Is Mental Rotation Ability a Predictor of Success for Motor Performance?

Previous studies provided evidence of a relationship between mental rotation (MR) and motor processes in children and adults. However, there is no direct evidence that MR ability is a reliable predictor of success for motor performance. After completion of a MR test, the motor performance of 7- to 8-year-old and 11- to 12-year-old children was measured along a steeple chase and an equivalent straight distance sprint. The chase involved several motor actions requiring, among different competencies, spatial abilities such as performing a forward roll, jumping, crawling, turning, and changing directions. Data revealed that the time taken to complete the chase was influenced by speed and sex, but also by the individual MR ability. Based on these findings, we assume that MR and motor performance may share similar subprocesses.     

A biologically based model for recognition of 2-D occluded patterns

In this work, we present a biologically inspired model for recognition of occluded patterns. The general architecture of the model is based on the two visual information processing pathways of the human visual system, i.e. the ventral and the dorsal pathways. The proposed hierarchically structured model consists of three parallel processing channels. The main channel learns invariant representations of the input patterns and is responsible for pattern recognition task. But, it is limited to process one pattern at a time. The direct channel represents the biologically based direct connection from the lower to the higher processing level in the human visual cortex. It computes rapid top-down pattern-specific cues to modulate processing in the other two channels. The spatial channel mimics the dorsal pathway of the visual cortex. It generates a combined saliency map of the input patterns and, later, segments the part of the map representing the occluded pattern. This segmentation process is based on our hypothesis that the dorsal pathway, in addition to encoding spatial properties, encodes the shape representations of the patterns as well. The lateral interaction between the main and the spatial channels at appropriate processing levels and top-down, pattern-specific modulation of the these two channels by the direct channel strengthen the locations and features representing the occluded pattern. Consequently, occluded patterns become focus of attention in the ventral channel and also the pattern selected for further processing along this channel for final recognition.     

Preferential Semantics using Non-smooth Preference Relations

This paper studies the properties of eight semantic consequence relations defined from a Tarski-logic L and a preference relation ? . They are equivalent to Shoham’s so-called preferential entailment for smooth model structures, but avoid certain problems of the latter in non-smooth configurations. Each of the logics can be characterized in terms of what we call multi-selection semantics. After discussing this type of semantics, we focus on some concrete proposals from the literature, checking a number of meta-theoretic properties and elaborating on their intuitive motivation. As it turns out, many of their meta-properties only hold in case ? is transitive. To tackle this problem, we propose slight modifications of each of the systems, showing the resulting logics to behave better at the intuitive level and in metatheoretic terms, for arbitrary ? .     

Spectral Decompositions of Multiple Time Series: A Bayesian Non-parametric Approach

We consider spectral decompositions of multiple time series that arise in studies where the interest lies in assessing the influence of two or more factors. We write the spectral density of each time series as a sum of the spectral densities associated to the different levels of the factors. We then use Whittle’s approximation to the likelihood function and follow a Bayesian non-parametric approach to obtain posterior inference on the spectral densities based on Bernstein–Dirichlet prior distributions. The prior is strategically important as it carries identifiability conditions for the models and allows us to quantify our degree of confidence in such conditions. A Markov chain Monte Carlo (MCMC) algorithm for posterior inference within this class of frequency-domain models is presented. We illustrate the approach by analyzing simulated and real data via spectral one-way and two-way models. In particular, we present an analysis of functional magnetic resonance imaging (fMRI) brain responses measured in individuals who participated in a designed experiment to study pain perception in humans.     

Structural Inference from Conditional Knowledge Bases

There are several approaches implementing reasoning based on conditional knowledge bases, one of the most popular being System Z (Pearl, Proceedings of the 3rd conference on theoretical aspects of reasoning about knowledge, TARK ’90, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp. 121–135, 1990). We look at ranking functions (Spohn, The Laws of Belief: Ranking Theory and Its Philosophical Applications, Oxford University Press, Oxford, 2012) in general, conditional structures and c-representations (Kern-Isberner, Conditionals in Nonmonotonic Reasoning and Belief Revision: Considering Conditionals as Agents, vol. 2087 of LNCS, Springer, Berlin, 2001) in order to examine the reasoning strength of the different approaches by learning which of the known calculi of nonmonotonic reasoning (System P and R) and Direct Inference are applicable to these inference relations. Furthermore we use the recently proposed Enforcement-postulate (Kern-Isberner and Krümpelmann, Proceedings of the 22nd international joint conference on artificial intelligence, vol. 2, IJCAI’11, AAAI Press, pp. 937–942, 2011) to show dependencies between these approaches.     

Probability Propagation in Generalized Inference Forms

Probabilistic inference forms lead from point probabilities of the premises to interval probabilities of the conclusion. The probabilistic version of Modus Ponens, for example, licenses the inference from \({P(A) = \alpha}\) and \({P(B|A) = \beta}\) to \({P(B)\in [\alpha\beta, \alpha\beta + 1 - \alpha]}\) . We study generalized inference forms with three or more premises. The generalized Modus Ponens, for example, leads from \({P(A_{1}) = \alpha_{1}, \ldots, P(A_{n})= \alpha_{n}}\) and \({P(B|A_{1} \wedge \cdots \wedge A_{n}) = \beta}\) to an according interval for P(B). We present the probability intervals for the conclusions of the generalized versions of Cut, Cautious Monotonicity, Modus Tollens, Bayes’ Theorem, and some SYSTEM O rules. Recently, Gilio has shown that generalized inference forms “degrade”—more premises lead to less precise conclusions, i.e., to wider probability intervals of the conclusion. We also study Adam’s probability preservation properties in generalized inference forms. Special attention is devoted to zero probabilities of the conditioning events. These zero probabilities often lead to different intervals in the coherence and the Kolmogorov approach.     

Structuring for team success: The interactive effects of network structure and cultural diversity on team potency and performance

This longitudinal study used data from 91 self-managed teams (456 individuals, 60 nationalities) to examine the interactive effects of a team’s task (“workflow”) network structure and its cultural diversity (as indexed by nationality) on the team’s “potency” (i.e., the team’s confidence in its ability to perform) and its performance (as rated by expert judges). We found that whereas the emergence of dense task networks enhanced team potency it was the emergence of (moderately) centralized task networks that facilitated team performance. These varied structural effects, moreover, were themselves contingent on team composition: the more culturally diverse a team, the more pronounced were the positive effects of network density on team potency and the higher the level of network centralization required for optimal team performance. The success of a team appears to hinge on the interplay between network structure and team composition.     

A Description–Experience Gap in Social Interactions: Information about Interdependence and Its Effects on Cooperation

In social interactions, decision makers are often unaware of their interdependence with others, precluding the realization of shared long‐term benefits. In an experiment, pairs of participants played an Iterated Prisoner's Dilemma under various conditions involving differing levels of interdependence information. Each pair was assigned to one of four conditions: “No‐Info” players saw their own actions and outcomes, but were not told that they interacted with another person; “Min‐Info” players knew they interacted with another person but still without seeing the other's actions or outcomes; “Mid‐Info” players discovered the other's actions and outcomes as they were revealed over time; and “Max‐Info” players were also shown a complete payoff matrix mapping actions to outcomes from the outset and throughout the game. With higher levels of interdependence information, we found increased individual cooperation and mutual cooperation, driven by increased reciprocating cooperation (in response to a counterpart's cooperation). Furthermore, joint performance and satisfaction were higher for pairs with more information. We discuss how awareness of interdependence may encourage cooperative behavior in real‐world interactions. Copyright © 2013 John Wiley & Sons, Ltd.