Self-Organizing Information Fusion and Hierarchical Knowledge Discovery: A New Framework Using Artmap Neural Networks

Classifying novel terrain or objects from sparse, complex data may require the resolution of conflicting information from sensors woring at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when eveidence variously suggests that and object's class is car, truck, or airplane. The methods described her address a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among classes are assumed to be unknown to the autonomated system or the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierachical knowlege structures. The fusion system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples, but is not limited to image domain.

Talking about the genes for cancer. A study of lay and professional knowledge of cancer genetics

This paper is concerned with the ways in which people who work in and use a cancer genetics clinic in the UK talk about the ‘gene for cancer’. By conceptualising such a gene as a boundary object, and using empirical data derived from clinic consultations, observations in a genetics laboratory and interviews with patients, the author seeks to illustrate how the various parties involved adopt different discursive strategies to appropriate, describe and understand what is apparently the ‘same’ thing. The consequent focus on the ways in which the rhetorical and syntactical features of lay and professional talk interlink and diverge, illustrates not merely how our contemporary knowledge of genes and genetics is structured, but also how different publics position themselves with respect to the biochemistry of life.

Analysis of the admissions tests for teacher training in Spain and Finland: knowledge or competences

One of the most decisive factors in the quality of education and academic performance of students is quality, preparation and dedication of the teachers. The exquisite system of selecting candidates for teacher training programs is one of the fundamentals of success of the Finnish Education System. The responsibility of choosing the best students to convert them into teachers is a challenge that involves a significant reform of university admission. Achieving this goal involves the choice of strategies and educational tools in accordance to the complexity of the demands presented by the teaching profession in the digital age. This study describes, analyzes and compares the admission tests in the University of Spain (PAU) and Finland (VAKAVA), for those who wish to become professional educators, in order to understand the possible influence of these tests to select the most suitable candidates to develop into future teaching professionals. The results showed that in Spain, the entrance test to universities is developed in a general way for all the students that aspire to any field of knowledge, while in Finland, the test is specific and particular for students aspiring to the field of education. The results of this study can guide and encourage the necessary changes that have to be done in the admission tests to Spanish university in general and to teacher education faculties in particular.

Staffing, status and subject knowledge: what does the construction of citizenship as a new curriculum subject in England tell us about the nature of school subjects?

Almost a decade ago, the new subject of citizenship was created in the English National Curriculum and several universities were funded to train teachers in this new subject. This presented a rare challenge, namely how to train people to teach a subject that did not exist in schools, and in which they were unlikely to have a specialist degree. In this article we have taken the opportunity afforded by the
tenth birthday of the report in which Crick recommended this curriculum reform to reflect on that experience from the perspective of teacher educators. Through reflecting on the case study of citizenship education in England we highlight several themes that are of more general interest to teacher educators. The key issues that have emerged in this case study relate to the general problems of translating central policy into classroom practice; the nature and aims of subjects in the curriculum; and the identities of teachers in secondary schools. The article illustrates how teacher educators responded to the formidable challenge of creating (or at least contributing to) a new subject and a subject community.

The Engine of Thought Is a Hybrid: Roles of Associative and Structured Knowledge in Reasoning

Across a range of domains in psychology different theories assume different mental representations of knowledge. For example, in the literature on category-based inductive reasoning, certain theories (e.g., Rogers & McClelland, 2004; Sloutsky & Fisher, 2008) assume that the knowledge upon which inductive inferences are based is associative, whereas others (e.g., Heit & Rubinstein, 1994; Kemp & Tenenbaum, 2009; Osherson, Smith, Wilkie, López, & Shafir, 1990) assume that knowledge is structured. In this article we investigate whether associative and structured knowledge underlie inductive reasoning to different degrees under different processing conditions. We develop a measure of knowledge about the degree of association between categories and show that it dissociates from measures of structured knowledge. In Experiment 1 participants rated the strength of inductive arguments whose categories were either taxonomically or causally related. A measure of associative strength predicted reasoning when people had to respond fast, whereas causal and taxonomic knowledge explained inference strength when people responded slowly. In Experiment 2, we also manipulated whether the causal link between the categories was predictive or diagnostic. Participants preferred predictive to diagnostic arguments except when they responded under cognitive load. In Experiment 3, using an open-ended induction paradigm, people generated and evaluated their own conclusion categories. Inductive strength was predicted by associative strength under heavy cognitive load, whereas an index of structured knowledge was more predictive of inductive strength under minimal cognitive load. Together these results suggest that associative and structured models of reasoning apply best under different processing conditions and that the application of structured knowledge in reasoning is often effortful.