A “word length effect”for sign language: Further evidence for the role of language in structuring working memory

We report a sign length effect in deaf users of American Sign Language that is analogous to the word length effect for speech. Lists containing long signs (signs that traverse relatively long distances) produced poorer memory performance than did lists of short signs (signs that do not change in location). Further, this length effect was eliminated by articulatory suppression (repetitive motion of the hands), and articulatory suppression produced an overall drop in performance. The pattern of results, together with previous findings (Wilson & Emmorey, 1997), provides evidence for a working memory system for sign language that consists of a phonological storage buffer and an articulatory rehearsal mechanism. This indicates a close equivalence of structure between working memory for sign language and working memory for speech. The implications of this equivalence are discussed.     

Issues surrounding the cognitive neuroscience of obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) has been studied extensively in recent years, with increased emphasis on understanding OCD’s biological substrates. There has been significant progress in documenting abnormal brain function in OCD patients, particularly in the orbitofrontal cortex, basal ganglia, and thalamus. Similar progress has broadened our understanding of the cognitive and behavioral manifestations of the disorder, including deficits in set shifting, hyperattention, and visuospatial construction abilities. Unfortunately, these results have not been replicated consistently. This report comprises a review of previous attempts to characterize the neurobiology and neuropsychology of OCD, and a discussion of several factors in OCD research that can help to explain previous inconsistencies.     

A new EOG-based eyeblink detection algorithm

Accurate and efficient operator functional state classification and assessment based on physiological data have many important applications ranging from operator monitoring to interaction and control of human/machine systems. Eyeblink characteristics are frequently used as physiological indicators for this purpose. In this paper, we describe an efficient and robust eyeblink detection algorithm based on nonlinear analysis of the electrooculogram (EOG) signal. The performance of the algorithm was evaluated via data analysis results of several benchmark test sets in comparison with another eyeblink detection algorithm.