Impulsivity and emotion dysregulation in Borderline Personality Disorder

This study examined the association of borderline personality disorder (BPD) and negative emotional states with impulsivity in the laboratory. Undergraduate participants who were high in BPD features (high-BPD; n = 39) and controls who were low in BPD features (low-BPD; n = 56) completed measures of negative emotional state before a laboratory measure of impulsivity--a passive avoidance learning task. Controlling for psychopathology, high-BPD participants committed a greater number of impulsive responses than did low-BPD participants. Negative emotional state moderated the effect of BPD on impulsive responses. High-BPD participants who were in a negative emotional state committed fewer impulsive responses than high-BPD participants who were low in negative emotional state. Fear, nervousness, and shame negatively correlated with impulsivity among high-BPD participants but not among low-BPD participants. In addition, high-BPD participants reported greater emotion dysregulation in a variety of domains, compared with low-BPD participants.     

Influence analysis for the factor analysis model with ranking data

Influence analysis is an important component of data analysis, and the local influence approach has been widely applied to many statistical models to identify influential observations and assess minor model perturbations since the pioneering work of Cook (1986) . The approach is often adopted to develop influence analysis procedures for factor analysis models with ranking data. However, as this well‐known approach is based on the observed data likelihood, which involves multidimensional integrals, directly applying it to develop influence analysis procedures for the factor analysis models with ranking data is difficult. To address this difficulty, a Monte Carlo expectation and maximization algorithm (MCEM) is used to obtain the maximum‐likelihood estimate of the model parameters, and measures for influence analysis on the basis of the conditional expectation of the complete data log likelihood at the E‐step of the MCEM algorithm are then obtained. Very little additional computation is needed to compute the influence measures, because it is possible to make use of the by‐products of the estimation procedure. Influence measures that are based on several typical perturbation schemes are discussed in detail, and the proposed method is illustrated with two real examples and an artificial example.     

Configural Frequency Analysis in Applied Psychological Research

L'Analyse de Fréquence des Configurations (AFC) est une méthode de recherche des classes et anticlasses dans les classifications croisées de catégories de variables. Les classes sont des modèles de catégories de variables qui apparaissent plus souvent que le hasard ne le laisse supposes. Les anticlasses sont des modèles de catégorie de variables qui apparaissent moins souvent que le hasard ne le laisse supposes. On présente quatre applications d'AFC: l'AFC de premier ordre, l'AFC prédictive, l'AFC de symétrie axiale, et un modèle d'AFC longitudinale. On examine les caractéristiques de l'AFC en insistant sur les contraintes et l'applicabilité. On compare aussi l'AFC avec d'autres méthodes permettant l'analyse multivariée de catégories de données, en particulier la modèlisation log-linéaire. Des sorties informatiques sont traitées.
Configural Frequency Analysis (CFA) is a method for the search for types and antitypes in cross-classifications of categorical variables. Types are patterns of variable categories that occur more often than expected from chance. Antitypes are patterns of variable categories that occur less often than expected from chance. Four applications of CFA are reviewed: the First Order CFA, the Prediction CFA, Axial Symmetry CFA, and one model of Longitudinal CFA. Characteristics of CFA are discussed, focusing on constraints posed and applicability. Comparisons are made of CFA with other methods for multivariate analysis of categorical data, in particular, log-linear modelling. Computational issues are discussed.     

Intra-individual reaction time variability in schizophrenia, depression and borderline personality disorder

Intra-individual reaction time variability (IIV) in neuropsychological task performance reflects short term fluctuations in performance. Increased IIV has been reported in patients with schizophrenia and could be related to a deficient neural timing mechanism, but the role of IIV in adult patients with other psychiatric disorders has not been established. Therefore, we compared IIV measures obtained in a Go/Nogo task from patients with schizophrenia, major depression and borderline personality disorder. IIV was increased for patients with schizophrenia. When correcting for differences in mean reaction time, depressive and borderline patients also showed increased IIV. Importantly, all groups showed a strong association between IIV and accuracy of task performance. This suggests that increased IIV might be a sensitive marker for the efficiency of top-down attentional control in all diagnostic groups. Aside from these similarities, the complete results including measures of IIV, mean reaction time and accuracy show differential patterns for patients with schizophrenia compared to those with borderline personality disorder or depression. These results are discussed with respect to common versus disorder-specific neural mechanisms underlying increased IIV.     

Immediate and delayed effects of word frequency and word length on eye movements in reading: a reversed delayed effect of word length

Three experiments examined the effects in sentence reading of varying the frequency and length of an adjective on (a) fixations on the adjective and (b) fixations on the following noun. The gaze duration on the adjective was longer for low frequency than for high frequency adjectives and longer for long adjectives than for short adjectives. This contrasted with the spillover effects: Gaze durations on the noun were longer when adjectives were low frequency but were actually shorter when the adjectives were long. The latter effect, which seems anomalous, can be explained by three mechanisms: (a) Fixations on the noun are less optimal after short adjectives because of less optimal targeting; (b) shorter adjectives are more difficult to process because they have more neighbors; and (c) prior fixations before skips are less advantageous places to extract parafoveal information. The viability of these hypotheses as explanations of this reverse length effect on the noun was examined in simulations using an updated version of the E-Z Reader model (A. Pollatsek, K. Reichle, & E. D. Rayner, 2006c; E. D. Reichle, A. Pollatsek, D. L. Fisher, & K. Rayner, 1998).     

A scaling analysis of the snake lightness illusion

Logvinenko and Maloney (2006) measured perceived dissimilarities between achromatic surfaces placed in two scenes illuminated by neutral lights that could differ in intensity. Using a novel scaling method, they found that dissimilarities between light surface pairs could be represented as a weighted linear combination of two dimensions, "surface lightness" (a perceptual correlate of the difference in the logarithm of surface albedo) and "surface brightness" (which corresponded to the differences of the logarithms of light intensity across the scenes). Here we attempt to measure the contributions of these dimensions to a compelling lightness illusion (the "snake illusion"). It is commonly assumed that this illusion is a result of erroneous segmentation of the snake pattern into regions of unequal illumination. We find that the illusory shift in the snake pattern occurs along the surface lightness dimension, with no contribution from surface brightness. Thus, even if an erroneous segmentation of the snake pattern into strips of unequal illumination does happen, it reveals itself, paradoxically, as illusory changes in surface lightness rather than as surface brightness. We conjecture that the illusion strength depends on the balance between two groups of illumination cues signaling the true (uniform) illumination and the pictorial (uneven) illumination.