Observing preschoolers' social-emotional behavior: structure, foundations, and prediction of early school success

Social-emotional behavior of 352 3- and 4-year-olds attending private child-care and Head Start programs was observed using the Minnesota Preschool Affect Checklist, Revised (MPAC-R). Goals of the investigation included (a) using MPAC-R data to extract a shortened version, MPAC-R/S, comparing structure, internal consistency, test-retest reliability, and stability of both versions; and, using the shortened measure, to examine (b) age, gender, and risk status differences in social-emotional behaviors; (c) contributions of emotion knowledge and executive function to social-emotional behaviors; and (d) contributions of social-emotional behaviors to early school adjustment and kindergarten academic success. Results show that reliability of MPAC-R/S was as good, or better, than the MPAC-R. MPAC-R/S structure, at both times of observation, included emotionally negative/aggressive, emotionally regulated/prosocial, and emotionally positive/productive behaviors; MPAC-R structure was similar but less replicable over time. Age, gender, and risk differences were found. Children's emotion knowledge contributed to later emotionally regulated/prosocial behavior. Finally, preschool emotionally negative/aggressive behaviors were associated with concurrent and kindergarten school success, and there was evidence of social-emotional behavior mediating relations between emotion knowledge or executive function, and school outcomes. The importance of portable, empirically supported observation measures of social-emotional behaviors is discussed along with possible applications, teacher utilization, and implementation barriers.     

Grapheme-color synaesthesia benefits rule-based Category learning

Researchers have long suspected that grapheme-color synaesthesia is useful, but research on its utility has so far focused primarily on episodic memory and perceptual discrimination. Here we ask whether it can be harnessed during rule-based Category learning. Participants learned through trial and error to classify grapheme pairs that were organized into categories on the basis of their associated synaesthetic colors. The performance of synaesthetes was similar to non-synaesthetes viewing graphemes that were physically colored in the same way. Specifically, synaesthetes learned to categorize stimuli effectively, they were able to transfer this learning to novel stimuli, and they falsely recognized grapheme-pair foils, all like non-synaesthetes viewing colored graphemes. These findings demonstrate that synaesthesia can be exploited when learning the kind of material taught in many classroom settings.     

The Shedler-Westen assessment procedure (SWAP): evaluating psychometric questions about its reliability, validity, and impact of its fixed score distribution

The Shedler-Westen assessment procedure (SWAP) is a personality assessment instrument designed for use by expert clinical assessors. Critics have raised questions about its psychometrics, most notably its validity across observers and situations, the impact of its fixed score distribution on research findings, and its test-retest reliability. We review empirical data addressing its validity, emphasizing the multitrait-multimethod approach to evaluating test validity. To evaluate the hypothesis that the fixed, asymmetric score distribution artifactually inflates correlations between SWAP profiles, we conducted Monte Carlo simulations and also presented empirical data from a large patient sample. We observed a mean correlation of zero between simulated SWAP profiles, indicating that the score distribution does not impact the correlation coefficients. Empirical correlations between SWAP profiles of actual patients were small and similar to those obtained using Diagnostic and statistical manual of mental disorders, fourth edition (DSM-IV) personality disorder scales that had no fixed score distributions, suggesting that the correlations were not a methodological artifact of the SWAP. We report new test-retest reliability data (median coefficient > .85) for the SWAP's trait and personality disorder dimensions. The SWAP appears to be reliable and valid. The data do not support its primary psychometric critiques.     

The Ryōan-ji axiom for common knowledge on hypergraphs

The article studies common knowledge in communication networks with a fixed topological structure. It introduces a non-trivial principle, called the Ryōan-ji axiom, which captures logical properties of common knowledge of all protocols with a given network topology. A logical system, consisting of the Ryōan-ji axiom and two additional axioms, is proven to be sound and complete.     

Equivalence of Problems (An Attempt at an Explication of Problem)

On the one hand, Pavel Tichý has shown in his Transparent Intensional Logic (TIL) that the best way of explicating meaning of the expressions of a natural language consists in identification of meanings with abstract procedures. TIL explicates objective abstract procedures as so-called constructions. Constructions that do not contain free variables and are in a well-defined sense ´normalized´ are called concepts in TIL. On the second hand, Kolmogorov in (Mathematische Zeitschrift 35: 58–65, 1932) formulated a theory of problems, using NL expressions. He explicitly avoids presenting a definition of problems. In the present paper an attempt at such a definition (explication)—independent of but in harmony with Medvedev´s explication—is given together with the claim that every concept defines a problem. The paper treats just mathematical concepts, and so mathematical problems, and tries to show that this view makes it possible to take into account some links between conceptual systems and the ways how to replace a noneffective formulation of a problem by an effective one. To show this in concreto a wellknown Kleene’s idea from his (Introduction to metamathematics. D. van Nostrand, New York, 1952) is exemplified and explained in terms of conceptual systems so that a threatening inconsistence is avoided.     

A computational framework for attentional object discovery in RGB-D videos

We present a computational framework for attention-guided visual scene exploration in sequences of RGB-D data. For this, we propose a visual object candidate generation method to produce object hypotheses about the objects in the scene. An attention system is used to prioritise the processing of visual information by (1) localising candidate objects, and (2) integrating an inhibition of return (IOR) mechanism grounded in spatial coordinates. This spatial IOR mechanism naturally copes with camera motions and inhibits objects that have already been the target of attention. Our approach provides object candidates which can be processed by higher cognitive modules such as object recognition. Since objects are basic elements for many higher level tasks, our architecture can be used as a first layer in any cognitive system that aims at interpreting a stream of images. We show in the evaluation how our framework finds most of the objects in challenging real-world scenes.