Clozapine usage increases the incidence of pneumonia compared with risperidone and the general population: a retrospective comparison of clozapine, risperidone, and the general population in a single hospital over 25 months.

The aim of this study was to determine whether the incidence of pneumonia in patients taking clozapine was more frequent compared with those taking risperidone or no atypical antipsychotics at all before admission to a tertiary care medical center. This was a retrospective, case-matched study of 465 general medicine patients over a 25 month period from 1 July 2010 to 31 July 2012. Detailed electronic medical records were analyzed to explore the association between the use of two atypical antipsychotics and incidence of pneumonia. Of the 155 patients in the clozapine group, 54 (34.8%) had documented pneumonia compared with 22 (14.2%) in the risperidone group and 18 (11.6%) in the general population group. Clozapine, when compared with the untreated general population, was associated with an increased risk of pneumonia (odds ratio=4.07; 95% confidence interval=2.25-7.36). There was, however, no significant increase in the risk of pneumonia associated with the use of risperidone (odds ratio=1.26; 95% confidence interval=0.65-2.45). Clozapine use is associated with increased risk of pneumonia that may be related to immunologic factors or side effects of sedation and drooling that make aspiration more likely, although causative mechanisms require further investigation. These findings suggest that providers should use added caution in choosing candidates for clozapine therapy.

Category label and response location shifts in category learning.

The category shift literature suggests that rule-based classification, an important form of explicit learning, is mediated by two separate learned associations: a stimulus-to-label association that associates stimuli and category labels, and a label-to-response association that associates category labels and responses. Three experiments investigate whether information-integration classification, an important form of implicit learning, is also mediated by two separate learned associations. Participants were trained on a rule-based or an information-integration categorization task and then the association between stimulus and category label, or between category label and response location was altered. For rule-based categories, and in line with previous research, breaking the association between stimulus and category label caused more interference than breaking the association between category label and response location. However, no differences in recovery rate emerged. For information-integration categories, breaking the association between stimulus and category label caused more interference and led to greater recovery than breaking the association between category label and response location. These results provide evidence that information-integration category learning is mediated by separate stimulus-to-label and label-to-response associations. Implications for the neurobiological basis of these two learned associations are discussed.

The Prep statistic as a measure of confidence in model fitting.

In traditional statistical methodology (e.g., the ANOVA), confidence in the observed results is often assessed by computing thep value or the power of the test. In most cases, adding more participants to a study will improve these measures more than will increasing the amount of data collected from each participant. Thus, traditional statistical methods are biased in favor of experiments with large numbers of participants. This article proposes a method for computing confidence in the results of experiments in which data are collected from a few participants over many trials. In such experiments, it is common to fit a series of mathematical models to the resulting data and to conclude that the best-fitting model is superior. The probability of replicating this result (i.e., Prep) is derived for any two nested models. Simulations and empirical applications of this new statistic confirm its utility in studies in which data are collected from a few participants over many trials.

The effects of positive versus negative feedback on information-integration category learning.

A number of studies have shown that in category learning, providing feedback about errors allows faster learning than providing feedback about correct responses. However, these previous studies used explicit, rule-based tasks in which the category structures could be separated by a simple rule that was easily verbalized. Here, the results of the first experiment known to compare the efficacy of positive versus negative feedback during information-integration category learning are reported. Information-integration tasks require participants to integrate perceptual information from incommensurable dimensions, and evidence suggests that optimal responding recruits procedural learning. The results show that although nearly all of the full-feedback control participants demonstrated information-integration learning, participants receiving either positive-only or negative-only feedback generally used explicit, rule-based strategies. It thus appears that, unlike rule-based learning, consistent information-integration learning requires full feedback. The theoretical implications of these findings for current models of information-integration learning are discussed.

Category learning and multiple memory systems.

Categorization is a vitally important skill that people use every day. Early theories of category learning assumed a single learning system, but recent evidence suggests that human category learning may depend on many of the major memory systems that have been hypothesized by memory researchers. As different memory systems flourish under different conditions, an understanding of how categorization uses available memory systems will improve our understanding of a basic human skill, lead to better insights into the cognitive changes that result from a variety of neurological disorders, and suggest improvements in training procedures for complex categorization tasks.