow was the author of more than 100 articles and chapters on psychological assessments and developmental disabilities and was senior author of the Vineland Adaptive Behavior Scales. Her research focused on the assessment of adaptive behavior, child neuropsychology, and developmental disabilities across a wide range of diagnostic groups of children and also across cultures. Psychology has lost a very able scholar who contributed much to the understanding of children and youth, including those with autism spectrum disorders, intellectual disability, and emotional disorders and gifted children. She was active for decades in the training of mental health professionals at the doctoral and postdoctoral levels.1 See Oakland and Harrison (2008) for a more complete discus...

High risk professions, such as pilots, police officers, and TSA agents, require sustained vigilance over long periods of time and/or under conditions of little sleep. This can lead to performance impairment in occupational tasks. Predicting impaired states before performance decrement manifests is critical to prevent costly and damaging mistakes. We hypothesize that machine learning models developed to analyze indices of eye and face tracking technologies can accurately predict impaired states. To test this we trained 12 types of machine learning algorithms using five methods of feature selection with indices of eye and face tracking to predict the performance of individual subjects during a psychomotor vigilance task completed at 2-h intervals during a 25-h sleep deprivation protocol. Our results show that (1) indices of eye and face tracking are sensitive to physiological and behavioral changes concomitant with impairment; (2) methods of feature selection heavily influence classification performance of machine learning algorithms; and (3) machine learning models using indices of eye and face tracking can correctly predict whether an individual's performance is "normal" or "impaired" with an accuracy up to 81.6%. These methods can be used to develop machine learning based systems intended to prevent operational mishaps due to sleep deprivation by predicting operator impairment, using indices of eye and face tracking.

We have performed a direct comparison between facial features obtained from a webcam and vigilance-task performance during prolonged wakefulness. Prolonged wakefulness deteriorates working performance due to changes in cognition, emotion, and by delayed response. Facial features can be potentially collected everywhere using webcams located in the workplace. If this type of device can obtain relevant information to predict performance deterioration, this technology can potentially reduce serious accidents and fatality. We extracted 34 facial indices, including head movements, facial expressions, and perceived facial emotions from 20 participants undergoing the psychomotor vigilance task (PVT) over 25 hours. We studied the correlation between facial indices and the performance indices derived from PVT, and evaluated the feasibility of facial indices as detectors of diminished reaction time during the PVT. Furthermore, we tested the feasibility of classifying performance as normal or impaired using several machine learning algorithms with correlated facial indices. Twenty-one indices were found significantly correlated with PVT indices. Pitch, from the head movement indices, and four perceived facial emotions-anger, surprise, sadness, and disgust-exhibited significant correlations with indices of performance. The eye-related facial expression indices showed especially strong correlation and higher feasibility of facial indices as classifiers. Significantly correlated indices were shown to explain more variance than the other indices for most of the classifiers. The facial indices obtained from a webcam strongly correlate with working performance during 25 hours of prolonged wakefulness.

The "cocktail party effect" refers to the ability of human listeners to separate the acoustic signal reaching their ears into its individual components, corresponding to individual sound sources in the environment. Despite this phenomenon appearing trivial for humans, implementing the cocktail party effect computationally remains an ambitious challenge. The approach used in this paper takes inspiration from human strategies for separating an acoustic environment into distinct perceptual auditory streams. A series of time-frequency-based features, analogous to those thought to emerge at various stages in the human auditory processing pathway, are derived from biaural auditory inputs. These feature vectors are used as inputs to an unsupervised cluster analysis used to group feature values that are assumed to correspond to the same object. Reconstructed auditory streams are then correlated to the original components used to create the auditory scene. Our model is capable of reconstructing streams that correlate to the original components (r = 0.3-0.7) used to create the complex auditory scene. The success of the reconstructions is largely dependent on the signal-to-noise ratio of the components of the auditory scene.

Introduction The Naval Submarine Medical Research Laboratory (NSMRL) is developing predictive models to examine how non-invasive, non-disruptive physiological monitoring can be used to track performance decrements due to sleep deficiency. Utilizing biometrics extracted from physiological measures to track performance changes would allow for automated tracking of fatigue and alleviate the overhead necessary to monitor individual schedules and sleep patterns. Methods NSMRL collaborated with the University of Connecticut to run a sleep deprivation study that deprived 20 participants of sleep for a period of up to 25 hours. During this time, subjects completed multiple tasks, including the Psychomotor Vigilance Test (PVT) every few hours. A non-invasive monitoring system collected physiological data from participants, which includes eye tracking, electrocardiography, electrodermal activity, and facial tracking (e.g., blink metrics, heart rate variability, skin conductance levels, facial action units). Using this multimodal approach, biometrics were extracted and evaluated to determine their predictive power on PVT performance. Multiple linear regression, using predictors selected via sequential forward selection, was used to develop a model of performance at an individual level based on a subset of these metrics chosen using principal component regression. Results Thirty-eight biometrics were extracted from the collected data and used to produce a predictive model of PVT performance. Sequential forward selection was used to select 11 primary biometrics. The criteria for primary metric inclusion in the model was minimization of root mean squared error. The resultant model had a correlation coefficient (r) of 0.71 (p < 0.001) with a root mean squared error (RMSE) of 49.8 ms between the predicted reaction time and true reaction time for each subject. Conclusion Non-invasive, non-disruptive monitoring could be used to track individual cognitive performance decrement due to sleep deficiency. This study examined the capability of combining the data from four physiological monitors that can be contained within a wrist worn device and a desk or helmet mounted camera. Utilizing 11 biometrics obtained from these monitors a stepwise regression model was developed that significantly correlates with PVT reaction time at both an individual and group level. Support This work was supported by the Military Operational Medicine Research Program.