Copying actions and copying outcomes: Social learning through the second year

The present work documents how the logic of a model's demonstration and the communicative cues that the model provides interact with age to influence how children engage in social learning. Children at ages 12, 18, and 24 months (n = 204) watched a model open a series of boxes. Twelve-month-old subjects only copied the specific actions of the model when they were given a logical reason to do so- otherwise, they focused on reproducing the outcome of the demonstrated actions. Eighteen-month-old subjects focused on copying the outcome when the model was aloof. When the model acted socially, the subjects were as likely to focus on copying actions as outcomes, irrespective of the apparent logic of the model's behavior. Finally, 24-month-old subjects predominantly focused on copying the model's specific actions. However, they were less likely to produce the modeled outcome when the model acted nonsocially.

Neuromuscular-skeletal constraints on the acquisition of skill in a discrete torque production task

The organisation of the human neuromuscular-skeletal system allows an extremely wide variety of actions to be performed, often with great dexterity. Adaptations associated with skill acquisition occur at all levels of the neuromuscular-skeletal system although all neural adaptations are inevitably constrained by the organisation of the actuating apparatus (muscles and bones). We quantified the extent to which skill acquisition in an isometric task set is influenced by the mechanical properties of the muscles used to produce the required actions. Initial performance was greatly dependent upon the specific combination of torques required in each variant of the experimental task. Five consecutive days of practice improved the performance to a similar degree across eight actions despite differences in the torques required about the elbow and forearm. The proportional improvement in performance was also similar when the actions were performed at either 20 or 40% of participants' maximum voluntary torque capacity. The skill acquired during practice was successfully extrapolated to variants of the task requiring more torque than that required during practice. We conclude that while the extent to which skill can be acquired in isometric actions is independent of the specific combination of joint torques required for target acquisition, the nature of the kinetic adaptations leading to the performance improvement in isometric actions is influenced by the neural and mechanical properties of the actuating muscles.

Workplace diversity: The moderating role of work context on the relationship between surface and deep level diversity and group functioning

This research examines the relationship between perceived group diversity and group conflict, and the moderating role of team context. Currentiy, diversity research predominantly focuses on surface and job-related dimensions, largely to the neglect of deep-level diversity (in terms of values, attitude and beliefs). First, this research hjfpothesised that all three dimensions of diversity would be positively related to group conflict, with deep-level diversity the strongest predictor of task. conflict. Second, it was hypothesised that team context would moderate the relationship between deep-level diversity and group conflict. Team context refers to the extent to which the work performed (1) has high consequences (in terms of health and well being for team members and others); (2) is relatively isolating, (3) requires a high reliance upon team members; (4) is volatile; and (5) interpersonal attraction and mutual helpfulness is essential. Two studies were conducted. The first study employed 44 part-time employees across a range of occupations, and the second study employed 66 full-time employees from a mining company in Australia. A series of hierarchical multiple regressions and moderated multiple regressions confirmed both hypotheses. Practical implications and future research directions are discussed.

Learning from the story of a great leader

This paper reports on research findings from a larger study which seeks to understand leadership from the experiences of well-known and well-recognised Australian leaders across a spectrum of endeavours such as the arts, business, science, the law and politics. To date there appears to be limited empirical research that has investigated the insights of Australian leaders regarding their leadership experiences, beliefs and practices. In this paper, the leadership story of a well-respected medical scientist is discussed revealing the contextual factors that influenced her thinking about leadership as well as the key values she embodies as a leader. The paper commences by briefly considering some of the salient leadership literature in the field. In particular, two prominent theoretical frameworks provided by Leavy (2003)and Kouzes and Posner (2002) are explored. While Leavy’s framework construes leadership as consisting of three “C’s” – context , conviction and credibility, Kouzes and Posner (2002)refer to five practices of exemplary leadership. The paper provides a snapshot of the life forces and context that played an important role in shaping the leader’s views and practices. An analytical discussion of these practices is considered in the light of the earlier frameworks identified. Some implications of the findings from this non-education context for those in schools are briefly noted.

Online help systems: Learning while working

Most widely-used computer software packages, such as word processors, spreadsheets and web browsers, incorporate comprehensive help systems, partly because the software is meant for those with little technical knowledge. This paper identifies four systematic philosophies or approaches to help system delivery, namely the documentation approach, based on written documents, either paper-based or online; the training approach, either offered before the user starts working on the software or on-the-job; intelligent help, that is online, context-sensitive help or that relying on software agents; and finally an approach based on minimalism, defined as providing help only when and where it is needed.

An implementation of genetic algorithms as a basis for a trading system on the foreign exchange market

Foreign exchange trading has emerged in recent times as a significant activity in many countries. As with most forms of trading, the activity is influenced by many random parameters so that the creation of a system that effectively emulates the trading process is very helpful. In this paper, we try to create such a system with a genetic algorithm engine to emulate trader behaviour on the foreign exchange market and to find the most profitable trading strategy.

Essays on the Economics of Networks Under Incomplete Information

Social networks constitute a major channel for the diffusion of information and the formation of attitudes in a society. Introducing a dynamic model of social learning, the first part of this thesis studies the emergence of socially influential individuals and groups, and identifies the characteristics that make them influential. The second part uses a Bayesian network game to analyse the role of social interaction and conformism in the making of decisions whose returns or costs are ex ante uncertain.

Climate and causation in the Swedish Iron Age : Learning from the present to understand the past

The paper reassesses the role of climate as a factor shaping changes in settlement and landscape in the Swedish Iron Age (500 BC to AD 1050). Two reasons motivate this re-evaluation. First, high-resolution data based on climate proxies from the natural sciences are now increasingly available. Second, the climate-related social sciences have yielded conceptual and theoretical developments regarding vulnerability and adaptability in the present and recent past, creating new ways to analyse the effects of climatic versus societal factors on societies in the more distant past. Recent research in this field is evaluated and the explicitly climate deterministic standpoint of many recent natural science texts is criticized. Learning from recent approaches to climate change in the social sciences is crucial for understanding society–climate relationships in the past. The paper concludes that we are not yet in a position to fully evaluate the role of the new evidence of abrupt climate change in 850 BC, at the beginning of the Iron Age. Regarding the crisis in the mid first millennium AD, however, new climate data indicate that a dust veil in AD 536–537 might have aggravated the economic and societal crisis known from previous research.

Using ancillary statistics in on-line learning algorithms

Neural networks are usually curved statistical models. They do not have finite dimensional sufficient statistics, so on-line learning on the model itself inevitably loses information. In this paper we propose a new scheme for training curved models, inspired by the ideas of ancillary statistics and adaptive critics. At each point estimate an auxiliary flat model (exponential family) is built to locally accommodate both the usual statistic (tangent to the model) and an ancillary statistic (normal to the model). The auxiliary model plays a role in determining credit assignment analogous to that played by an adaptive critic in solving temporal problems. The method is illustrated with the Cauchy model and the algorithm is proved to be asymptotically efficient.

On the annealed VC entropy for margin classifiers: A statistical mechanics study

Using techniques from Statistical Physics, the annealed VC entropy for hyperplanes in high dimensional spaces is calculated as a function of the margin for a spherical Gaussian distribution of inputs.

Efficient Bayesian inference for learning in the Ising linear perceptron and signal detection in CDMA

Efficient new Bayesian inference technique is employed for studying critical properties of the Ising linear perceptron and for signal detection in code division multiple access (CDMA). The approach is based on a recently introduced message passing technique for densely connected systems. Here we study both critical and non-critical regimes. Results obtained in the non-critical regime give rise to a highly efficient signal detection algorithm in the context of CDMA; while in the critical regime one observes a first-order transition line that ends in a continuous phase transition point. Finite size effects are also studied. © 2006 Elsevier B.V. All rights reserved.

Present perspectives on the automated classification of the G-protein coupled receptors (GPCRs) at the protein sequence level

The G-protein coupled receptors--or GPCRs--comprise simultaneously one of the largest and one of the most multi-functional protein families known to modern-day molecular bioscience. From a drug discovery and pharmaceutical industry perspective, the GPCRs constitute one of the most commercially and economically important groups of proteins known. The GPCRs undertake numerous vital metabolic functions and interact with a hugely diverse range of small and large ligands. Many different methodologies have been developed to efficiently and accurately classify the GPCRs. These range from motif-based techniques to machine learning as well as a variety of alignment-free techniques based on the physiochemical properties of sequences. We review here the available methodologies for the classification of GPCRs. Part of this work focuses on how we have tried to build the intrinsically hierarchical nature of sequence relations, implicit within the family, into an adaptive approach to classification. Importantly, we also allude to some of the key innate problems in developing an effective approach to classifying the GPCRs: the lack of sequence similarity between the six classes that comprise the GPCR family and the low sequence similarity to other family members evinced by many newly revealed members of the family.

Studies on the generalisation of Gaussian processes and Bayesian neural networks

The assessment of the reliability of systems which learn from data is a key issue to investigate thoroughly before the actual application of information processing techniques to real-world problems. Over the recent years Gaussian processes and Bayesian neural networks have come to the fore and in this thesis their generalisation capabilities are analysed from theoretical and empirical perspectives. Upper and lower bounds on the learning curve of Gaussian processes are investigated in order to estimate the amount of data required to guarantee a certain level of generalisation performance. In this thesis we analyse the effects on the bounds and the learning curve induced by the smoothness of stochastic processes described by four different covariance functions. We also explain the early, linearly-decreasing behaviour of the curves and we investigate the asymptotic behaviour of the upper bounds. The effect of the noise and the characteristic lengthscale of the stochastic process on the tightness of the bounds are also discussed. The analysis is supported by several numerical simulations. The generalisation error of a Gaussian process is affected by the dimension of the input vector and may be decreased by input-variable reduction techniques. In conventional approaches to Gaussian process regression, the positive definite matrix estimating the distance between input points is often taken diagonal. In this thesis we show that a general distance matrix is able to estimate the effective dimensionality of the regression problem as well as to discover the linear transformation from the manifest variables to the hidden-feature space, with a significant reduction of the input dimension. Numerical simulations confirm the significant superiority of the general distance matrix with respect to the diagonal one.In the thesis we also present an empirical investigation of the generalisation errors of neural networks trained by two Bayesian algorithms, the Markov Chain Monte Carlo method and the evidence framework; the neural networks have been trained on the task of labelling segmented outdoor images.