Re-Examining the Stop-Signal Task to Test Competing Theories of AD/HD

The current study tested two competing models of Attention-Deficit/Hyperactivity Disorder (AD/HD), the inhibition and state regulation theories, by conducting fine-grained analyses of the Stop-Signal Task and another putative measure of behavioral inhibition, the Gordon Continuous Performance Test (G-CPT), in a large sample of children and adolescents. The inhibition theory posits that performance on these tasks reflects increased difficulties for AD/HD participants to inhibit prepotent responses. The model predicts that putative stop-signal reaction time (SSRT) group differences on the Stop-Signal Task will be primarily related to AD/HD participants requiring more warning than control participants to inhibit to the stop-signal and emphasizes the relative importance of commission errors, particularly "impulsive" type commissions, over other error types on the G-CPT. The state regulation theory, on the other hand, proposes response variability due to difficulties maintaining an optimal state of arousal as the primary deficit in AD/HD. This model predicts that SSRT differences will be more attributable to slower and/or more variable reaction time (RT) in the AD/HD group, as opposed to reflecting inhibitory deficits. State regulation assumptions also emphasize the relative importance of omission errors and "slow processing" type commissions over other error types on the G-CPT. Overall, results of Stop-Signal Task analyses were more supportive of state regulation predictions and showed that greater response variability (i.e., SDRT) in the AD/HD group was not reducible to slow mean reaction time (MRT) and that response variability made a larger contribution to increased SSRT in the AD/HD group than inhibitory processes. Examined further, ex-Gaussian analyses of Stop-Signal Task go-trial RT distributions revealed that increased variability in the AD/HD group was not due solely to a few excessively long RTs in the tail of the AD/HD distribution (i.e., tau), but rather indicated the importance of response variability throughout AD/HD group performance on the Stop-Signal Task, as well as the notable sensitivity of ex-Gaussian analyses to variability in data screening procedures. Results of G-CPT analyses indicated some support for the inhibition model, although error type analyses failed to further differentiate the theories. Finally, inclusion of primary variables of interest in exploratory factor analysis with other neurocognitive predictors of AD/HD indicated response variability as a separable construct and further supported its role in Stop-Signal Task performance. Response variability did not, however, make a unique contribution to the prediction of AD/HD symptoms beyond measures of motor processing speed in multiple deficit regression analyses. Results have implications for the interpretation of the processes reflected in widely-used variables in the AD/HD literature, as well as for the theoretical understanding of AD/HD.

Human Motion Analysis Based on Sequential Modeling of Radar Signal and Stereo Image Features

Falls are one of the greatest threats to elderly health in their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be provided, by sending fall alarms to caregivers. Radar is an effective non-intrusive sensing modality which is well suited for this purpose, which can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. Micro-Doppler features are utilized in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition (MPD) for feature extraction and fall detection. The extracted features include MPD features and the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, the extracted features are used for training and testing hidden Markov models (HMM) in different falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections. The risk of falls increases sharply when the elderly or patients try to exit beds. Thus, if a bed exit can be detected at an early stage of this motion, the related injuries can be prevented with a high probability. To detect bed exit for fall prevention, the trajectory of head movements is used for recognize such human motion. A head detector is trained using the histogram of oriented gradient (HOG) features of the head and shoulder areas from recorded bed exit images. A data association algorithm is applied on the head detection results to eliminate head detection false alarms. Then the three dimensional (3D) head trajectories are constructed by matching scale-invariant feature transform (SIFT) keypoints in the detected head areas from both the left and right stereo images. The extracted 3D head trajectories are used for training and testing an HMM based classifier for recognizing bed exit activities. The results of the classifier are presented and discussed in the thesis, which demonstrates the effectiveness of the proposed stereo vision based bed exit detection approach.

How I Learned to Stop Worrying and Love the Bits: Archival Motivation in the Digital Age

Why do people become archivists? Historically (and anecdotally) it was a deep love of musty, old records that drew people to the profession. While there have been many other motivating forces that inspired would-be archivists, it is most often that one hears of people seeking jobs in archives for love of “the stuff,” as evidenced in Kate Thiemer’s blog post, Honest tips for wannabe archivists (2012). As a result of the continually advancing presence of digitized and born digital archival collections, the physical nature of archival “stuff” is changing. While there remains the physical imprint of digital information on floppy disks, CDs, DVDs, hard drives, and old computers; the aspects of these physical artifacts might not evoke the same visceral pull to the profession as musty, raspy, paper-based documents. In light of this shift in physical presentation of information, we are faced with the question: how does love of archival “stuff” translate to work in digital archives? What is and/or will be the pull to become a digital archivist? To answer these questions, we will perform a survey-based study where we will invite archivists who work with both traditional and digital archival material to answer questions related to the aspects of their work that inspired or motivated them to join the profession. What motivates people to become archivists? What aspects of digital archives do or can potentially motivate people to seek out a career as an archivist? What, if any, motivational factors for becoming a traditional archivist are the same as those for becoming a digital archivist? What, if any, motivational factors for becoming a traditional archivist are different from those for becoming a digital archivist? By answering these questions, we hope to expand the archival discussion on what it means to be an archivist in the digital age. What compelling intrinsic, evidential, or informational values are present in digital archival content that will draw professionals to the field? Are there other values inherent in digital content that are currently unexplored? In our poster, we will present our discussion of the topic, our survey design, and results we have at the time of the Institute. Thiemer, K. (2012). Honest tips for wannabe archivists. Archivesnext blog. Retrieved from http://www.archivesnext.com/?p=2849

Stop, Collaborate & Listen: How the Librarian/Publisher Relationship Can Facilitate the Development of the Information Literacy Curriculum

A librarian from the Florida Gulf Coast University (FGCU) and the Library Communications Manager at Taylor & Francis Group partnered to launch a collaborative information literacy pilot program focusing on assisting FGCU students and faculty navigate and understand the scholarly publishing process. This article describes how the idea was created, as well as steps involved in developing the publishing toolkit to help FGCU patrons. An overview of the pilot program was presented during the 2015 Charleston Conference as a poster session.