Generalisation between opposing visuomotor rotations when each is associated with visual targets and movements of different amplitude

Previous studies have attempted to identify sources of contextual information which can facilitate dual adaptation to two variants of a novel environment, which are normally prone to interference. The type of contextual information previously used can be grouped into two broad categories: that which is arbitrary to the motor system, such as a colour cue, and that which is based on an internal property of the motor system, such as a change in movement effector. The experiments reported here examined whether associating visuomotor rotations to visual targets and movements of different amplitude would serve as an appropriate source of contextual information to enable dual adaptation. The results indicated that visual target and movement amplitude is not a suitable source of contextual information to enable dual adaptation in our task. Interference was observed in groups who were exposed to opposing visuomotor rotations, or a visuomotor rotation and no rotation, both when the onset of the visuomotor rotations was sudden, or occurred gradually over the course of training. Furthermore, the pattern of interference indicated that the inability to dual adapt was a result of the generalisation of learning between the two visuomotor mappings associated with each of the visual target and movement amplitudes. (C) 2008 Elsevier B.V. All rights reserved.

Visual target separation determines the extent of generalisation between opposing visuomotor rotations

Here we investigated the influence of angular separation between visual and motor targets on concurrent adaptation to two opposing visuomotor rotations. We inferred the extent of generalisation between opposing visuomotor rotations at individual target locations based on whether interference (negative transfer) was present. Our main finding was that dual adaptation occurred to opposing visuomotor rotations when each was associated with different visual targets but shared a common motor target. Dual adaptation could have been achieved either within a single sensorimotor map (i.e. with different mappings associated with different ranges of visual input), or by forming two different internal models (the selection of which would be based on contextual information provided by target location). In the present case, the pattern of generalisation was dependent on the relative position of the visual targets associated with each rotation. Visual targets nearest the workspace of the opposing visuomotor rotation exhibited the most interference (i.e. generalisation). When the minimum angular separation between visual targets was increased, the extent of interference was reduced. These results suggest that the separation in the range of sensory inputs is the critical requirement to support dual adaptation within a single sensorimotor mapping.

Habit reversal training as a treatment for refractory OCD - A case study

Nearly 4 million American men and women from all geographic, ethnic, or economic backgrounds are diagnosed with obsessive-compulsive disorder (OCD). While a combination of cognitive behaviour therapy (CBT) and psycho-pharmaca seems successful for 50% to 60% of patients, for intractable cases the typical recommendation is more medication or more CBT, however there is little evidence that the intensified treatment regimen is successful. In this paper, habit reversal training, including awareness training, competing/other response training, self-monitoring, social support, and generalisation, was implemented with a long-term treatment-refractory OCD patient. Treatment gains and long-term maintenance indicate the potential of habit reversal procedures with these patients.

Enhancing process insight in polymer extrusion by grey box modelling

Due to the complexity and inherent instability in polymer extrusion there is a need for process models which can be run on-line to optimise settings and control disturbances. First-principle models demand computationally intensive solution, while ‘black box’ models lack generalisation ability and physical process insight. This work examines a novel ‘grey box’ modelling technique which incorporates both prior physical knowledge and empirical data in generating intuitive models of the process. The models can be related to the underlying physical mechanisms in the extruder and have been shown to capture unpredictable effects of the operating conditions on process instability. Furthermore, model parameters can be related to material properties available from laboratory analysis and as such, lend themselves to re-tuning for different materials without extensive remodelling work.

Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

Improving neural network training solutions using regularisation

This paper describes the application of regularisation to the training of feedforward neural networks, as a means of improving the quality of solutions obtained. The basic principles of regularisation theory are outlined for both linear and nonlinear training and then extended to cover a new hybrid training algorithm for feedforward neural networks recently proposed by the authors. The concept of functional regularisation is also introduced and discussed in relation to MLP and RBF networks. The tendency for the hybrid training algorithm and many linear optimisation strategies to generate large magnitude weight solutions when applied to ill-conditioned neural paradigms is illustrated graphically and reasoned analytically. While such weight solutions do not generally result in poor fits, it is argued that they could be subject to numerical instability and are therefore undesirable. Using an illustrative example it is shown that, as well as being beneficial from a generalisation perspective, regularisation also provides a means for controlling the magnitude of solutions. (C) 2001 Elsevier Science B.V. All rights reserved.

'Foreign' Terror? London Bombings, Resistance and the Failing State

The British government's response to the London bombings sought to make the terror of that day foreign, even though it appeared largely domestic. This helped construct it as unusual, contingent, part of the uncontrollable ‘otherness’ of the ‘foreign’. However, it also drew the response into the arena of British foreign policy, where the ‘failing state’ has been the dominant conceptualisation of insecurity and terrorism, especially since September 11th. When the bombings are examined through the ‘failing state’ disturbing and important problems are uncovered. Primarily, the ‘failing state’ discourse deconstructs under the influence of the terrorism in London, revealing that Britain itself is a ‘failing state’ by its own description and producing a generalisation of state ‘failure’. It thereby reveals several possible sites for responding to and resisting the government's representation.

A fast method for fuzzy neural modeling and refinement

In the identification of complex dynamic systems using fuzzy neural networks, one of the main issues is the curse of dimensionality, which makes it difficult to retain a large number of system inputs or to consider a large number of fuzzy sets. Moreover, due to the correlations, not all possible network inputs or regression vectors in the network are necessary and adding them simply increases the model complexity and deteriorates the network generalisation performance. In this paper, the problem is solved by first proposing a fast algorithm for selection of network terms, and then introducing a refinement procedure to tackle the correlation issue. Simulation results show the efficacy of the method.

Do development and learning really decrease memory? On similarity and category-based induction in adults and children

In a recently published study, Sloutsky and Fisher [Sloutsky, V. M., & Fisher, A.V. (2004a). When development and learning decrease memory: Evidence against category-based induction in children. Psychological Science, 15, 553-558; Sloutsky, V. M., & Fisher, A. V. (2004b). Induction and categorization in young children: A similarity-based model. Journal of Experimental Psychology: General, 133, 166-188.] demonstrated that children have better memory for the items that they generalise to than do adults. On the basis of this finding, they claim that children and adults use different mechanisms for inductive generalisations;whereas adults focus on shared category membership, children project properties on the basis of perceptual similarity. Sloutsky & Fisher attribute children's enhanced recognition memory to the more detailed processing required by this similarity-based mechanism. In Experiment I we show that children look at the stimulus items for longer than adults. In Experiment 2 we demonstrate that although when given just 250 ms to inspect the items children remain capable of making accurate inferences, their subsequent memory for those items decreases significantly. These findings suggest that there are no necessary conclusions to be drawn from Sloutsky & Fisher's results about developmental differences in generalisation strategy. (C) 2007 Elsevier B.V. All rights reserved.

A hierarchical multiclass support vector machine incorporated with holistic triple learning units

This paper proposes a new hierarchical learning structure, namely the holistic triple learning (HTL), for extending the binary support vector machine (SVM) to multi-classification problems. For an N-class problem, a HTL constructs a decision tree up to a depth of A leaf node of the decision tree is allowed to be placed with a holistic triple learning unit whose generalisation abilities are assessed and approved. Meanwhile, the remaining nodes in the decision tree each accommodate a standard binary SVM classifier. The holistic triple classifier is a regression model trained on three classes, whose training algorithm is originated from a recently proposed implementation technique, namely the least-squares support vector machine (LS-SVM). A major novelty with the holistic triple classifier is the reduced number of support vectors in the solution. For the resultant HTL-SVM, an upper bound of the generalisation error can be obtained. The time complexity of training the HTL-SVM is analysed, and is shown to be comparable to that of training the one-versus-one (1-vs.-1) SVM, particularly on small-scale datasets. Empirical studies show that the proposed HTL-SVM achieves competitive classification accuracy with a reduced number of support vectors compared to the popular 1-vs-1 alternative.

The development of University Technology Transfer stakeholder relationships at a regional level: Lessons for the future

The increasing importance placed upon regional development and the knowledge-based economy as economic growth stimuli has led to a changing role for Universities and their interaction with the business community through (though not limited to) the transfer of technology from academia to industry. With the emergence of Local Enterprise Partnerships (LEPs) replacing the Regional Development Agencies (RDAs), there is a need for policy and practice going forward to be clearly informed by a critique of TTO (Technology Transfer Office)–RDA stakeholder relationship in a lessons learned approach so that LEPs can benefit from a faster learning curve. Thus, the aim of this paper is to examine the stakeholder relationship between three regional universities in the context of its TTO and the RDA with a view to determining lessons learned for the emerging LEP approach. Although the issues raised are contextual, the abstracted stakeholder conceptualisation of the TTO–RDA relationship should enable wider generalisation of the issues raised beyond the UK. Stakeholder theory relationship and stage development models are used to guide a repeat interview study of the TTO and RDA stakeholder groupings. The findings, interpreted using combined category and stage based stakeholder models, show how the longitudinal development of the TTO–RDA stakeholder relationship for each case has progressed through different stakeholder pathways, and stages where specific targeting of funding was dependant on the stakeholder stage. Greater targeted policy and funding, based on the stakeholder relationship approach, led to the development of joint mechanisms and a closer alignment of performance measures between the TTO and the RDA. However, over-reliance on the unitary nature of the TTO–RDA relationship may lead to a lack of cultivation and dependency for funding from other stakeholders.