Postural Stability and Hand Preference as Constraints on One-Handed Catching Performance in Children

Effects of postural state and hand preference as constraints on 1-handed catching performance were investigated in different ability groups of children aged 9-10 years. On the basis of pretest data, the authors classified 48 participants into groups of good, intermediate, and poor catchers (n = 16 in each) and asked them to perform 1-handed catches with their preferred and nonpreferred hands while standing and sitting. The good catchers' performance was not affected by the imposed postural constraints but did improve when they used the preferred hand. A similar effect of hand preference was evident in the intermediate and poor catchers, but there was also an effect of postural constraint. Independent of hand preference, intermediate catchers' performance while seated improved significantly compared with that during standing. For poor catchers, there was an interaction between hand preference and posture; significant improvement was evident only when they used the preferred hand in the sitting condition. The finding that manipulation of posture and hand preference affected performance outcomes indicates that perceptual skill is not the only influence on catching performance in children. Manipulation of those key constraints may facilitate the acquisition of catching skill, but more research is needed to determine the permanence of those effects.     

Extracting Global and Local Dynamics From the Stochastics of Rhythmic Forearm Movements

The authors examined the dynamics governing rhythmic forearm movements that 9 participants performed under a variety of task constraints by using a generic, unbiased analysis technique for extracting the drift coefficients of Fokker-Planck equations from stochastic data. From those coefficients, they reconstructed and analyzed vector fields and phase portraits to identify characteristic, task-dependent kinematic and dynamical features. They first directly estimated the parameters of weakly nonlinear self-sustaining oscillators from the extracted drift coefficients. The estimated parameters that the authors had selected instinctively and then particularized by using averaging methods largely confirmed previously derived limit-cycle models. Next, they ventured beyond limit-cycle models to examine global and local dynamical features that those models cannot adequately address, particularly task-dependent changes in flow strength and curvature and distinct dynamical features associated with flexion and extension. The authors argue that those features should be focal points of researchers' future modeling efforts to formulate a more adequate and encompassing account of the dynamics of rhythmic movement.     

Temporal integration of face view sequences and recognition of novel views

When faces are learned from rotating view sequences, novel views may be recognized by matching them with an integrated representation of the sequence or with individual views. An integrated-representation process should benefit from short view durations, and thus from the inclusion of views in a short temporal window, allowing the distribution of attention over the entire sequence. A view-matching process should benefit from long view durations, allowing the attention to focus on each view. In a sequential comparison task, we tested the recognition of learned and novel interpolated and extrapolated views after learning faces from rapid and slow sequences (240 ms or 960 ms for each view). We found a superiority of rapid over slow sequences, in favour of the integrated-representation hypothesis. In addition, the recognition pattern for the different viewpoints in the sequence depended on the absence or presence of extrapolated views, showing a bias of the distribution of attention.     

A multi-attribute extension of the secretary problem: Theory and experiments

We present a generalization of a class of sequential search problems with ordinal ranks, referred to as “secretary” problems, in which applicants are characterized by multiple attributes. We then present a procedure for numerically computing the optimal search policy and test it in two experiments with incentive-compatible payoffs. With payoffs dependent on the absolute ranks of the attributes, we test the optimal search model with both symmetric (Experiment 1) and asymmetric (Experiment 2) search problems. In both experiments we find that, relative to the optimal search policy, subjects stop the search too early. Our results show that this bias is largely driven by a propensity to stop prematurely on applicants of intermediate (relative) quality.     

Deontic database constraints,violation and recovery

The paper discusses the potential value of a deontic approach to database specification. More specifically, some different types of integrity constraints are considered and a distinction is drawn between necessary (hard) and deontic (soft) constraints.Databases are compared with other normative systems. A deontic logic for database specification is proposed and the problems of how to react to, and of how to correct, or repair, a situation which arises through norm violation are discussed in the context of this logic. The limitations of the proposed logic and possible modifications and extensions of it are analysed.     

The approximation of two-mode proximity matrices by sums of order-constrained matrices

A least-squares strategy is proposed for representing a two-mode proximity matrix as an approximate sum of a small number of matrices that satisfy certain simple order constraints on their entries. The primary class of constraints considered define Q-forms (or anti-Q-forms) for a two-mode matrix, where after suitable and separate row and column reorderings, the entries within each row and within each column are nondecreasing (or nonincreasing) to a maximum (or minimum) and thereafter nonincreasing (or nondecreasing). Several other types of order constraints are also mentioned to show how alternative structures can be considered using the same computational strategy.     

Peek-a-who? Exploring the dynamics of early communication with an interactive partner swap paradigm and state space grid visualization

Effective caregiver-infant communication occurs when interactive partners successfully coordinate multiple modalities (e.g., body movements, affect, eye gaze). The complex interplay of multiple modalities during caregiver-infant interactions is difficult to capture, which has made a comprehensive, evidence-based understanding of caregiver-infant communication difficult to achieve. We present a novel methodological approach to address this challenge by combining an Interactive Partner Swap (IPS) paradigm with a longitudinal design, detailed multimodal coding, and data visualization via state space grids (SSGs). We demonstrate the utility of our approach by presenting three sets of SSGs which reveal both dyadic flexibility and stability in caregiver-infant peek-a-boo interactions across three levels: micro (moment-to-moment), meso (interactive context), and macro (infant development). By using SSGs to explore the patterns that hold and others that differ systematically across interactive partner and infant development, our novel approach promises to offer critical first steps to creating a more detailed understanding of the dynamics of early multimodal communication.     

Exclusion constraints facilitate statistical word learning

The roles of linguistic, cognitive, and social-pragmatic processes in word learning are well established. If statistical mechanisms also contribute to word learning, they must interact with these processes; however, there exists little evidence for such mechanistic synergy. Adults use co-occurrence statistics to encode speech-object pairings with detailed sensitivity in stochastic learning environments (Vouloumanos, 2008). Here, we replicate this statistical work with nonspeech sounds and compare the results with the previous speech studies to examine whether exclusion constraints contribute equally to the statistical learning of speech-object and nonspeech-object associations. In environments in which performance could benefit from exclusion, we find a learning advantage for speech over nonspeech, revealing an interaction between statistical and exclusion processes in associative word learning.