Ramsey, ‘Universals’ and atomic propositions

In ‘Universals’ (1925), Ramsey declares that we do not, and cannot, know the forms of atomic propositions. A year later, in a symposium with Braithwaite and Joseph, he announces a change of mind: atomic propositions may, after all, be discoverable by analysis. It is clear from the 1926 paper that Ramsey intends this to be a revision of the 1925 claim. Puzzlingly, however, Ramsey does not mention analysis in 1925. My task in this article is to provide a justification for that change of mind. My argument consists of two parts. I first show what relation holds between the negative 1925 assertion and the more positive 1926 assertion which allows us to read the latter claim as a retraction of the former, as Ramsey appears to have intended it. Then I shall argue that the retraction of the 1925 claim is forced upon Ramsey by reflection on the fact that three theses, all of which he held in or around the relevant period, are, rather surprisingly, revealed to be inconsistent.     

Role of social skills training in adolescents with social anxiety disorder

Background/Objective: Social skills training (SST) is frequently included in the treatment of social anxiety disorder (SAD) in both children and adolescents, although there is no empirical evidence to support it. Consequently, our objective is to study the role and effects of SST in the treatment of a sample of adolescents with SAD. Method: A total of 108 adolescents diagnosed with generalized social phobia were randomly assigned to two treatment conditions (with and without SST) and a control group waiting list (WLCG). The evaluation included self-report measures, observational tests and blind evaluators. Results: Both interventions significantly reduced the number of social situations feared/avoided with respect to the WLCG, which worsened. Likewise, both interventions were effective but the group with SST obtained better results in the post-test and follow-ups, as well as a lower dropout rate (6:1). Conclusions: The use of SST reduces the dropout rate of treated adolescents and increases the effectiveness of the Intervention Program for Adolescents with Social Phobia.     

Capturing non-linear temporally embedded processes in organizations using recurrence quantification analysis

ABSTRACT

Despite the growing consensus that the majority of psychological phenomena at work are temporally embedded and highly dynamic, existing research is often based on simplified theoretical and methodological models, which take limited account of process dynamics and especially non-linear growth trajectories. In this paper, we highlight the potentials of using recurrence quantifications analysis (RQA) and an extension of RQA – cross-recurrence quantification analysis (CRQA) – for researching process dynamics in organizations. (C)RQA is a powerful technique that can be used to both visualize and quantify time-series data such as repeated measurements of psychological states or sequentially coded dyadic and team interactions. To illustrate the manifold opportunities of (C)RQA, we present three application examples focusing on individuals as systems, dyads as systems, and teams as systems. Specifically, we highlight how (C)RQA can be applied to individual diary data, to leader-follower communication dynamics observed during annual appraisal interviews, and to high-density coded team interactions observed during organizational meetings. We discuss the strengths and limitations of (C)RQA and provide recommendations for researchers interested in using the method.     

Beyond Consistency: Contextual Dependency of Language Style in Monolog and Conversation

Language is highly dynamic: It unfolds over time, and we can use it to achieve a wide variety of communicative goals, from telling a story to trying to persuade another person. One aspect of language that has gained increasing popularity among researchers in the last several decades is the individual language style (LS) represented by an individual’s use of function words (e.g., pronouns, articles). Previous approaches to LS mostly focus on LS of one individual in isolation, paying less attention to the fact that language emerges from interaction with others. The aim of this paper is twofold: First, we integrate LS into a dynamical theoretical framework and present an innovative methodological approach. Second, this paper aims to address how interactive conversation—as an aspect of the communicative setting—changes an individual’s LS. We use recurrence quantification analysis to look at structure in patterns of LS in monologs and conversations of 118 participants. Our results showed that LS significantly differs from monolog to conversation, and post hoc analyses further revealed that the change in LS is greater for conflict than for friendly conversations. The difference between monologs and conversations is reflected more strongly in the dynamics (i.e., structure and complexity) of LS than the proportion of function words used. Theoretical implications and directions for future studies are discussed.     

Measuring inaccuracy of uncertain doxastic states in many-valued logical systems

I will propose an alternative philosophical approach to the representation of uncertain doxastic states. I will argue that the current account of measuring inaccuracy of uncertain doxastic states is inadequate for Belnap's four-valued logic. Specifically, a situation can be found in which either an inaccuracy measure returns a completely wrong result or an agent's inaccuracy score is inadequate relative to the mistake in her doxastic attitude. This will motivate an alternative representation of uncertain doxastic states based on ordered pairs. I will describe a possible inaccuracy measure that is suitable for ordered pairs, and I will show that it has all the qualities that are required for an inaccuracy measure to be legitimate. Finally, I will introduce conditions of rationality for uncertain doxastic states represented by ordered pairs.     

Graph matching approach and generalized median graph for automatic labeling of cortical sulci with parallel and distributed algorithms

The human brain cortex is very complex structure containing folds (gyri) and fissures (sulci) that were the subject of our study in this paper. The sulcus is one of the most important features in order to know the different functions areas of the brain. The exact identification of sulci on human brain using MRI images is helpful in many studies and applications related to brain diseases and human behavior. Automatic labeling of cortical sulci with all this complexity and inter-subject variability, this is considered non-trivial task. In this paper, we have proposed a new graph based approach of automatic labeling of cortical sulci with parallel and distributed algorithms using graph matching and generalized median graph. The graph matching is very important in many studies and applications such as pattern recognition and classification. The generalized median graph of a set of graphs is a way to represent a set of graphs by a comprehensive graph that minimizes the sum of the distances to all graphs. We have used the characteristics of shape, orientation and location to describe the sulci. The results that we have obtained prove that our approach is accurate and acceptable in this field which uses the graph matching for automatic labeling of cortical sulci.     

Empirical Bayes Derivative Estimates

Abstract

A dynamic system is a set of interacting elements characterized by changes occurring over time. The estimation of derivatives is a mainstay for exploring dynamics of constructs, particularly when the dynamics are complicated or unknown. The presence of measurement error in many social science constructs frequently results in poor estimates of derivatives, as even modest proportions of measurement error can compound when estimating derivatives. Given the overlap in the specification of latent differential equation models and latent growth curve models, and the equivalence of latent growth curve models and mixed models under some conditions, derivatives could be estimated from estimates of random effects. This article proposes a new method for estimating derivatives based on calculating the Empirical Bayes estimates of derivatives from a mixed model. Two simulations compare four derivative estimation methods: Generalized Local Linear Approximation, Generalized Orthogonal Derivative Estimates, Functional Data Analysis, and the proposed Empirical Bayes Derivative Estimates. The simulations consider two data collection scenarios: short time series (≤10 observations) from many individuals or occasions, and long individual time series (25–500 observations). A substantive example visualizing the dynamics of intraindividual positive affect time series is also presented.     

Monotone Quantifiers Emerge via Iterated Learning

Natural languages exhibit many semantic universals, that is, properties of meaning shared across all languages. In this paper, we develop an explanation of one very prominent semantic universal, the monotonicity universal. While the existing work has shown that quantifiers satisfying the monotonicity universal are easier to learn, we provide a more complete explanation by considering the emergence of quantifiers from the perspective of cultural evolution. In particular, we show that quantifiers satisfy the monotonicity universal evolve reliably in an iterated learning paradigm with neural networks as agents.     

Verbal and practical intelligence in general anxiety,obsessive compulsive and major depression disorders

IntroductionPrevious researches have shown that anxiety symptoms are negatively associated with measures of intelligence. However, recent findings indicate possible positive relationships between Generalized Anxiety Disorders (GAD) and intelligence. Also, Obsessive Compulsive Disorder (OCD) is associated with a moderate degree of underperformance on cognitive tests, including deficient processing. There are inconsistent results to present the relationship between Major Depression Disorder (MDD) and IQ. The present study has three main aims. The first aim of this study is to investigate the difference between IQ in individuals with GAD, OCD and major depressive disorder, and normal group. The second purpose is to perform a comparative study between the GAD, OCD and MDD groups on verbal and non-verbal intelligence. The third aim of this study is to examine the relationships between GAD, OCD and MDD as well as their underlying cognitive processes, including worry, rumination, and post-event processing, with verbal and non-verbal intelligence.ObjectiveThe present study is performed on four groups of participants including those with GAD, OCD, MDD and Healthy Volunteer (HV) group consisting of individuals without psychiatric disorders.MethodThe number of 50 healthy volunteers as the control group, 45 patients with GAD, 20 patients with OCD and 25 patients with MDD (n = 140) were selected as the case-referent groups. The present study was a cross-sectional type and the research was performed based on the causal-comparative method. Verbal and non-verbal intelligence was measured with the Wechsler Adult Intelligence Scale-3rd edition (WAIS-III). Rumination and post-event processing were measured by PSWQ, RRS-BR, and PEPQ, respectively.ResultsThe results indicate that Verbal Intelligence and Verbal Comprehension Index in GAD patients have significant differences in comparison to the OCD, MDD and control groups. While, the value of the Working Memory Index (WMI) in the normal group is higher than the value of the same index in the GAD, OCD and MDD groups. Also, the values of the Processing Speed Index (PSI) in normal and GAD groups are higher than the OCD and MDD groups. The worry, rumination, and post-event processing in patients with GAD are positively correlated with general and verbal intelligence. But, verbal and non-verbal intelligence had a negative correlation with worry, rumination and post-event processing in healthy volunteers.ConclusionInvestigation of the possible connections between intelligence and the cognitive processes underlying emotional disorders can provide therapeutic strategies for smart individuals who are at risk for GAD.