It is well known that when data are nonnormally distributed, a test of the significance of Pearson's r may inflate Type I error rates and reduce power. Statistics textbooks and the simulation literature provide several alternatives to Pearson's correlation. However, the relative performance of these alternatives has been unclear. Two simulation studies were conducted to compare 12 methods, including Pearson, Spearman's rank-order, transformation, and resampling approaches. With most sample sizes (n ≥ 20), Type I and Type II error rates were minimized by transforming the data to a normal shape prior to assessing the Pearson correlation. Among transformation approaches, a general purpose rank-based inverse normal transformation (i.e., transformation to rankit scores) was most beneficial. However, when samples were both small (n ≤ 10) and extremely nonnormal, the permutation test often outperformed other alternatives, including various bootstrap tests.

Chronic cannabis users are known to be impaired on a test of decision-making, the Iowa Gambling Task (IGT). Computational models of the psychological processes underlying this impairment have the potential to provide a rich description of the psychological characteristics of poor performers within particular clinical groups. We used two computational models of IGT performance, the Expectancy-Valence Learning model (EVL) and the Prospect-Valence Learning model (PVL), to assess motivational, memory, and response processes in 17 chronic cannabis abusers and 15 control participants. Model comparison and simulation methods revealed that the PVL model explained the observed data better than the EVL model. Results indicated that cannabis abusers tended to be underinfluenced by loss magnitude, treating each loss as a constant and minor negative outcome regardless of the size of the loss. In addition, they were more influenced by gains, and made decisions that were less consistent with their expectancies relative to non-using controls.Keywords decision-making; cannabis; Iowa Gambling Task; cognitive modeling Substance abusers often are impaired on laboratory measures of decision-making (Bechara et al., 2001;Petry, 2003;Petry, Bickel, & Arnett, 1998;Rogers et al., 1999). For example, in a laboratory decision-making task known as the Iowa Gambling Task (IGT; Bechara, Damasio, Damasio, & Anderson, 1994), substance abusers often make choices that lead to small, immediate gains at the cost of larger losses over time (S. Grant, Contoreggi, & London, 2000). Cannabis (marijuana) users, like other substance-using populations, perform more poorly than non-using controls on the IGT (Lamers, Bechara, Rizzo, & Ramaekers, 2006;Whitlow et al., 2004), even after prolonged abstinence from the drug (Bolla, Eldreth, Matochik, & Cadet, 2005). This impairment may be due to underlying deficits or differences in psychological processes (e.g., memory impairments, loss insensitivity, etc.), but pinpointing such processes can be difficult with traditional behavioral measures from the IGT. Recent work Address correspondence to: Julie C. Stout, School of Psychology, Psychiatry, & Psychological Medicine, Room 534, Building 17 Clayton Campus, Monash University, Victoria 3800 AUSTRALIA, Tel: +61 3 99053987, Fax: +61 3 99053948, julie.stout@med.monash.edu.au. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. NIH Public Access NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript has attempted to disentangle component processes of the IGT by means of computational cognitive models (Buse...

Recent research suggests that older adults are more susceptible to interference effects than are young adults; however, that research has failed to equate differences in original learning. In 4 experiments, the authors show that older adults are more susceptible to interference effects produced by a misleading prime. Even when original learning was equated, older adults were 10 times as likely to falsely remember misleading information and were much less likely to increase their accuracy by opting not to answer under conditions of free responding. The results are well described by a multinomial model that postulates multiple modes of cognitive control. According to that model, older adults are likely to be captured by misleading information, a form of goal neglect or deficit in inhibitory functions.

A recent study demonstrated that individuals making experience-based choices underweight small probabilities, in contrast to the overweighting observed in a typical descriptive paradigm. We tested whether trial-by-trial feedback in a repeated descriptive paradigm would engender choices more correspondent with experiential or descriptive paradigms. The results of a repeated gambling task indicated that individuals receiving feedback underweighted small probabilities, relative to their no-feedback counterparts. These results implicate feedback as a critical component during the decision-making process, even in the presence of fully specified descriptive information. A model comparison at the individual-subject level suggested that feedback drove individuals' decision weights toward objective probability weighting.

Patients with bipolar disorder and schizophrenia often show decision-making deficits in everyday circumstances. A failure to appropriately weigh immediate versus future consequences of choices may contribute to these deficits. We used the delay discounting task in individuals with bipolar disorder (BD) or schizophrenia (SZ) to investigate their temporal decision-making. Twenty-two individuals with BD, 21 individuals with SZ and 31 healthy individuals completed the delay discounting task along with neuropsychological measures of working memory and cognitive function. Both BD and SZ groups discounted delayed rewards more steeply than the healthy group even after controlling for current substance use, age, gender, and employment. Hierarchical multiple regression analyses showed that discounting rate was associated both with diagnostic group and working memory/intelligence composite scores. In each group, working memory or intelligence scores negatively correlated with discounting rate. The results suggest that 1) both BD and SZ groups value smaller, immediate rewards more than larger, delayed rewards compared to the healthy group and 2) working memory or intelligence is related to temporal decision-making in individuals with BD or SZ as well as in healthy individuals.

People often misattribute the causes of their thoughts and feelings. The authors propose a multinomial process model of affect misattributions, which separates three component processes. The first is an affective response to the true cause of affect. The second is an affective response to the apparent cause. The third process is when the apparent source is confused for the real source. The model is validated using the affect misattribution procedure (AMP), which uses misattributions as a means to implicitly measure attitudes. The model illuminates not only the AMP but also other phenomena in which researchers wish to model the processes underlying misattributions using subjective judgments.

The Wisconsin Card Sort Task (WCST) is a commonly used neuropsychological test of executive or frontal lobe functioning. Traditional behavioral measures from the task (e.g., perseverative errors) distinguish healthy controls from clinical populations, but such measures can be difficult to interpret. In an attempt to supplement traditional measures, we developed and tested a family of sequential learning models that allowed for estimation of processes at the individual subject level in the WCST. Testing the model with substance dependent individuals and healthy controls, the model parameters significantly predicted group membership even when controlling for traditional behavioral measures from the task. Substance dependence was associated with a) slower attention shifting following punished trials and b) reduced decision consistency. Results suggest that model parameters may offer both incremental content validity and incremental predictive validity.

It is more common for educational and psychological data to be nonnormal than to be approximately normal. This tendency may lead to bias and error in point estimates of the Pearson correlation coefficient. In a series of Monte Carlo simulations, the Pearson correlation was examined under conditions of normal and nonnormal data, and it was compared with its major alternatives, including the Spearman rank-order correlation, the bootstrap estimate, the Box-Cox transformation family, and a general normalizing transformation (i.e., rankit), as well as to various bias adjustments. Nonnormality caused the correlation coefficient to be inflated by up to +.14, particularly when the nonnormality involved heavy-tailed distributions. Traditional bias adjustments worsened this problem, further inflating the estimate. The Spearman and rankit correlations eliminated this inflation and provided conservative estimates. Rankit also minimized random error for most sample sizes, except for the smallest samples ( = 10), where bootstrapping was more effective. Overall, results justify the use of carefully chosen alternatives to the Pearson correlation when normality is violated.