A cognitive behavioral workshop for subclinical obsessions and compulsions

The present study investigates the effectiveness of a 3-h cognitive behavioral workshop for individuals, ages 18-22, with subclinical obsessions and compulsions. It was hypothesized that, compared to individuals in an assessment-only waitlist group (n = 42), individuals assigned to the workshop group (n = 43) would experience a significant decrease in obsessive-compulsive (OC) symptomatology, comorbidity, and thought action fusion endorsement at 1-month and 5-month follow-up assessments. An additional outcome of interest was the number of incident cases of obsessive-compulsive disorder (OCD) over the course of the study. The results indicated that the workshop group reported a significantly fewer number of OC symptoms at 5-month follow-up and endorsed significantly less thought action fusion at both follow-up points. However, there were no differences between groups in severity of OC symptoms and number of comorbid diagnoses endorsed. Only one incident case of OCD was observed during the study, from a participant in the waitlist group. These results are discussed in reference to treatment of subclinical anxiety symptoms.     

Professionalizing the Practice of Public Policy in the Prevention of Violence

The State of the Science Conference Statement on “Preventing Violence and Related Health-Risking Social Behaviors in Adolescents” accurately summarizes the state of knowledge regarding risk factors for violence and intervention efficacy. The Statement missed an opportunity, however, to move the field of prevention practice and policy forward by advocating for more systematic, central review of preventive interventions through a new federal regulatory body, such as an “FDA for Preventive Interventions.” This body would provide review of evidence-based programs and aid decision-making in funding. As a complement to this body, decision-makers also need guidelines in evidence-based practice in ambiguous circumstances, which characterize much of the reality of public policy. Therefore, this new regulatory body should be accompanied by guidelines for evidence-based practice in intervention and policy. Finally, in order to move forward both of these concepts, a National Academy of Sciences Panel should convene to deliberate how these concepts can be implemented.     

The effects of mindfulness-based cognitive therapy on recurrence of depressive episodes, mental health and quality of life: A randomized controlled study

Depression is characterized by a large risk of relapse/recurrence. Mindfulness-based cognitive therapy (MBCT) is a recent non-drug psychotherapeutic intervention to prevent future depressive relapse/recurrence in remitted/recovered depressed patients. In this randomized controlled trial, the authors investigated the effects of MBCT on the relapse in depression and the time to first relapse since study participation, as well as on several mood states and the quality of life of the patients. 106 recovered depressed patients with a history of at least 3 depressive episodes continued either with their treatment as usual (TAU) or received MBCT in addition to TAU. The efficacy of MBCT was assessed over a study period of 56 weeks. At the end of the study period relapse/recurrence was significantly reduced and the time until first relapse increased in the MBCT plus TAU condition in comparison with TAU alone. The MBCT plus TAU group also showed a significant reduction in both short and longer-term depressive mood and better mood states and quality of the life. For patients with a history of at least three depressive episodes who are not acutely depressed, MBCT, added to TAU, may play an important role in the domain of relapse prevention in depression.     

Beta-MPT: Multinomial processing tree models for addressing individual differences

Traditionally, multinomial processing tree (MPT) models are applied to groups of homogeneous participants, where all participants within a group are assumed to have identical MPT model parameter values. This assumption is unreasonable when MPT models are used for clinical assessment, and it often may be suspect for applications to ordinary psychological experiments. One method for dealing with parameter variability is to incorporate random effects assumptions into a model. This is achieved by assuming that participants’ parameters are drawn independently from some specified multivariate hyperdistribution. In this paper we explore the assumption that the hyperdistribution consists of independent beta distributions, one for each MPT model parameter. These beta-MPT models are ‘hierarchical models’, and their statistical inference is different from the usual approaches based on data aggregated over participants. The paper provides both classical (frequentist) and hierarchical Bayesian approaches to statistical inference for beta-MPT models. In simple cases the likelihood function can be obtained analytically; however, for more complex cases, Markov Chain Monte Carlo algorithms are constructed to assist both approaches to inference. Examples based on clinical assessment studies are provided to demonstrate the advantages of hierarchical MPT models over aggregate analysis in the presence of individual differences.     

A Signal Detection Model for Multiple-Choice Exams

A model for multiple-choice exams is developed from a signal-detection perspective. A correct alternative in a multiple-choice exam can be viewed as being a signal embedded in noise (incorrect alternatives). Examinees are assumed to have perceptions of the plausibility of each alternative, and the decision process is to choose the most plausible alternative. It is also assumed that each examinee either knows or does not know each item. These assumptions together lead to a signal detection choice model for multiple-choice exams. The model can be viewed, statistically, as a mixture extension, with random mixing, of the traditional choice model, or similarly, as a grade-of-membership extension. A version of the model with extreme value distributions is developed, in which case the model simplifies to a mixture multinomial logit model with random mixing. The approach is shown to offer measures of item discrimination and difficulty, along with information about the relative plausibility of each of the alternatives. The model, parameters, and measures derived from the parameters are compared to those obtained with several commonly used item response theory models. An application of the model to an educational data set is presented.     

A Minister's Information-Handling

ABSTRACT

One of the roles which ministers perform is that of “caregiver.” Caregiving functions include such activities as visiting parishioners, hearing confessions, and counselling people. These functions are carried out within a legal context. In this paper, two aspects of the parish minister's information handling in respect to his/her role as caregiver are examined. Specific attention is given to the pastor's vulnerability, while carrying out pastoral duties, to civil lawsuits for malpractice and to her/his expectation of privilege against testifying in criminal proceedings. It is observed that in North America, considerable confusion exists over who actually owns and who can control the information a minister acquires and disseminates.     

The response dynamics of preferential choice

The ubiquity of psychological process models requires an increased degree of sophistication in the methods and metrics that we use to evaluate them. We contribute to this venture by capitalizing on recent work in cognitive science analyzing response dynamics, which shows that the bearing information processing dynamics have on intended action is also revealed in the motor system. This decidedly “embodied” view suggests that researchers are missing out on potential dependent variables with which to evaluate their models—those associated with the motor response that produces a choice. The current work develops a method for collecting and analyzing such data in the domain of decision making. We first validate this method using widely normed stimuli from the International Affective Picture System (Experiment 1), and demonstrate that curvature in response trajectories provides a metric of the competition between choice options. We next extend the method to risky decision making (Experiment 2) and develop predictions for three popular classes of process model. The data provided by response dynamics demonstrate that choices contrary to the maxim of risk seeking in losses and risk aversion in gains may be the product of at least one “online” preference reversal, and can thus begin to discriminate amongst the candidate models. Finally, we incorporate attentional data collected via eye-tracking (Experiment 3) to develop a formal computational model of joint information sampling and preference accumulation. In sum, we validate response dynamics for use in preferential choice tasks and demonstrate the unique conclusions afforded by response dynamics over and above traditional methods.     

Hierarchical models for relational event sequences

Interaction within small groups can often be represented as a sequence of events, each event involving a sender and a recipient. Recent methods for modeling network data in continuous time model the rate at which individuals interact conditioned on the previous history of events as well as actor covariates. We present a hierarchical extension for modeling multiple such sequences, facilitating inferences about event-level dynamics and their variation across sequences. The hierarchical approach allows one to share information across sequences in a principled manner—we illustrate the efficacy of such sharing through a set of prediction experiments. After discussing methods for adequacy checking and model selection for this class of models, the method is illustrated with an analysis of high school classroom dynamics from 297 sessions.