Anthony J. Bishara

Testing the significance of a correlation with nonnormal data: comparison of Pearson, Spearman, transformation, and resampling approaches.

It is well known that when data are nonnormally distributed, a test of the significance of Pearsons r may inflate Type I error rates and reduce power. Statistics textbooks and the simulation literature provide several alternatives to Pearsons correlation. However, the relative performance of these alternatives has been unclear. Two simulation studies were conducted to compare 12 methods, including Pearson, Spearmans 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.

Temporal Discounting of Rewards in Patients With Bipolar Disorder and Schizophrenia

Patients with bipolar disorder (BD) and schizophrenia (SZ) 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 BD or SZ to investigate their temporal decision making. Twenty-two individuals with BD, 21 individuals with SZ, and 30 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 did the healthy group even after controlling for current substance use, age, gender, and employment. Hierarchical multiple regression analyses showed that discounting rate was associated with both diagnostic group and working memory or intelligence scores. In each group, working memory or intelligence scores negatively correlated with discounting rate. The results suggest that (a) both BD and SZ groups value smaller, immediate rewards more than larger, delayed rewards compared with the healthy group and (b) working memory or intelligence is related to temporal decision making in individuals with BD or SZ as well as in healthy individuals.

A Process Model of Affect Misattribution

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.

An integrative review of process dissociation and related models in social cognition

This chapter reviews the use of formal dual process models in social psychology, with a focus on the process dissociation model and related multinomial models. The utility of the models is illustrated using studies of social and affective influences on memory, judgement and decision making, and social attitudes and stereotypes. We then compare and contrast the process dissociation model with other approaches, including implicit and explicit tests, signal detection theory, and multinomial models. Finally we show how several recently proposed multinomial models can be integrated into a single family of models, of which process dissociation is a specific instance. We describe how these process models can be used as both theoretical and measurement tools to answer questions about the role of automatic and controlled processes in social behaviour.

Identifying Cognitive Remediation Change Through Computational Modelling—Effects on Reinforcement Learning in Schizophrenia

OBJECTIVE Converging research suggests that individuals with schizophrenia show a marked impairment in reinforcement learning, particularly in tasks requiring flexibility and adaptation. The problem has been associated with dopamine reward systems. This study explores, for the first time, the characteristics of this impairment and how it is affected by a behavioral intervention-cognitive remediation. METHOD Using computational modelling, 3 reinforcement learning parameters based on the Wisconsin Card Sorting Test (WCST) trial-by-trial performance were estimated: R (reward sensitivity), P (punishment sensitivity), and D (choice consistency). In Study 1 the parameters were compared between a group of individuals with schizophrenia (n = 100) and a healthy control group (n = 50). In Study 2 the effect of cognitive remediation therapy (CRT) on these parameters was assessed in 2 groups of individuals with schizophrenia, one receiving CRT (n = 37) and the other receiving treatment as usual (TAU, n = 34). RESULTS In Study 1 individuals with schizophrenia showed impairment in the R and P parameters compared with healthy controls. Study 2 demonstrated that sensitivity to negative feedback (P) and reward (R) improved in the CRT group after therapy compared with the TAU group. R and P parameter change correlated with WCST outputs. Improvements in R and P after CRT were associated with working memory gains and reduction of negative symptoms, respectively. CONCLUSION Schizophrenia reinforcement learning difficulties negatively influence performance in shift learning tasks. CRT can improve sensitivity to reward and punishment. Identifying parameters that show change may be useful in experimental medicine studies to identify cognitive domains susceptible to improvement.

Reducing Bias and Error in the Correlation Coefficient Due to Nonnormality

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 (n = 10), where bootstrapping was more effective. Overall, results justify the use of carefully chosen alternatives to the Pearson correlation when normality is violated.

All of the above: When multiple correct response options enhance the testing effect

Previous research has shown that multiple choice tests often improve memory retention. However, the presence of incorrect lures often attenuates this memory benefit. The current research examined the effects of “all of the above” (AOTA) options. When such options are correct, no incorrect lures are present. In the first three experiments, a correct AOTA option on an initial test led to a larger memory benefit than no test and standard multiple choice test conditions. The benefits of a correct AOTA option occurred even without feedback on the initial test; for both 5-minute and 48-hour retention delays; and for both cued recall and multiple choice final test formats. In the final experiment, an AOTA question led to better memory retention than did a control condition that had identical timing and exposure to response options. However, the benefits relative to this control condition were similar regardless of the type of multiple choice test (AOTA or not). Results suggest that retrieval contributes to multiple choice testing effects. However, the extra testing effect from a correct AOTA option, rather than being due to more retrieval, might be due simply to more exposure to correct information.

Anchoring effects on early autobiographical memories

ABSTRACT Studies of childhood memory typically show that our earliest memories come from between three and four years of age. This finding is not universal, however. The age estimate varies across cultures and is affected by social influences. Research from the judgments and decision-making literature suggests that these estimates might also involve a judgment under uncertainty. Therefore, they might be susceptible to less social influences such as heuristics and biases. To investigate this possibility, we conducted two experiments that used anchoring paradigms to influence participants’ estimates of their age during early autobiographical memories. In Experiment 1, participants answered either a high-anchor or a low-anchor question, and were warned that the anchor was uninformative; they went on to estimate their age during their earliest autobiographical memory. In Experiment 2, we replicated Experiment 1 and extended the design to examine additional early autobiographical memories. In both experiments, participants in the low-anchor condition gave earlier age estimates than those in the high-anchor condition. These results provide new insights into the methods used to investigate autobiographical memory. Moreover, they show that reports of early autobiographical memories can be influenced by a relatively light touch – a change to a single digit in a single question.