Understanding the mnemonic functions of the brain has been extensively facilitated by the development of functional neuroimaging techniques such as positron emission tomography (PET) and functional magnetic resonance imaging (fMRI). The present thesis aims at investigating the neural mechanisms underlying memory for personally experienced events (episodic memory), using PET. In paper I, similarities between encoding and retrieval of enacted (motor) information were explored. We observed increased retrieval activation in right premotor areas in the brain when sentences encoded by motor enactment and sentences encoded by maintenance rehearsal were contrasted. In paper II, overlap between encoding and retrieval was explicitly tested for three types of event information: spatial, item, and temporal. Using conjunction analyses, we found that encoding and retrieval of spatial information was associated with increased brain activity in bilateral inferior parietal regions. Encoding and retrieval of item information were related to increased activation in right inferior temporal cortex, and encoding and retrieval of temporal information were associated with increased activation in left inferior temporal and left inferior frontal cortex. In paper III, brain activity associated with retrieval success was examined. Conditions included three levels of retrieval success (high, medium, and low level), for two types of information (pictures and sentences). The results showed a pattern of activation that distinguished between brain regions involved in processing of sentences vs. processing of pictures. A second pattern that distinguished between brain regions involved in encoding vs. retrieval processes, irrespectively of material (sentences and pictures) and retrieval success, was also found. The manipulation of retrieval success was associated with systematic changes in the correlation between material specific regions and other areas of the brain. In study IV, changes in activation related to successful retrieval of pictures were investigated. More specifically, we expected to find decreases in infero-temporal (IT) regions of the brain that were associated with successful recognition memory. As expected, we found a region in left IT cortex that showed decreased activation related to memory for event information. This decrease in activation could be dissociated from responses related to novelty detection, and perceptual priming. The results from study I and II are discussed in relation to findings and theories regarding similarities between encoding and retrieval processes, and reactivation of modality-specific brain areas important for memory storage. The results from studies III and IV are discussed in relation to differences between encoding and retrieval processes, e.g. asymmetric frontal activation and sub-processes of episodic memory, such as retrieval mode, retrieval success, and novelty detection. Taken together, the studies show that different episodic memory processes are correlated with distinct brain areas, hence supporting the view that remembering is based on multiple component processes. / digitalisering@umu.se

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Typescript (photocopy). / Digitized by Kansas Correctional Industries

Module extraction is an important reasoning task, aiding in the design, reuse and maintenance of ontologies. Reasoning services such as subsumption testing and MinA extraction have been shown to bene t from module extraction methods. Though various syntactic traversal-based module extraction algorithms exist for extracting modules, many only consider the subsumee of a subsumption statement as a selection criterion for reducing the axioms in the module. In this dissertation we extend the bottom-up reachability-based module extraction heuristic for the inexpressive Description Logic EL, by introducing a top-down version of the heuristic which utilises the subsumer of a subsumption statement as a selection criterion to minimize the number of axioms in a module. Then a combined bidirectional heuristic is introduced which uses both operands of a subsumption statement in order to extract very small modules. We then investigate the relationship between MinA extraction and bidirectional reachabilitybased module extraction. We provide empirical evidence that bidirectional reachability-based module extraction for subsumption entailments in EL provides a signi cant reduction in the size of modules for almost no additional costs in the running time of the original algorithms. / Computer Science / M. Sc. (Computer Science)

Case-Based Reasoning (CBR) is a technique in Artificial Intelligence where a new problem is solved by making use of the solution to a similar past problem situation. People naturally solve problems in this way, without even thinking about it. For example, an occupational therapist (OT) that assesses the needs of a new disabled person may be reminded of a previous person in terms of their disabilities. He may or may not decide to recommend the same devices based on the outcome of an earlier (disabled) person. Case-based reasoning makes use of a collection of past problem-solving experiences thus enabling users to exploit the information of others’ successes and failures to solve their own problem(s). This project has developed a CBR tool to assist in matching SmartHouse technology to the needs of the elderly and people with disabilities. The tool makes suggestions of SmartHouse devices that could assist with given impairments. SmartHouse past problem-solving textual reports have been used to obtain knowledge for the CBR system. Creating a case-based reasoning system from textual sources is challenging because it requires that the text be interpreted in a meaningful way in order to create cases that are effective in problem-solving and to be able to reasonably interpret queries. Effective case retrieval and query interpretation is only possible if a domain-specific conceptual model is available and if the different meanings that a word can take can be recognised in the text. Approaches based on methods in information retrieval require large amounts of data and typically result in knowledge-poor representations. The costs become prohibitive if an expert is engaged to manually craft cases or hand tag documents for learning. Furthermore, hierarchically structured case representations are preferred to flat-structured ones for problem-solving because they allow for comparison at different levels of specificity thus resulting in more effective retrieval than flat structured cases. This project has developed SmartCAT-T, a tool that creates knowledge-rich hierarchically structured cases from semi-structured textual reports. SmartCAT-T highlights important phrases in the textual SmartHouse problem-solving reports and uses the phrases to create a conceptual model of the domain. The model then becomes a standard structure onto which each semi-structured SmartHouse report is mapped in order to obtain the correspondingly structured case. SmartCAT-T also relies on an unsupervised methodology that recognises word synonyms in text. The methodology is used to create a uniform vocabulary for the textual reports and the resulting harmonised text is used to create the standard conceptual model of the domain. The technique is also employed in query interpretation during problem solving. SmartCAT-T does not require large sets of tagged data for learning, and the concepts in the conceptual model are interpretable, allowing for expert refinement of knowledge. Evaluation results show that the created cases contain knowledge that is useful for problem solving. An improvement in results is also observed when the text and queries are harmonised. A further evaluation highlights a high potential for the techniques developed in this research to be useful in domains other than SmartHouse. All this has been implemented in the Smarter case-based reasoning system.

published_or_final_version / Business Administration / Master / Master of Business Administration

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This paper reports on an exploratory study that investigates the relationship between participants' learning style preferences and their perceptions of a professional workshop on collaboration and technology to support collaboration. The Learning Preference Scale-Students (LPSS) (Owens & Barnes, 1992) was administered to identify participants' learning style preferences as cooperative, competitive and/or individualized. Using cluster analysis two groups, or categories, of learning style preferences among the participants emerged. Group 1 showed a strong preference for the cooperative learning style, and Group 2 showed a strong preference for competitive and cooperative learning styles. Group 1 rated the workshop more positively than Group 2. However, Group 2 reported a larger increase in self-efficacy compared to those in Group 1 (18.9% vs. 6.0%). Both groups provided different suggestions regarding the content of the workshop. Group 1 suggested adding more discussions and group exercises, whereas Group 2 suggested adding explicit theory or rules to govern behavior. These findings indicate that learning styles should be considered as a potential variable that influences learning outcomes and preferences.

Organizations must consider increasing their decision-making capabilities in order to remain viable in a post-industrial society that Huber characterized as having "more and increasing knowledge, more and increasing complexity, and more and increasing turbulence" (1984). He sees the challenge for managers in the post-industrial environment as learning to make decisions in less time using greater quantities of more complex information. Group Decision Support Systems (GDSSs) represent a computer-based technology that has the potential to increase an organization's decision-making capabilities, and to meet this post-industrial challenge. This dissertation investigated a specific GDSS to study how GDSS technology affects group decision making compared to the more traditional face-to-face group decision making. The research was conducted through the use of a laboratory study comparing face-to-face groups of size six to GDSS groups of the same size. The decision process was the same for both types of groups, i.e., the sequence of steps used to solve the problem was consistent for both. Additionally, all of the groups were given the same task. Process and decision outcomes were measured for the six sets of treatments considered feasible for the manipulation of the communication condition, leadership, and anonymity. The process outcomes included satisfaction, time to decision, consensus, participation and uninhibited comments. The quality of a group's decision was the decision outcome measurement. The major findings of this study are: (1) Decision quality was equivalent for both face-to-face and GDSS groups; (2) Time to decision was greater for GDSS; (3) Consensus was less likely to occur in GDSS groups; (4) Satisfaction was lower in GDSS groups; (5) Participation was more equitable in GDSS groups.

A multispectral image data compression scheme has been investigated in which a scene is imaged onto a detector array whose elements vary in spectral sensitivity. The elements are staggered such that the scene is undersampled within any single spectral band, but is sufficiently sampled by the total array. Compression thus results from transmitting only one spectral component of a scene at any given array coordinate. The pixels of the mosaic array may then be directly transmitted via PCM or undergo further compression (e.g. DPCM). The scheme has the advantages of attaining moderate compression without compression hardware at the transmitter, high compression with low-order DPCM processing, and a choice of reconstruction algorithms suitable to the application at hand. Efficient spatial interpolators such as parametric cubic convolution may be employed to fill in the missing pixels in each spectral band in cases where high resolution is not a requirement. However, high-resolution reconstructions are achieved by a space-variant minimum-mean-square spectral regression estimation of the missing pixels of each band from the adjacent samples of other bands. In this case, reconstruction accuracy is determined by the local spectral correlations between bands, the estimates of which include the effects of interband contrast reversal. Digital simulations have been performed on three-band aerial and four-band Landsat multispectral images. Spectral regressions of mosaic array data can provide reconstruction errors comparable to second-order DPCM processing and lower than common intraband interpolators at data rates of approximately 2 bits per pixel. When the mosaic data is itself DPCM-coded, the radiometric accuracy of spectral regression is superior to direct DPCM for equivalent bit rates.