The Notion of Pseudo-Argument in Perelman’s Thought

According to Perelman (Rhétoriques, Presses Universitaires de Bruxelles, 1989: 80), a pseudo-argument is an argument that is supposed to be convincing from a given audience viewpoint, while it is not from another audience viewpoint. Such a claim raises the traditional problem of the boundaries between the well known “convince versus persuade” dichotomy. This paper aims at investigating it from a contemporary rhetorical and argumentative perspective which will take into account the fictional dimension of persuasion. In this perspective, it will be claimed that the notion of an “as if” argument better fits to some rhetorical phenomena.

Depressive affect among health care seekers: how it is related to attachment style, emotional disclosure, and health complaints

The present cross-sectional questionnaire survey investigated depressive affect among persons visiting their general practitioner (GP). It examined the impact of a number of factors likely to be associated with depressive affect, including demographic variables, severity of medical condition, and personal factors such as ease in disclosing personal information, and attachment style. Results showed that among the sample of patients (N = 198) visiting their GP, depressive affect was generally quite high. In particular, those reporting more depressive affect were the elderly, those with more severe health problems, more psychological problems, and a lower level of emotional disclosure frequency. As expected, attachment style was also related to lack of well-being among patients: those with a less avoidant but more anxious attachment style reported higher levels of depressive affect. In total, 38% of the variance of depressive affect was predicted by this model. Security of attachment was also related to reasons why patients consulted their GP, with avoidant attachment related to physical problems and anxious attachment to mental problems. Theoretical relevance of the findings and implications for medical help-seeking are discussed.     

Factor analyses of the DIB-R in adolescents

The goal of this study was to examine the factor structure of the Revised Diagnostic Interview for Borderlines (DIB-R; Zanarini, Gunderson, Franckenburg, & Chauncey, 1989) in a nonpatient sample of 118 adolescents. A principal components factor analysis with a Varimax rotation extracted three factors. A confirmatory factor analysis showed that this three-factor model provided an adequate fit. The first factor, which included depression, anxiety, helplessness/hopelessness, loneliness/emptiness, and also encompassed odd thinking/unusual perceptive experiences and quasi-psychotic experiences, appeared to be centered on painful feelings and dissociative defense. The second factor, labeled "impulsivity," consisted of impulse action patterns, counterdependency, and stormy relationships. This factor seemed to be centered on defensive acting-out. The third factor, called "aggressiveness," consisted of anger, hypomania, devaluation/manipulation/sadism, and demand/entitlement, and might express manic/narcissistic defenses. These analyses suggest that there may be homogeneous components of borderline symptomatology in adolescents that may reflect affective disturbances and defensive mechanisms.     

Automatic identification of storytelling responses to past-behavior interview questions via machine learning

Structured interviews often feature past-behavior questions, where applicants are asked to tell a story about past work experience. Applicants often experience difficulties producing such stories. Automatic analyses of applicant behavior in responding to past-behavior questions may constitute a basis for delivering feedback and thus helping them improve their performance. We used machine learning algorithms to predict storytelling in transcribed speech of participants responding to past-behavior questions in a simulated selection interview. Responses were coded as to whether they featured a story or not. For each story, utterances were also manually coded as to whether they described the situation, the task/action performed, or results obtained. The algorithms predicted whether a response features a story or not (best accuracy: 78%), as well as the count of situation, task/action, and response utterances. These findings contribute to better automatic identification of verbal responses to past-behavior questions and may support automatic provision of feedback to applicants about their interview performance.