Regional cerebral volume loss associated with verbal learning and memory in dementia of the Alzheimer type.

Twenty-seven research participants with dementia of the Alzheimer type were studied with the California Verbal Learning Test (D. C. Delis, J. H. Kramer, E. Kaplan, & B. A. Ober, 1987) and standardized volume measures of the mesial temporal cortical gray matter, neocortical gray matter, thalamus, and caudate nuclei, from magnetic resonance imaging. A pattern of atrophic brain changes in the mesial temporal lobes (MTL) and the thalamus, with relatively less severe atrophy in the neocortical gray matter, was associated with poorer learning of the word list. Similar patterns of brain atrophy were observed for measures of delayed recall and recognition hits. However, for delayed recall, neither contribution was statistically significant, and for recognition hits, MTL was only at the trend level for significance. These results provide evidence that the verbal memory deficit of Alzheimer's disease (AD) is associated not only with the mesial temporal limbic cortex, thought to be the site of earliest and most severe pathology in AD, but also with damage in the thalamus. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Unexpected discoveries and S-invention of design requirements: important vehicles for a design process

Designers, during a conceptual design process, do not just synthesize solutions that satisfy initially given requirements, but also invent design issues or requirements that capture important aspects of the given problem. How do they do this? What becomes the impetus for the invention of important issues or requirements? So-called ‘unexpected discoveries', the acts of attending to visuo-spatial features in sketches which were not intended when they were drawn, are believed to contribute to it. The purpose of the present research is to verify this hypothesis. Analysing the cognitive processes of a practising architect in a design session, we found that in about a half of his entire design process there were bi-directional relations between unexpected discoveries and the invention of issues or requirements. Not only did unexpected discoveries become the driving force for the invention of issues or requirements, but also the occurrence of invention, in turn, tended to cause new unexpected discoveries. This has provided empirical evidence for two anecdotal views of designing. First, designing is a situated act; designers invent design issues or requirements in a way situated in the environment in which they design. We call inventions of this sort situated-invention (S-invention). Secondly, a design process progresses in such a way that the problem-space and the solution-space co-evolve. Further, this has brought a pedagogical implication as well as an insight about an important aspect of learning by experience in design.     

Block-processing adaptive filter for human eye movements

Adaptive processing techniques can be divided into two categories: block processing and recursive methods. With block processing methods, incoming data are divided into blocks, and each block is processed as a whole to estimate predictor coefficients. With recursive methods, predictor parameters are updated as each new data point becomes available and computed thorugh a set of recursive algorithms. In this paper, five block processing adaptive filters are used in the prediction of the human eye movements. They are two-point-linear predictor (TPLP), five-point-quadratic predictor (FPQP), nine-point-cubic predictor (NPCP), polynomial-filter predictor 1 (PFP1), which is a linear convex combination of a TPLP and an FPQP, and polynomial-filter predictor 2 (PFP2), which is a linear convex combination of a TPLP, as FPQP, and an NPCP. These predictors were tested with various signals such as saccadic eye movements, sinusoidal, cubic, triangular, and parabolic signals. The results show that the TPLP is the best predictor for triangular signal and the NPCP is the best predictor for sinusoidal signal. Conversely, the FPQP is the best predictor for parabolic and cubic signals. The results also suggest that the PFP1 and PFP2 show significant improvement over that of the TPLP, FPQP, and NPCP in long-range prediction.     

Organizational adoption of preemployment drug testing.

This study explored the adoption of preemployment drug testing by 360 organizations. Survival models were developed that included internal organizational and labor market factors hypothesized to affect the likelihood of adoption of drug testing. Also considered was another set of variables that included social and political variables based on institutional theory. An event history analysis using Cox regressions indicated that both internal organizational and environmental variables predicted adoption of drug testing. Results indicate that the higher the proportion of drug testers in the worksite's industry, the more likely it would be to adopt drug testing. Also, the extent to which an organization uses an internal labor market, voluntary turnover rate, and the extent to which management perceives drugs to be a problem were related to likelihood of adoption of drug testing. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Action as a Fast and Frugal Heuristic

Decision making is usually viewed as involving a period of thought, while the decision maker assesses options, their likely consequences, and his or her preferences, and selects the preferred option. The process ends in a terminating action. In this view errors of thought will inevitably show up as errors of action; costs of thinking are to be balanced against costs of decision errors. Fast and frugal heuristics research has shown that, in some environments, modest thought can lead to excellent action. In this paper we extend this work to situations in which action is taken after little or no thought. We show that these `highly active' or `decision cycles' processes can lead to excellent results at the cost of almost no thought. The paper examines the settings in which this effectiveness is possible, and lists a number of environmental features that are required for decision cycles to work well. Several research directions for analytical, laboratory, and field-based research are identified.     

Parent-child interactions during the initial weeks following brain injury in young children.

Objective: To understand how traumatic brain injury (TBI) affects parent-child interactions acutely following injury. Participants: Young children hospitalized for TBI (n = 80) and orthopedic injuries (OI; n = 113). Method: Raters coded videotaped interactions during free play and structured tasks for parental warmth/responsiveness and negativity and child warmth, behavior regulation, and cooperation. Raters also counted parental directives, critical/restricting statements, and scaffolds. Results: Parents of children with TBI exhibited less warm responsiveness and made more directive statements during a structured task than parents in the OI group. Children with TBI displayed less behavior regulation than children with OI. Parental warm responsiveness was more strongly related to child cooperativeness in the OI group than in the TBI group. Child behavior also mediated group differences in parental responsiveness and directiveness. TBI accounted for as much variance in parental behaviors as or more than did sociodemographic factors. Conclusion: TBI-related changes in child behavior may negatively influence parent-child interactions and disrupt the reciprocity between parent and child. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Factors explaining why people join, remain in, or leave a scholarly society: The Canadian Psychological Association.

Both theory and subject matter experts were used to generate items for a survey designed to increase understanding of why psychologists join, remain in, or leave the Canadian Psychological Association (CPA). The subject matter experts included current, former, and nonmember psychologists. 2,200 questionnaires were distributed by e-mail, and 275 were sent by surface mail. The response rates were 20% and 29% for e-mail and surface mail, respectively. Principal components analysis of the survey items resulted in 5 factors: outcomes, advocacy, organizational justice, annual convention, and professional recognition. Discriminant function analysis results showed that, on the basis of these factors, the authors were able to correctly classify respondents as current vs former members, and current vs never been members. Current members, former members, and psychologists who never joined CPA were found to differ significantly on their rating of most factors. The factors relevant for retaining members differ from those that attract psychologists to join CPA. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Grounding spatial language in perception: An empirical and computational investigation.

The present paper grounds the linguistic categorization of space in aspects of visual perception; specifically, the structure of projective spatial terms such as above are grounded in the process of attention and in vector-sum coding of overall direction. This is formalized in the attentional vector-sum (AVS) model. This computational model accurately predicts linguistic acceptability judgments for spatial terms, under a variety of spatial configurations. In 7 experiments, the predictions of the AVS model are tested against those of 3 competing models. The results support the AVS model and disconfirm its competitors. The authors conclude that the structure of linguistic spatial categories can be partially explained in terms of independently motivated perceptual processes. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Supporting ordinal four-state classification decisions using neural networks

Many accounting and finance problems require ordinal multi-state classification decisions, (e.g., control risk, bond rating, financial distress, etc.), yet few decision support systems are available to aid decision makers in such tasks. In this study, we develop a Neural Network based decision support system (NN-DSS) to classify firms in four ordinal states of financial condition namely healthy, dividend reduction, debt default and bankrupt. The classification results of the NN-DSS model are compared with those of a Naïve model, a Multiple Discriminant Analysis (MDA) model, and an Ordinal Logistic Regression (OLGR) model. Four different evaluation criteria are used to compare the models, namely, simple classification accuracy, distance-weighted classification accuracy, expected cost of misclassification (ECM) and ranked probability score. Our study shows that NN-DSS models perform significantly better than the Naïve, MDA, and OLGR models on the ECM criteria, and provide better results than MDA and OLGR on other criteria, although not always significantly better. The effect of the proportion of firms of each state in the training set is also studied. A balanced training set leads to more uniform (less skewed) classification across all four states, whereas an unbalanced training set biases the classification results in favor of the state with the largest number of observations.