Associative knowledge controls deployment of visual selective attention

According to some models of visual selective attention, objects in a scene activate corresponding neural representations, which compete for perceptual awareness and motor behavior. During a visual search for a target object, top-down control exerted by working memory representations of the target's defining properties resolves competition in favor of the target. These models, however, ignore the existence of associative links among object representations. Here we show that such associations can strongly influence deployment of attention in humans. In the context of visual search, objects associated with the target were both recalled more often and recognized more accurately than unrelated distractors. Notably, both target and associated objects competitively weakened recognition of unrelated distractors and slowed responses to a luminance probe. Moreover, in a speeded search protocol, associated objects rendered search both slower and less accurate. Finally, the first saccades after onset of the stimulus array were more often directed toward associated than control items.

Increased salience of gains versus decreased associative learning differentiate bipolar disorder from schizophrenia during incentive decision making

Background Abnormalities in incentive decision making, typically assessed using the Iowa Gambling Task (IGT), have been reported in both schizophrenia (SZ) and bipolar disorder (BD). We applied the Expectancy-Valence (E-V) model to determine whether motivational, cognitive and response selection component processes of IGT performance are differentially affected in SZ and BD. Method Performance on the IGT was assessed in 280 individuals comprising 70 remitted patients with SZ, 70 remitted patients with BD and 140 age-, sex-and IQ-matched healthy individuals. Based on the E-V model, we extracted three parameters, 'attention to gains or loses', 'expectancy learning' and 'response consistency', that respectively reflect motivational, cognitive and response selection influences on IGT performance. Results Both patient groups underperformed in the IGT compared to healthy individuals. However, the source of these deficits was diagnosis specific. Associative learning underlying the representation of expectancies was disrupted in SZ whereas BD was associated with increased incentive salience of gains. These findings were not attributable to non-specific effects of sex, IQ, psychopathology or medication. Conclusions Our results point to dissociable processes underlying abnormal incentive decision making in BD and SZ that could potentially be mapped to different neural circuits. © 2012 Cambridge University Press.

Age differences in associative and strategic processes in human conditional learning

Research indicates associative and strategic deficits mediate age related deficits in memory, whereas simple associative processes are independent of strategic processing and strategic processes mediate resistance to interference. The present study showed age-related deficits in a contingency learning task, although older participants' resistance to interference was not disproportionately affected. Recognition memory predicted discrimination, whereas general cognitive ability predicted resistance to interference, suggesting differentiation between associative and strategic processes in learning and memory, and age declines in associative processes. Older participants' generalisation of associative strength from existing to novel stimulus-response associations was consistent with elemental learning theories, whereas configural models predicted younger participants' responses. This is consistent with associative deficits and reliance on item-level representations in memory during later life. © 2011 Psychology Press Ltd.

Novel formulation strategies for the fabrication of lyophilised orally disintegrating tablets

Orally disintegrating Tablets (ODTs), also known as fast-disintegrating, fast-melt or fast-dissolving tablets, are a relatively novel dosage technology that involves the rapid disintegration or dissolution of the dosage form into a solution or suspension in the mouth without the need for water. The solution containing the active ingredients is swallowed, and the active ingredients are then absorbed through the gastrointestinal epithelium to reach the target and produce the desired effect. Formulation of ODTs was originally developed to address swallowing difficulties of conventional solid oral dosage forms (tablets and capsules) experienced by wide range of patient population, especially children and elderly. The current work investigates the formulation and development of ODTs prepared by freeze drying. Initial studies focused on formulation parameters that influence the manufacturing process and performance of lyophilised tablets based on excipients used in commercial products (gelatin and saccharides). The second phase of the work was followed up by comprehensive studies to address the essential need to create saccharide free ODTs using naturally accruing amino acids individually or in combinations. Furthermore, a factorial design study was carried out to investigate the feasibility of delivering multiparticulate systems of challenging drugs using a novel formulation that exploited the electrostatic associative interaction between gelatin and carrageenan. Finally, studies aimed to replace gelatin with ethically and morally accepted components to the end users were performed and the selected binder was used in factorial design studies to investigate and optimise ODT formulations that incorporated drugs with varies physicochemical properties. Our results show that formulation of elegant lyophilised ODTs with instant disintegration and adequate mechanical strength requires carful optimisation of gelatin concentration and bloom strength in addition to saccharide type and concentration. Successful formulation of saccharides free lyophilised ODTs requires amino acids that crystallise in the frozen state or display relatively high Tg', interact and integrate completely with the binder and, also, display short wetting time with the disintegrating medium. The use of an optimised mixture of gelatin, carrageenan and alanine was able to create viscous solutions to suspend multiparticulate systems and at the same time provide tablets with short disintegration times and adequate mechanical properties. On the other hand, gum arabic showed an outstanding potential for use as a binder in the formulation of lyophilised ODTs. Compared to gelatin formulations, the use of gum arabic simplified the formulation stages, shortened the freeze drying cycles and produced tablets with superior performance in terms of the disintegration time and mechanical strength. Furthermore, formulation of lyophilised ODTs based on gum arabic showed capability to deliver diverse range of drugs with advantages over commercial products.

The TAP approach to intensive and extensive connectivity systems

The Thouless-Anderson-Palmer (TAP) approach was originally developed for analysing the Sherrington-Kirkpatrick model in the study of spin glass models and has been employed since then mainly in the context of extensively connected systems whereby each dynamical variable interacts weakly with the others. Recently, we extended this method for handling general intensively connected systems where each variable has only O(1) connections characterised by strong couplings. However, the new formulation looks quite different with respect to existing analyses and it is only natural to question whether it actually reproduces known results for systems of extensive connectivity. In this chapter, we apply our formulation of the TAP approach to an extensively connected system, the Hopfield associative memory model, showing that it produces identical results to those obtained by the conventional formulation.

Mapping dissociations in verb morphology

Substantial behavioural and neuropsychological evidence has been amassed to support the dual-route model of morphological processing, which distinguishes between a rule-based system for regular items (walk–walked, call–called) and an associative system for the irregular items (go–went). Some neural-network models attempt to explain the neuropsychological and brain-mapping dissociations in terms of single-system associative processing. We show that there are problems in the accounts of homogeneous networks in the light of recent brain-mapping evidence of systematic double-dissociation. We also examine the superior capabilities of more internally differentiated connectionist models, which, under certain conditions, display systematic double-dissociations. It appears that the more differentiation models show, the more easily they account for dissociation patterns, yet without implementing symbolic computations.

Formulation of multiparticulate systems as lyophilised orally disintegrating tablets

The current study aimed to exploit the electrostatic associative interaction between carrageenan and gelatin to optimise a formulation of lyophilised orally disintegrating tablets (ODTs) suitable for multiparticulate delivery. A central composite face centred (CCF) design was applied to study the influence of formulation variables (gelatin, carrageenan and alanine concentrations) on the crucial responses of the formulation (disintegration time, hardness, viscosity and pH). The disintegration time and viscosity were controlled by the associative interaction between gelatin and carrageenan upon hydration which forms a strong complex that increases the viscosity of the stock solution and forms tablet with higher resistant to disintegration in aqueous medium. Therefore, the levels of carrageenan, gelatin and their interaction in the formulation were the significant factors. In terms of hardness, increasing gelatin and alanine concentration was the most effective way to improve tablet hardness. Accordingly, optimum concentrations of these excipients were needed to find the best balance that fulfilled all formulation requirements. The revised model showed high degree of predictability and optimisation reliability and therefore was successful in developing an ODT formulation with optimised properties that were able deliver enteric coated multiparticulates of omeprazole without compromising their functionality.

Mechanistic studies of organotellurium chemistry

The oxidation of bis(p-ethoxyphenyl) ditelluride by hydrogen peroxide has been studied kinetically. The reaction monitored was an oxidation from tellurium(I) to tellurium(II). The reaction stoichiometry ratio was found to depend upon the initial reagent concentrations. The presence of dioxygen was found to retard the rate and attributed to a dioxygen-ditelluride adduct. The rate varies in the following order of different atmospheres N2> Air> > O2. The final product obtained from the oxidation has been characterised by IR, NMR and ESR spectroscopy. A mechanism for the oxidation has been suggested. The reduction of p-EtOPhTeCl3 by the hydrazinium ion has been studied kinetically. The stoichiometric measurements show that four moles p-EtOPhTeCl3 are equivalent to three moles hydrazinium ion. The kinetics were studied under pseudo first order conditions. No ammonia was detected as a nitrogen containing product. The reduction proceeds via a two-electron process which indicates that it is inner-sphere in nature. A mechanism for the reduction is suggested. The solvolysis of p-EtOPhTeCl3 by methanol in benzene/methanol media has been studied. The study shows that the solvolysis is a reversible, acid catalysed reaction. Replacement of the chlorides on tellurium by methanol is agreed to be associative and replacement of the first chloride is rate determining. The rate of solvolysis varies in the order trichloride > tribromide > triiodide. A mechanism for the solvolysis is suggested. The synthesis of some tellurium heterocyclics is reported. The synthesis and characterisation of telluranthrene is reported. The attempted synthesis of telluraxanthene was unsuccessful.

Mirror-image relations in category learning

The discrimination of patterns that are mirror-symmetric counterparts of each other is difficult and requires substantial training. We explored whether mirror-image discrimination during expertise acquisition is based on associative learning strategies or involves a representational shift towards configural pattern descriptions that permit resolution of symmetry relations. Subjects were trained to discriminate between sets of unfamiliar grey-level patterns in two conditions, which either required the separation of mirror images or not. Both groups were subsequently tested in a 4-class category-learning task employing the same set of stimuli. The results show that subjects who had successfully learned to discriminate between mirror-symmetric counterparts were distinctly faster in the categorization task, indicating a transfer of conceptual knowledge between the two tasks. Additional computer simulations suggest that the development of such symmetry concepts involves the construction of configural, protoholistic descriptions, in which positions of pattern parts are encoded relative to a spatial frame of reference.

The n-tuple network: coping with sub-optimality

The subject of this thesis is the n-tuple net.work (RAMnet). The major advantage of RAMnets is their speed and the simplicity with which they can be implemented in parallel hardware. On the other hand, this method is not a universal approximator and the training procedure does not involve the minimisation of a cost function. Hence RAMnets are potentially sub-optimal. It is important to understand the source of this sub-optimality and to develop the analytical tools that allow us to quantify the generalisation cost of using this model for any given data. We view RAMnets as classifiers and function approximators and try to determine how critical their lack of' universality and optimality is. In order to understand better the inherent. restrictions of the model, we review RAMnets showing their relationship to a number of well established general models such as: Associative Memories, Kamerva's Sparse Distributed Memory, Radial Basis Functions, General Regression Networks and Bayesian Classifiers. We then benchmark binary RAMnet. model against 23 other algorithms using real-world data from the StatLog Project. This large scale experimental study indicates that RAMnets are often capable of delivering results which are competitive with those obtained by more sophisticated, computationally expensive rnodels. The Frequency Weighted version is also benchmarked and shown to perform worse than the binary RAMnet for large values of the tuple size n. We demonstrate that the main issues in the Frequency Weighted RAMnets is adequate probability estimation and propose Good-Turing estimates in place of the more commonly used :Maximum Likelihood estimates. Having established the viability of the method numerically, we focus on providillg an analytical framework that allows us to quantify the generalisation cost of RAMnets for a given datasetL. For the classification network we provide a semi-quantitative argument which is based on the notion of Tuple distance. It gives a good indication of whether the network will fail for the given data. A rigorous Bayesian framework with Gaussian process prior assumptions is given for the regression n-tuple net. We show how to calculate the generalisation cost of this net and verify the results numerically for one dimensional noisy interpolation problems. We conclude that the n-tuple method of classification based on memorisation of random features can be a powerful alternative to slower cost driven models. The speed of the method is at the expense of its optimality. RAMnets will fail for certain datasets but the cases when they do so are relatively easy to determine with the analytical tools we provide.

Individual differences in human-computer interaction

This thesis initially presents an 'assay' of the literature pertaining to individual differences in human-computer interaction. A series of experiments is then reported, designed to investigate the association between a variety of individual characteristics and various computer task and interface factors. Predictor variables included age, computer expertise, and psychometric tests of spatial visualisation, spatial memory, logical reasoning, associative memory, and verbal ability. These were studied in relation to a variety of computer-based tacks, including: (1) word processing and its component elements; (ii) the location of target words within passages of text; (iii) the navigation of networks and menus; (iv) command generation using menus and command line interfaces; (v) the search and selection of icons and text labels; (vi) information retrieval. A measure of self-report workload was also included in several of these experiments. The main experimental findings included: (i) an interaction between spatial ability and the manipulation of semantic but not spatial interface content; (ii) verbal ability being only predictive of certain task components of word processing; (iii) age differences in word processing and information retrieval speed but not accuracy; (iv) evidence of compensatory strategies being employed by older subjects; (v) evidence of performance strategy differences which disadvantaged high spatial subjects in conditions of low spatial information content; (vi) interactive effects of associative memory, expertise and command strategy; (vii) an association between logical reasoning and word processing but not information retrieval; (viii) an interaction between expertise and cognitive demand; and (ix) a stronger association between cognitive ability and novice performance than expert performance.

Exploring ant-based algorithms for gene expression data analysis

Objective: Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper investigates ant-based algorithms for gene expression data clustering and associative classification. Methods and material: An ant-based clustering (Ant-C) and an ant-based association rule mining (Ant-ARM) algorithms are proposed for gene expression data analysis. The proposed algorithms make use of the natural behavior of ants such as cooperation and adaptation to allow for a flexible robust search for a good candidate solution. Results: Ant-C has been tested on the three datasets selected from the Stanford Genomic Resource Database and achieved relatively high accuracy compared to other classical clustering methods. Ant-ARM has been tested on the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) dataset and generated about 30 classification rules with high accuracy. Conclusions: Ant-C can generate optimal number of clusters without incorporating any other algorithms such as K-means or agglomerative hierarchical clustering. For associative classification, while a few of the well-known algorithms such as Apriori, FP-growth and Magnum Opus are unable to mine any association rules from the ALL/AML dataset within a reasonable period of time, Ant-ARM is able to extract associative classification rules.

Sustainable agricultural production:an investigation in Brazilian semi-arid livestock farms

This paper investigates the environmental sustainability and competitiveness perceptions of small farmers in a region in northern Brazil. The main data collection instruments included a survey questionnaire and an analysis of the region's strategic plan. In total, ninety-nine goat and sheep breeding farmers were surveyed. Data analysis methods included descriptive statistics, cluster analysis, and chi-squared tests. The main results relate to the impact of education, land size, and location on the farmers' perceptions of competitiveness and environmental issues. Farmers with longer periods of education have higher perception scores about business competitiveness and environmental sustainability than those with less formal education. Farmers who are working larger land areas also have higher scores than those with smaller farms. Lastly, location can yield factors that impact on farmers' perceptions. In our study, farmers located in Angicos and Lajes had higher perception scores than Pedro Avelino and Afonso Bezerra, despite the geographical proximity of these municipalities. On the other hand, three other profile variables did not impact on farmers' perceptions, namely: family income, dairy production volume, and associative condition. The authors believe the results and insights can be extended to livestock farming in other developing countries and contribute generally to fostering effective sustainable development policies, mainly in the agribusiness sector. © 2013 Elsevier Ltd. All rights reserved.

The data type of spatial objects

A spatial object consists of data assigned to points in a space. Spatial objects, such as memory states and three dimensional graphical scenes, are diverse and ubiquitous in computing. We develop a general theory of spatial objects by modelling abstract data types of spatial objects as topological algebras of functions. One useful algebra is that of continuous functions, with operations derived from operations on space and data, and equipped with the compact-open topology. Terms are used as abstract syntax for defining spatial objects and conditional equational specifications are used for reasoning. We pose a completeness problem: Given a selection of operations on spatial objects, do the terms approximate all the spatial objects to arbitrary accuracy? We give some general methods for solving the problem and consider their application to spatial objects with real number attributes. © 2011 British Computer Society.