This paper examines relevant writings of Howard McClusky and his power-load-margin (PLM) formula as the nucleus for a teaching-learning model. A teaching model is defined as a pattern or plan which can be used to shape a curriculum or course, to select instructional materials, and to guide a teacher's action. The seven concepts utilized by Joyce and Weil in Models of Teaching serve as the framework for the PLM model. The PLM teaching model fosters mutual respect, shared responsibility, and a spirit of mutual inquiry in small groups or individualized learning experiences. Four phases form the syntax of the PLM model: exploration of the PLM formula, clarification of value set, determination of a sense of direction, and implementation of the educational objectives. Instructional values claimed as direct outcomes of the PLM model include: introduction to the PLM formula, content areas as defined by the learner, and self-enhancement and goal achievement. Nurturant values claimed as indirect outcomes from "living" in the PLM learning environment include: ability to cope with change, respect for the dignity of the person, and interpersonal warmth and affiliation.Joyce and Weil indicate that there are a wide range of options or approaches to creating environments for learning and that there is no one &dquo;cor-KEITH MAIN is
Word familiarity may affect magnocellular processes of word recognition. To explore this idea, we measured reading rate, speed-discrimination, and contrast detection thresholds in adults and children with a wide range of reading abilities. We found that speed-discrimination thresholds are higher in children than in adults and are correlated with age. Speed discrimination thresholds are also correlated with reading rate, but only for words, not for pseudo-words. Conversely, we found no correlation between contrast sensitivity and reading rate and no correlation between speed discrimination thresholds WASI subtest scores. These findings support the position that reading rate is influenced by magnocellular circuitry attuned to the recognition of familiar word-forms.
Standard MRI methods are often inadequate for identifying mild traumatic brain injury (TBI). Advances in diffusion tensor imaging now provide potential biomarkers of TBI among white matter fascicles (tracts). However, it is still unclear which tracts are most pertinent to TBI diagnosis. This study ranked fiber tracts on their ability to discriminate patients with and without TBI. We acquired diffusion tensor imaging data from military veterans admitted to a polytrauma clinic (Overall n = 109; Age: M = 47.2, SD = 11.3; Male: 88%; TBI: 67%). TBI diagnosis was based on self-report and neurological examination. Fiber tractography analysis produced 20 fiber tracts per patient. Each tract yielded four clinically relevant measures (fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity). We applied receiver operating characteristic (ROC) analyses to identify the most diagnostic tract for each measure. The analyses produced an optimal cutpoint for each tract. We then used kappa coefficients to rate the agreement of each cutpoint with the neurologist's diagnosis. The tract with the highest kappa was most diagnostic. As a check on the ROC results, we performed a stepwise logistic regression on each measure using all 20 tracts as predictors. We also bootstrapped the ROC analyses to compute the 95% confidence intervals for sensitivity, specificity, and the highest kappa coefficients. The ROC analyses identified two fiber tracts as most diagnostic of TBI: the left cingulum (LCG) and the left inferior fronto-occipital fasciculus (LIF). Like ROC, logistic regression identified LCG as most predictive for the FA measure but identified the right anterior thalamic tract (RAT) for the MD, RD, and AD measures. These findings are potentially relevant to the development of TBI biomarkers. Our methods also demonstrate how ROC analysis may be used to identify clinically relevant variables in the TBI population.
Visual search may be affected by mirror-image symmetry between target and non-targets and also by switching the roles of target and non-target. Do different attention mechanisms underlie these two phenomena? Can a unifying explanation account for both? We conducted two experiments to decompose processing into component parts, and compared results to competing models' predictions. Mirror-image search was unimpaired after target discrimination had been balanced across search conditions-results were consistent with an unlimited-capacity, decision noise model. Search asymmetry affected higher-level processing, however, resulting in capacity limitations that necessitated serial processing. A unifying explanation can account for these two seemingly unrelated phenomena.
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