Pathological gambling and personality disorders: an exploratory study with the IPDE

This article describes the most frequent personality disorders related to pathological gambling. Participants included 50 pathological gamblers assessed with the IPDE, and 50 normative subjects from the general population with the same demographic features (age, sex, and socioeconomic level). Thirty-two percent of the clinical sample (vs. the 8% of the normative sample) showed at least one personality disorder. The most prevalent disorders were Borderline (16%), followed by Antisocial, Paranoid, Narcissistic, and Non-specified (8% each). Gamblers with personality disorders presented an average of 1.5 disorders and they reported higher gambling severity and more severe symptoms of anxiety, depression and alcohol abuse. The implications of this study for clinical practice and research are discussed.     

One-year prediction of cognitive decline following cognitive-stimulation from real-world data

Clinical evidence based on real-world data (RWD) is accumulating exponentially providing larger sample sizes available, which demand novel methods to deal with the enhanced heterogeneity of the data. Here, we used RWD to assess the prediction of cognitive decline in a large heterogeneous sample of participants being enrolled with cognitive stimulation, a phenomenon that is of great interest to clinicians but that is riddled with difficulties and limitations. More precisely, from a multitude of neuropsychological Training Materials (TMs), we asked whether was possible to accurately predict an individual's cognitive decline one year after being tested. In particular, we performed longitudinal modelling of the scores obtained from 215 different tests, grouped into 29 cognitive domains, a total of 124,610 instances from 7902 participants (40% male, 46% female, 14% not indicated), each performing an average of 16 tests. Employing a machine learning approach based on ROC analysis and cross-validation techniques to overcome overfitting, we show that different TMs belonging to several cognitive domains can accurately predict cognitive decline, while other domains perform poorly, suggesting that the ability to predict decline one year later is not specific to any particular domain, but is rather widely distributed across domains. Moreover, when addressing the same problem between individuals with a common diagnosed label, we found that some domains had more accurate classification for conditions such as Parkinson's disease and Down syndrome, whereas they are less accurate for Alzheimer's disease or multiple sclerosis. Future research should combine similar approaches to ours with standard neuropsychological measurements to enhance interpretability and the possibility of generalizing across different cohorts.     

Akrasia and the Desire to Become Someone Else: Venturinha on Moral Matters

Philosophia - This paper discusses practical akrasia from the perspective of the sophisticated form of moral subjectivism that can be derived from Nuno Venturinha’s (2018) remarks on moral...     

Dispossessing Defeat

Higher-order evidence can make an agent doubt the reliability of her reasoning. When this happens, it seems rational for the agent to adopt a cautious attitude towards her original conclusion, even in cases where the higher-order evidence is misleading and the agent's original reasons were actually perfectly good. One may think that recoiling to a cautious attitude in the face of misleading self-doubt involves a failure to properly respond to one's reasons. My aim is to show that this is not so. My proposal is that (misleading) higher-order evidence can undermine the agent's possession of her first-order reasons, constituting what I call a dispossessing defeater. After acquiring the higher-order evidence, the agent is no longer in a position to rely competently on the relevant first-order considerations as reasons for her original conclusion, so that such reasons stop being available to her (even if they remain as strong as in the absence of the higher-order evidence). In this way, an agent with misleading higher-order evidence can adopt a cautious stance towards her original conclusion, while properly responding to the set of reasons that she possesses–a set that is reduced due to the acquisition of higher-order dispossessing defeaters.     

Transfer across unconditioned stimuli in serial feature discrimination training

The transfer of conditioned modulation across conditioned stimuli (CS) and unconditioned stimuli (US) was examined in 3 experiments that used Pavlovian appetitive training procedures with rats. In Experiment 1, after training in a positive patterning discrimination (X-->A+/X-/A-), X increased conditioned responding elicited by another trained-then-extinguished CS as long as that CS had been trained with the same US as was used in discrimination training. In Experiment 2, after training with a feature-negative discrimination (X-->A-/A+), X inhibited conditioned responding elicited by another trained-then-extinguished CS as long as that CS had been trained with the same US. Experiments 1 and 2 used a between-groups design, whereas Experiment 3 used a more powerful within-groups design. In Experiment 3, rats were trained in a feature-positive discrimination (X-->A+/A-). In transfer tests, X increased conditioned responding elicited by another CS trained then extinguished with the same US from training. This increase was greater than the X increased conditioned responding elicited by another CS trained then extinguished with a different US from training. The results supported the suggestion that features trained in serial discrimination tasks influence behavior indirectly by transiently raising or lowering the threshold for activation of the US representations by its target stimuli and by any other stimuli that may be associated with that US. Other interpretations of the findings were also considered.     

Response perseveration in psychopaths: Interpersonal/affective or social deviance traits?

In order to clarify the role of the two broad components of psychopathy (interpersonal/affective and social deviance; R. D. Hare, 2003) in explaining maladaptive response perseveration in psychopaths, as well as the role of reflection after punished responses in this deficit, the authors administered a card perseveration task to 47 Spanish male inmates assessed using the Hare Psychopathy Checklist-Revised (PCL-R; R. D. Hare, 1991). Hierarchical regressions showed that psychopaths' maladaptive perseveration (more cards played and less money earned) was uniquely predicted by the social deviance features of psychopathy (PCL-R Factor 2)--particularly by its impulsive and irresponsible lifestyle facet (PCL-R Facet 3)--and not by its interpersonal/affective features (PCL-R Factor 1). Moreover, perseveration was related to a lack of reflection both after punishment and after reward feedback. The authors' results, in conjunction with previous evidence indicating perseverative deficits in several impulse control disorders, suggest that response perseveration may not be specific to psychopathy but rather is associated more generally with the externalizing dimension of psychopathology.     

Depression and the complete state model of health

Health is a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity. In order to specify the contents of this positive state, the Complete State Model of Health (CSMH) considers mental health as a series of symptoms of hedonia and positive functioning, operationalized by measures of subjective, psychological, and social well-being. This model has empirically confirmed two new axioms: (a) rather than forming a single bipolar dimension, health and illness are correlated unipolar dimensions, and (b) the presence of mental health implies positive personal and social functioning. In the present article, we have taken the CSMH as the theoretical framework for the study of depression. Confirmatory factor analyses did not support the first axiom. In fact, the model that posits that measures of mental illness and health form a single bipolar dimension provided the best fit to the data.     

Application of the pi* goodness-of-fit index to latent structure models

Testing model fit for latent structure models (latent trait models and latent class models) is difficult because of the lack of goodness-of-fit statistics with a known distribution. This paper describes the application of the pi* goodness-of-fit statistic to latent structure models. The statistic pi* is based on the concept of latent classes and has a natural interpretation when applied to these models. This statistic assumes that the population is made up of several classes that follow a parametric model, and a residual class outside the model. The value of pi* is the population proportion in the residual class. This paper describes the estimation algorithms of pi* for latent trait and latent class models and an empirical example with a scale of study habits. There are two latent classes in these data: bad and regular students, which are related to the student s responsibility.