Mental playmates: Siblings, executive functioning and theory of mind

This study assessed the theory of mind (ToM) and executive functioning (EF) abilities of 124 typically developing preschool children aged 3 to 5 years in relation to whether or not they had a child-aged sibling (i.e. a child aged 1 to 12 years) at home with whom to play and converse. On a ToM battery that included tests of false belief, appearance-reality (AR) and pretend representation, children who had at least 1 child-aged sibling scored significantly higher than both only children and those whose only siblings were infants or adults. The numbers of child-aged siblings in preschoolers' families positively predicted their scores on both a ToM battery (4 tasks) and an EF battery (2 tasks), and these associations remained significant with language ability partialled out. Results of a hierarchical multiple regression analysis revealed that independent contributions to individual differences in ToM were made by language ability, EF skill and having a child-aged sibling. However, even though some conditions for mediation were met, there was no statistically reliable evidence that EF skills mediated the advantage of presence of child-aged siblings for ToM performance. While consistent with the theory that distinctively childish interaction among siblings accelerates the growth of both TOM and EF capacities, alternative evidence and alternative theoretical interpretations for the findings were also considered.

The (new ?) basics of a teacher professional learning community

This paper argues for the need to further theorise the concept of teacher professional learning communities and provides empirical evidence to support this case. The paper presents findings from an ongoing research project, which investigates the nature of teacher professional learning communities. The study reveals that actual communities do not conform to a normailized articulation of features, as outlined in much of the literature on this topic. Consequently, it represents an attempt to engage in the difficult but necessary task of simultaneously fashioning theory from practice, whilst interpreting theory, in practice. The study proposes that current functionalist understandings of teacher professional learning communities are based upon a literature base which is insufficiently nuanced to capture the complexity inherent within these bodies. A broader base of a more critical sociological literature is also drawn upon to better understand actual, "lived" teacher communities, which are somewhat difficult to describe. In part, such communities exhibit features of functionalist conceptions but they are also organic entities which may be quite unpredictable in their outcomes and cannot be reduced to specific features; they each have their own specific "logic of practice" (Bourdieu, 1990) which influences their activities, in their particular field. The argument proposed here is that in one particular community, this complexity may be represented by the many purposes which the community served, arguably often unbeknown to its members, which fashioned the actual community. This paper tries to add to the existing theoretical base of literature, at the same time as providing evidence to support this theorisation.

The dynamics of a genetic algorithm for a simple learning problem

A formalism for describing the dynamics of Genetic Algorithms (GAs) using method s from statistical mechanics is applied to the problem of generalization in a perceptron with binary weights. The dynamics are solved for the case where a new batch of training patterns is presented to each population member each generation, which considerably simplifies the calculation. The theory is shown to agree closely to simulations of a real GA averaged over many runs, accurately predicting the mean best solution found. For weak selection and large problem size the difference equations describing the dynamics can be expressed analytically and we find that the effects of noise due to the finite size of each training batch can be removed by increasing the population size appropriately. If this population resizing is used, one can deduce the most computationally efficient size of training batch each generation. For independent patterns this choice also gives the minimum total number of training patterns used. Although using independent patterns is a very inefficient use of training patterns in general, this work may also prove useful for determining the optimum batch size in the case where patterns are recycled.

Noisy fitness evaluation in genetic algorithms and the dynamics of learning

A theoretical model is presented which describes selection in a genetic algorithm (GA) under a stochastic fitness measure and correctly accounts for finite population effects. Although this model describes a number of selection schemes, we only consider Boltzmann selection in detail here as results for this form of selection are particularly transparent when fitness is corrupted by additive Gaussian noise. Finite population effects are shown to be of fundamental importance in this case, as the noise has no effect in the infinite population limit. In the limit of weak selection we show how the effects of any Gaussian noise can be removed by increasing the population size appropriately. The theory is tested on two closely related problems: the one-max problem corrupted by Gaussian noise and generalization in a perceptron with binary weights. The averaged dynamics can be accurately modelled for both problems using a formalism which describes the dynamics of the GA using methods from statistical mechanics. The second problem is a simple example of a learning problem and by considering this problem we show how the accurate characterization of noise in the fitness evaluation may be relevant in machine learning. The training error (negative fitness) is the number of misclassified training examples in a batch and can be considered as a noisy version of the generalization error if an independent batch is used for each evaluation. The noise is due to the finite batch size and in the limit of large problem size and weak selection we show how the effect of this noise can be removed by increasing the population size. This allows the optimal batch size to be determined, which minimizes computation time as well as the total number of training examples required.

Dynamics of learning with restricted training sets

The dynamics of supervised learning in layered neural networks were studied in the regime where the size of the training set is proportional to the number of inputs. The evolution of macroscopic observables, including the two relevant performance measures can be predicted by using the dynamical replica theory. Three approximation schemes aimed at eliminating the need to solve a functional saddle-point equation at each time step have been derived.

Ethnic diversity as a multilevel construct:the combined effects of dissimilarity, group diversity, and societal status on learning performance in work groups

The authors present a model of the multilevel effects of diversity on individual learning performance in work groups. For ethnically diverse work groups, the model predicts that group diversity elicits either positive or negative effects on individual learning performance, depending on whether a focal individual’s ethnic dissimilarity from other group members is high or low. By further considering the societal status of an individual’s ethnic origin within society (Anglo versus non-Anglo for our U.K. context), the authors hypothesize that the model’s predictions hold more strongly for non-Anglo group members than for Anglo group members. We test this model with data from 412 individuals working on a 24-week business simulation in 87 four- to seven-person groups with varying degrees of ethnic diversity. Two of the three hypotheses derived from the model received full support and one hypothesis received partial support. Implications for theory development, methods, and practice in applied group diversity research are discussed.