Cardiac autonomic reactivity and recovery in predicting carotid atherosclerosis: the cardiovascular risk in young Finns study.

The present study examined the association of cardiac autonomic task-induced reactivity and recovery to preclinical atherosclerosis. Thirty-three men and 33 women aged 24-39 years participated in the ongoing epidemiological Cardiovascular Risk in Young Finns study. The authors measured heart rate (HR), respiratory sinus arrhythmia (RSA), and preejection period (PEP) during the mental arithmetic and speech tasks in 1999. Carotid atherosclerosis was assessed by measuring the thickness of the common carotid artery intima-media complex (IMT) with ultrasound in 2001. Higher HR, RSA, and PEP reactivity were associated with lower IMT values even after adjusting for cardiovascular risk factors (lipid levels, obesity, and blood pressure). In addition, better HR recovery after the mental arithmetic task was associated with lower IMT values, and this association persisted after all adjustments. Thus, higher task-induced cardiac autonomic reactivity and better HR recovery were related to less preclinical atherosclerosis. The authors concluded that cardiac pattern of reactivity and quick recovery may be associated with better cardiovascular health, and therefore all reactivity occurring in challenging situations should not automatically be considered as potentially pathological.     

The effects of divided attention on encoding and retrieval processes: The resiliency of retrieval processes

We have recently cast doubt (Craik, Govoni, Naveh-Benjamin, & Anderson, 1996; Naveh-Benjamin, Craik, Guez, & Dori, 1998) on the view that encoding and retrieval processes in human memory are similar. Divided attention at encoding was shown to reduce memory performance significantly, whereas divided attention at retrieval affected memory performance only minimally. In this article we examined this asymmetry further by using more difficult retrieval tasks, which require substantial effort. In one experiment, subjects had to encode and retrieve lists of unfamiliar name-nouns combinations attached to people's photographs, and in the other, subjects had to encode words that were either strong or weak associates of the cues presented with them and then to retrieve those words with either intra- or extra-list cues. The results of both experiments showed that unlike division of attention at encoding, which reduces memory performance markedly, division of attention at retrieval has almost no effect on memory performance, but was accompanied by an increase in secondary-task cost. Such findings again illustrated the resiliency of retrieval processes to manipulations involving the withdrawal of attention. We contend that retrieval processes are obligatory or protected, but that they require attentional resources for their execution.     

Religion and Health: Anxiety,Religiosity, Meaning of Life and Mental Health

We examined the association among anxiety, religiosity, meaning of life and mental health in a nonclinical sample from a Chinese society. Four hundred fifty-one Taiwanese adults (150 males and 300 females) ranging in age from 17 to 73 years (M = 28.9, SD = 11.53) completed measures of Beck Anxiety Inventory, Medical Outcomes Study Health Survey, Perceived Stress Scale, Social Support Scale, and Personal Religiosity Scale (measuring religiosity and meaning of life). Meaning of life has a significant negative correlation with anxiety and a significant positive correlation with mental health and religiosity; however, religiosity does not correlate significantly anxiety and mental health after controlling for demographic measures, social support and physical health. Anxiety explains unique variance in mental health above meaning of life. Meaning of life was found to partially mediate the relationship between anxiety and mental health. These findings suggest that benefits of meaning of life for mental health can be at least partially accounted for by the effects of underlying anxiety.     

A unified approach to exploratory factor analysis with missing data,nonnormal data,and in the presence of outliers

Factor analysis is regularly used for analyzing survey data. Missing data, data with outliers and consequently nonnormal data are very common for data obtained through questionnaires. Based on covariance matrix estimates for such nonstandard samples, a unified approach for factor analysis is developed. By generalizing the approach of maximum likelihood under constraints, statistical properties of the estimates for factor loadings and error variances are obtained. A rescaled Bartlett-corrected statistic is proposed for evaluating the number of factors. Equivariance and invariance of parameter estimates and their standard errors for canonical, varimax, and normalized varimax rotations are discussed. Numerical results illustrate the sensitivity of classical methods and advantages of the proposed procedures.This project was supported by a University of North Texas Faculty Research Grant, Grant #R49/CCR610528 for Disease Control and Prevention from the National Center for Injury Prevention and Control, and Grant DA01070 from the National Institute on Drug Abuse. The results do not necessarily represent the official view of the funding agencies. The authors are grateful to three reviewers for suggestions that improved the presentation of this paper.     

Category induction from distributional cues in an artificial language

The ability to identify the grammatical category of a word (e.g., noun, verb, adjective) is a fundamental aspect of competence in a natural language. Children show evidence of categorization by as early as 18 months, and in some cases younger. However, the mechanisms that underlie this ability are not well understood. The lexical co-occurrence patterns of words in sentences could provide information about word categories--for example, words that follow the in English often belong to the same category. As a step in understanding the role distributional mechanisms might play in language learning, the present study investigated the ability of adults to categorize words on the basis of distributional information. Forty participants listened for approximately 6 min to sentences in an artificial language and were told that they would later be tested on their memory for what they had heard. Participants were next tested on an additional set of sentences and asked to report which sentences they recognized from the first 6 min. The results suggested that learners performed a distributional analysis on the initial set of sentences and recognized sentences on the basis of their memory of sequences of categories of words. Thus, mechanisms that would be useful in natural language learning were shown to be active in adults in an artificial language learning task.