The association between intelligence level and brain volume measures: a negative finding

Ratings of pre-morbid intelligence level, derived from the hospital charts of 26 chronic schizophrenics, were correlated with the post-mortem brain volume measures of cortex, white matter, central grey matter, and ventricles. Contrary to hypothesis, no significant correlations were found.     

THE RELATIONSHIP BETWEEN PAY-FOR-PERFORMANCE PERCEPTIONS AND PAY SATISFACTION

A study was conducted with 104 hospital employees to assess the relationship between pay-for-performance perceptions and pay satisfaction. Unlike previous research examining this relationship, a multi-item measure of pay-for-performance perceptions and a multidimensional measure of pay satisfaction were used. As hypothesized, the results indicated a positive relationship between pay-for-performance perceptions and pay-raise satisfaction, pay-level satisfaction, and overall pay satisfaction even after the effects of salary level, salary increases, performance ratings, job tenure, job satisfaction, and promotions were controlled. The importance of gathering perceptual data on characteristics of the pay system believed to have differential relationships with the subdimensions of pay satisfaction are discussed.     

IES Arrow Dot performance and success in a cocaine detoxification program

The IES Arrow-Dot was administered to 41 cocaine detoxification patients. This group's scores indicated lower ego functioning and higher impulsivity and superego functioning than other clinical samples. No significant differences were noted between the Arrow-Dot scores of patients who successfully completed the program and those who did not.     

A reformulation of the general Euclidean model for the external analysis of preference data

This paper discusses least squares methods for fitting a reformulation of the general Euclidean model for the external analysis of preference data. The reformulated subject weights refer to a common set of reference vectors for all subjects and hence are comparable across subjects. If the rotation of the stimulus space is fixed, the subject weight estimates in the model are uniquely determined. Weight estimates can be guaranteed nonnegative. While the reformulation is a metric model for single stimulus data, the paper briefly discusses extensions to nonmetric, pairwise, and logistic models. The reformulated model is less general than Carroll's earlier formulation.The author is grateful to Christopher J. Nachtsheim for his helpful suggestions.     

A representation theorem for languages with generalized quantifiers through back-and-forth methods

We obtain in this paper a representation of the formulae of extensions ofL by generalized quantifiers through functors between categories of first-order structures and partial isomorphisms. The main tool in the proofs is the back-and-forth technique. As a corollary we obtain the Caicedo's version of Fraïssés theorem characterizing elementary equivalence for such languages. We also discuss informally some geometrical interpretations of our results.     

Problem structure and the use of base-rate information from experience

This article is concerned with the use of base-rate information that is derived from experience in classifying examples of a category. The basic task involved simulated medical decision making in which participants learned to diagnose hypothetical diseases on the basis of symptom information. Alternative diseases differed in their relative frequency or base rates of occurrence. In five experiments initial learning was followed by a series of transfer tests designed to index the use of base-rate information. On these tests, patterns of symptoms were presented that suggested more than one disease and were therefore ambiguous. The alternative or candidate diseases on such tests could differ in their relative frequency of occurrence during learning. For example, a symptom might be presented that had appeared with both a relatively common and a relatively rare disease. If participants are using base-rate information appropriately (according to Bayes' theorem), then they should be more likely to predict that the common disease is present than that the rare disease is present on such ambiguous tests. Current classification models differ in their predictions concerning the use of base-rate information. For example, most prototype models imply an insensitivity to base-rate information, whereas many exemplar-based classification models predict appropriate use of base-rate information. The results reveal a consistent but complex pattern. Depending on the category structure and the nature of the ambiguous tests, participants use base-rate information appropriately, ignore base-rate information, or use base-rate information inappropriately (predict that the rare disease is more likely to be present). To our knowledge, no current categorization model predicts this pattern of results. To account for these results, a new model is described incorporating the ideas of property or symptom competition and context-sensitive retrieval.