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.     

Predicting the biological variability of environmental rhythms: Weak or strong anticipation for sensorimotor synchronization?

The internal processes involved in synchronizing our movements with environmental stimuli have traditionally been addressed using regular metronomic sequences. Regarding real-life environments, however, biological rhythms are known to have intrinsic variability, ubiquitously characterized as fractal long-range correlations. In our research we thus investigate to what extent the synchronization processes drawn from regular metronome paradigms can be generalized to other (biologically) variable rhythms. Participants performed synchronized finger tapping under five conditions of long-range and/or short-range correlated, randomly variable, and regular auditory sequences. Combining experimental data analysis and numerical simulation, we found that synchronizing with biologically variable rhythms involves the same internal processes as with other variable rhythms (whether totally random or comprising lawful regularities), but different from those involved with a regular metronome. This challenges both the generalizability of conclusions drawn from regular-metronome paradigms, and recent research assuming that biologically variable rhythms may trigger specific strong anticipatory processes to achieve synchronization.     

Analysis of meteorological conditions in Spain by means of clustering techniques

A comprehensive analysis of clustering techniques is presented in this paper through their application to data on meteorological conditions. Six partitional and hierarchical clustering techniques (k-means, k-medoids, SOM k-means, Agglomerative Hierarchical Clustering, and Clustering based on Gaussian Mixture Models) with different distance criteria, together with some clustering evaluation measures (Calinski–Harabasz, Davies–Bouldin, Gap and Silhouette criterion clustering evaluation object), present various analyses of the main climatic zones in Spain. Real-life data sets, recorded by AEMET (Spanish Meteorological Agency) at four of its weather stations, are analyzed in order to characterize the actual weather conditions at each location. The clustering techniques process the data on some of the main daily meteorological variables collected at these stations over six years between 2004 and 2010.     

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.     

Application of the generalized matching law to point-after-touchdown conversions and kicker selection in college football

Coaches make a variety of complex decisions in American-rules college football, especially related to point-after-touchdown (PAT) conversions and, historically, kicker selection. However, little research has characterized the pattern of these choices and whether they are sensitive to environmental manipulations, such as increased effort to score. In the present study, the generalized matching law (GML)—a model that predicts a linear relation between log-transformed choices for two alternatives and the amount of reinforcement garnered from them—was applied to PAT-conversion (1 point vs. 2 point) and kicker (soccer-style vs. conventional-style) selection using archived data from the National Collegiate Athletic Association. Overall, PAT-conversion and kicker selection exhibited matching. Further, narrowing the goal-post width was associated with decreased preference for one-point (relative to two-point) PAT attempts and enhanced sensitivity to increases in points scored from one-point (relative to two-point) PAT attempts. This investigation provides support for the ecological validity of the GML and demonstrates that complex decisions in football conform to an orderly pattern that can be described using the GML.