Pre-existent expectancy effects in the relationship between caffeine and performance

The present study investigated the impact of pre-existent expectancy regarding the effects of the caffeine load of a drink and the perception of the caffeine content on subjective mood and vigilance performance. Caffeine deprived participants (N=25) were tested in four conditions (within subjects design), using a 2 × 2 design, with caffeine load and information regarding the caffeine content of the drink. In two sessions, they were given caffeinated coffee and in two were given decaffeinated coffee. Within these two conditions, on one occasion they were given accurate information about the drink and on the other they were given inaccurate information about the drink. Mood and vigilance performance were assessed post ingestion. Caffeine was found to enhance performance, but only when participants were accurately told they were receiving it. When decaffeinated coffee was given, performance was poorer, irrespective of expectancy. However, when caffeine was given, but participants were told it was decaffeinated coffee, performance was as poor as when no caffeine had been administered. There were no easily interpretable effects on mood. The pharmacological effects of caffeine appear to act synergistically with expectancy. © 2010.

First year qualifying report: neural networks for extracting wind vectors from satellite scatterometer data

The ERS-1 Satellite was launched in July 1991 by the European Space Agency into a polar orbit at about km800, carrying a C-band scatterometer. A scatterometer measures the amount of radar back scatter generated by small ripples on the ocean surface induced by instantaneous local winds. Operational methods that extract wind vectors from satellite scatterometer data are based on the local inversion of a forward model, mapping scatterometer observations to wind vectors, by the minimisation of a cost function in the scatterometer measurement space.par This report uses mixture density networks, a principled method for modelling conditional probability density functions, to model the joint probability distribution of the wind vectors given the satellite scatterometer measurements in a single cell (the `inverse' problem). The complexity of the mapping and the structure of the conditional probability density function are investigated by varying the number of units in the hidden layer of the multi-layer perceptron and the number of kernels in the Gaussian mixture model of the mixture density network respectively. The optimal model for networks trained per trace has twenty hidden units and four kernels. Further investigation shows that models trained with incidence angle as an input have results comparable to those models trained by trace. A hybrid mixture density network that incorporates geophysical knowledge of the problem confirms other results that the conditional probability distribution is dominantly bimodal.par The wind retrieval results improve on previous work at Aston, but do not match other neural network techniques that use spatial information in the inputs, which is to be expected given the ambiguity of the inverse problem. Current work uses the local inverse model for autonomous ambiguity removal in a principled Bayesian framework. Future directions in which these models may be improved are given.

Effects of caffeine on persuasion and attitude change:the role of secondary tasks in manipulating systematic message processing

Two experiments are reported that examine the effects of caffeine consumption on attitude change by using different secondary tasks to manipulate message processing. The first experiment employed an orientating task whilst the second experiment employed a distracter task. In both experiments participants consumed an orange-juice drink that either contained caffeine (3.5?mg/kg body weight) or did not contain caffeine (placebo) prior to reading a counter-attitudinal communication. The results across both experiments were similar. When message processing was reduced or under high distraction, there was no attitude change irrespective of caffeine consumption. However, when message processing was enhanced or under low distraction, there was greater attitude change in the caffeine vs. placebo conditions. Furthermore, attitudes formed after caffeine consumption resisted counter-persuasion (Experiment 1) and led to indirect attitude change (Experiment 2). The extent that participants engaged in message-congruent thinking mediated the amount of attitude change. These results provide evidence that moderate amounts of caffeine increase systematic processing of the arguments in the message resulting in greater agreement.

Caffeine, cognition, and persuasion:evidence for caffeine increasing the systematic processing of persuasive messages

Caffeine is known to increase arousal, attention, and information processing–all factors implicated in facilitating persuasion. In a standard attitude-change paradigm, participants consumed an orange-juice drink that either contained caffeine (3.5 mg/kg body weight) or did not (placebo) prior to reading a counterattitudinal communication (anti-voluntary euthanasia). Participants then completed a thought-listing task and a number of attitude scales. The first experiment showed that those who consumed caffeine showed greater agreement with the communication (direct attitude: voluntary euthanasia) and on an issue related to, but not contained in, the communication (indirect attitude: abortion). The order in which direct and indirect attitudes were measured did not affect the results. A second experiment manipulated the quality of the arguments in the message (strong vs. weak) to determine whether systematic processing had occurred. There was evidence that systematic processing occurred in both drink conditions, but was greater for those who had consumed caffeine. In both experiments, the amount of message-congruent thinking mediated persuasion. These results show that caffeine can increase the extent to which people systematically process and arc influenced by a persuasive communication.

The effects of caffeine consumption on direct and indirect majority and minority influence

A study is reported that examines the effect of caffeine consumption on majority and minority influence. In a double blind procedure, 72 participants consumed an orange drink, which either contained caffeine (3.5mg per kilogram of body weight) or did not (placebo). After a 40-minute delay, participants read a counter-attitudinal message (antivoluntary euthanasia) endorsed by either a numerical majority or minority. Both direct (message issue, i.e., voluntary euthanasia) and indirect (message issue-related, i.e., abortion) change was assessed by attitude scales completed before and after exposure to the message. In the placebo condition, the findings replicated the predictions of Moscovici's (1980) conversion theory; namely, majorities leading to compliance (direct influence) and minorities leading to conversion (indirect influence). When participants had consumed caffeine, majorities not only led to more direct influence than in the placebo condition but also to indirect influence. Minorities, by contrast, had no impact on either level of influence. The results suggest that moderate levels of caffeine increase systematic processing of the message but the consequences of this vary for each source. When the source is a majority there was increased indirect influence while for a minority there was decreased indirect influence. The results show the need to understand how contextual factors can affect social influence processes.

A novel process for the production of castor oil by direct solvent extraction

This research was undertaken to: develop a process for the direct solvent extraction of castor oil seeds. A literature survey confirmed the desirability of establishing such a process with emphasis on the decortication, size, reduction, detoxification-deallergenization, and solvent·extraction operations. A novel process was developed for the dehulling of castor seeds which consists of pressurizing the beans and then suddenly releasing the pressure to vaccum. The degree of dehulling varied according to the pressure applied and the size of the beans. Some of the batches were difficult-to-hull, and this phenomenon was investigated using the scanning electron microscope and by thickness and compressive strength measurements. The other variables studied to lesser degrees included residence time, moisture, content, and temperature.The method was successfully extended to cocoa beans, and (with modifications) to peanuts. The possibility of continuous operation was looked into, and a mechanism was suggested to explain the method works. The work on toxins and allergens included an extensive literature survey on the properties of these substances and the methods developed for their deactivation Part of the work involved setting up an assay method for measuring their concentration in the beans and cake, but technical difficulties prevented the completion of this aspect of the project. An appraisal of the existing deactivation methods was made in the course of searching for new ones. A new method of reducing the size of oilseeds was introduced in this research; it involved freezing the beans in cardice and milling them in a coffee grinder, the method was found to be a quick, efficient, and reliable. An application of the freezing technique was successful in dehulling soybeans and de-skinning peanut kernels. The literature on the solvent extraction, of oilseeds, especially castor, was reviewed: The survey covered processes, equipment, solvents, and mechanism of leaching. three solvents were experimentally investigated: cyclohexane, ethanol, and acetone. Extraction with liquid ammonia and liquid butane was not effective under the conditions studied. Based on the results of the research a process has been suggested for the direct solvent extraction of castor seeds, the various sections of the process have analysed, and the factors affecting the economics of the process were discussed.

Extracting protein-protein interactions from MEDLINE using the hidden vector state model

A major challenge in text mining for biomedicine is automatically extracting protein-protein interactions from the vast amount of biomedical literature. We have constructed an information extraction system based on the Hidden Vector State (HVS) model for protein-protein interactions. The HVS model can be trained using only lightly annotated data whilst simultaneously retaining sufficient ability to capture the hierarchical structure. When applied in extracting protein-protein interactions, we found that it performed better than other established statistical methods and achieved 61.5% in F-score with balanced recall and precision values. Moreover, the statistical nature of the pure data-driven HVS model makes it intrinsically robust and it can be easily adapted to other domains.

Extracting interactions between proteins from the literature

During the last decade, biomedicine has witnessed a tremendous development. Large amounts of experimental and computational biomedical data have been generated along with new discoveries, which are accompanied by an exponential increase in the number of biomedical publications describing these discoveries. In the meantime, there has been a great interest with scientific communities in text mining tools to find knowledge such as protein-protein interactions, which is most relevant and useful for specific analysis tasks. This paper provides a outline of the various information extraction methods in biomedical domain, especially for discovery of protein-protein interactions. It surveys methodologies involved in plain texts analyzing and processing, categorizes current work in biomedical information extraction, and provides examples of these methods. Challenges in the field are also presented and possible solutions are discussed.

Automatically extracting polarity-bearing topics for cross-domain sentiment classification

Joint sentiment-topic (JST) model was previously proposed to detect sentiment and topic simultaneously from text. The only supervision required by JST model learning is domain-independent polarity word priors. In this paper, we modify the JST model by incorporating word polarity priors through modifying the topic-word Dirichlet priors. We study the polarity-bearing topics extracted by JST and show that by augmenting the original feature space with polarity-bearing topics, the in-domain supervised classifiers learned from augmented feature representation achieve the state-of-the-art performance of 95% on the movie review data and an average of 90% on the multi-domain sentiment dataset. Furthermore, using feature augmentation and selection according to the information gain criteria for cross-domain sentiment classification, our proposed approach performs either better or comparably compared to previous approaches. Nevertheless, our approach is much simpler and does not require difficult parameter tuning.

Automatically Extracting Procedural Knowledge from Instructional Texts using Natural Language Processing

Procedural knowledge is the knowledge required to perform certain tasks, and forms an important part of expertise. A major source of procedural knowledge is natural language instructions. While these readable instructions have been useful learning resources for human, they are not interpretable by machines. Automatically acquiring procedural knowledge in machine interpretable formats from instructions has become an increasingly popular research topic due to their potential applications in process automation. However, it has been insufficiently addressed. This paper presents an approach and an implemented system to assist users to automatically acquire procedural knowledge in structured forms from instructions. We introduce a generic semantic representation of procedures for analysing instructions, using which natural language techniques are applied to automatically extract structured procedures from instructions. The method is evaluated in three domains to justify the generality of the proposed semantic representation as well as the effectiveness of the implemented automatic system.

Extracting significant words from corpora for ontology extraction

We show a new method for term extraction from a domain relevant corpus using natural language processing for the purposes of semi-automatic ontology learning. Literature shows that topical words occur in bursts. We find that the ranking of extracted terms is insensitive to the choice of population model, but calculating frequencies relative to the burst size rather than the document length in words yields significantly different results.

Extracting topical phrases from clinical documents

In clinical documents, medical terms are often expressed in multi-word phrases. Traditional topic modelling approaches relying on the “bag-of-words” assumption are not effective in extracting topic themes from clinical documents. This paper proposes to first extract medical phrases using an off-the-shelf tool for medical concept mention extraction, and then train a topic model which takes a hierarchy of Pitman-Yor processes as prior for modelling the generation of phrases of arbitrary length. Experimental results on patients’ discharge summaries show that the proposed approach outperforms the state-of-the-art topical phrase extraction model on both perplexity and topic coherence measure and finds more interpretable topics.