A general proof of consistency of heuristic classification for cognitive diagnosis models

The Asymptotic Classification Theory of Cognitive Diagnosis (Chiu et al., 2009, Psychometrika, 74, 633–665) determined the conditions that cognitive diagnosis models must satisfy so that the correct assignment of examinees to proficiency classes is guaranteed when non‐parametric classification methods are used. These conditions have only been proven for the Deterministic Input Noisy Output AND gate model. For other cognitive diagnosis models, no theoretical legitimization exists for using non‐parametric classification techniques for assigning examinees to proficiency classes. The specific statistical properties of different cognitive diagnosis models require tailored proofs of the conditions of the Asymptotic Classification Theory of Cognitive Diagnosis for each individual model – a tedious undertaking in light of the numerous models presented in the literature. In this paper a different way is presented to address this task. The unified mathematical framework of general cognitive diagnosis models is used as a theoretical basis for a general proof that under mild regularity conditions any cognitive diagnosis model is covered by the Asymptotic Classification Theory of Cognitive Diagnosis.     

Transdiagnostic Internet treatment for anxiety disorders: A randomized controlled trial

Clinician-guided Internet-based cognitive behavioural therapy (iCBT) programs are clinically effective at treating specific anxiety disorders. The present study examined the efficacy of a transdiagnostic Internet-based cognitive behavioural treatment (iCBT) program to treat more than one anxiety disorder within the same program (the Anxiety Program). Eighty six individuals meeting diagnostic criteria for generalized anxiety disorder (GAD), panic disorder, and/or social phobia were randomly assigned to a treatment group, or to a waitlist control group. Treatment consisted of CBT based online educational lessons and homework assignments, weekly email or telephone contact from a clinical psychologist, access to a moderated online discussion forum, and automated emails. An intention-to-treat model using the baseline-observation-carried-forward principle was employed for data analyses. Seventy-five percent of treatment group participants completed all 6 lessons within the 8 week program. Post-treatment data was collected from 38/40 treatment group and 38/38 control group participants, and 3-month follow-up data was collected from 32/40 treatment group participants. Relative to controls, treatment group participants reported significantly reduced symptoms of anxiety as measured by the Generalized Anxiety Disorder - 7 Item, Social Phobia Screening Questionnaire, and the Panic Disorder Severity Rating Scale - Self Report Scale, but not on the Penn State Worry Questionnaire, with corresponding between-groups effect sizes (Cohen’s d) at post-treatment of 0.78, 0.43, 0.43, and 0.20, respectively. The clinician spent a total mean time of 46 min per person over the program, participants rated the procedure as moderately acceptable, and gains were sustained at follow-up. Modifications to the Anxiety program, based on post-treatment feedback from treatment group participants, were associated with improved outcomes in the control group. These results indicate that transdiagnostic programs for anxiety disorders may be successfully administered via the Internet.     

How does mindfulness-based cognitive therapy work?

Mindfulness-based cognitive therapy (MBCT) is an efficacious psychosocial intervention for recurrent depression ( [Kuyken et?al., 2008], [Ma and Teasdale, 2004] and [Teasdale et?al., 2000]). To date, no compelling research addresses MBCT’s mechanisms of change. This study determines whether MBCT’s treatment effects are mediated by enhancement of mindfulness and self-compassion across treatment, and/or by alterations in post-treatment cognitive reactivity. The study was embedded in a randomized controlled trial comparing MBCT with maintenance antidepressants (mADM) with 15-month follow-up (Kuyken et al., 2008). Mindfulness and self-compassion were assessed before and after MBCT treatment (or at equivalent time points in the mADM group). Post-treatment reactivity was assessed one month after the MBCT group sessions or at the equivalent time point in the mADM group. One hundred and twenty-three patients with ≥3 prior depressive episodes, and successfully treated with antidepressants, were randomized either to mADM or MBCT. The MBCT arm involved participation in MBCT, a group-based psychosocial intervention that teaches mindfulness skills, and discontinuation of ADM. The mADM arm involved maintenance on a therapeutic ADM dose for the duration of follow-up. Interviewer-administered outcome measures assessed depressive symptoms and relapse/recurrence across 15-month follow-up. Mindfulness and self-compassion were measured using self-report questionnaire. Cognitive reactivity was operationalized as change in depressive thinking during a laboratory mood induction.MBCT’s effects were mediated by enhancement of mindfulness and self-compassion across treatment. MBCT also changed the nature of the relationship between post-treatment cognitive reactivity and outcome. Greater reactivity predicted worse outcome for mADM participants but this relationship was not evident in the MBCT group.MBCT’s treatment effects are mediated by augmented self-compassion and mindfulness, along with a decoupling of the relationship between reactivity of depressive thinking and poor outcome. This decoupling is associated with the cultivation of self-compassion across treatment.     

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.     

FEELING WITHOUT THINKING: LESSONS FROM THE ANCIENTS ON EMOTION AND VIRTUE-ACQUISITION

Abstract: By briefly sketching some important ancient accounts of the connections between psychology and moral education, I hope to illuminate the significance of the contemporary debate on the nature of emotion and to reveal its stakes. I begin the essay with a brief discussion of intellectualism in Socrates and the Stoics, and Plato's and Posidonius's respective attacks against it. Next, I examine the two current leading philosophical accounts of emotion: the cognitive theory and the noncognitive theory. I maintain that the noncognitive theory better explains human behavior and experience and has more empirical support than the cognitive theory. In the third section of the essay I argue that recent empirical research on emotional contagion and mirroring processes provides important new evidence for the noncognitive theory. In the final section, I draw some preliminary conclusions about moral education and the acquisition of virtue.     

Modeling and estimating recall processing capacity: Sensitivity and diagnostic utility in application to mild cognitive impairment

We investigate the potential for using latency-based measures of retrieval processing capacity to assess changes in performance specific to individuals with mild cognitive impairment (MCI), a reliable precursor state to Alzheimer’s Disease. Use of these capacity measures is motivated in part by exploration of the effects of atrophy on a computational model of a basic hippocampal circuit. We use this model to suggest that capacity may be a more sensitive indicator of the underlying atrophy than speed of processing, and test this hypothesis by adapting a standard behavioral measure of memory (the free and cued selective reminding test, FCSRT) to allow for the collection of cued recall latencies. Participants were drawn from five groups: college-aged, middle-aged, healthy elderly, those with a diagnosis of MCI, and a sample of MCI control participants. The measure of capacity is shown to offer increased classificatory sensitivity relative to the standard behavioral measures, and is also shown to be the behavioral measure that correlated most strongly with hippocampal volume.     

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.