From bird to sparrow: Learning-induced modulations in fine-grained semantic discrimination.

Recognition of environmental sounds is believed to proceed through discrimination steps from broad to more narrow categories. Very little is known about the neural processes that underlie fine-grained discrimination within narrow categories or about their plasticity in relation to newly acquired expertise. We investigated how the cortical representation of birdsongs is modulated by brief training to recognize individual species. During a 60-minute session, participants learned to recognize a set of birdsongs; they improved significantly their performance for trained (T) but not control species (C), which were counterbalanced across participants. Auditory evoked potentials (AEPs) were recorded during pre- and post-training sessions. Pre vs. post changes in AEPs were significantly different between T and C i) at 206-232ms post stimulus onset within a cluster on the anterior part of the left superior temporal gyrus; ii) at 246-291ms in the left middle frontal gyrus; and iii) 512-545ms in the left middle temporal gyrus as well as bilaterally in the cingulate cortex. All effects were driven by weaker activity for T than C species. Thus, expertise in discriminating T species modulated early stages of semantic processing, during and immediately after the time window that sustains the discrimination between human vs. animal vocalizations. Moreover, the training-induced plasticity is reflected by the sharpening of a left lateralized semantic network, including the anterior part of the temporal convexity and the frontal cortex. Training to identify birdsongs influenced, however, also the processing of C species, but at a much later stage. Correct discrimination of untrained sounds seems to require an additional step which results from lower-level features analysis such as apperception. We therefore suggest that the access to objects within an auditory semantic category is different and depends on subject's level of expertise. More specifically, correct intra-categorical auditory discrimination for untrained items follows the temporal hierarchy and transpires in a late stage of semantic processing. On the other hand, correct categorization of individually trained stimuli occurs earlier, during a period contemporaneous with human vs. animal vocalization discrimination, and involves a parallel semantic pathway requiring expertise.

Could machine learning improve the prediction of pelvic nodal status of prostate cancer patients? Preliminary results of a pilot study.

We tested and compared performances of Roach formula, Partin tables and of three Machine Learning (ML) based algorithms based on decision trees in identifying N+ prostate cancer (PC). 1,555 cN0 and 50 cN+ PC were analyzed. Results were also verified on an independent population of 204 operated cN0 patients, with a known pN status (187 pN0, 17 pN1 patients). ML performed better, also when tested on the surgical population, with accuracy, specificity, and sensitivity ranging between 48-86%, 35-91%, and 17-79%, respectively. ML potentially allows better prediction of the nodal status of PC, potentially allowing a better tailoring of pelvic irradiation.

Los enfoques de aprendizaje en estudiantes universitarios Catalanes mediante el approaches and study skills inventory for students (ASSIST) = Learning Approaches of Catalan University Students Measured with the Approaches and Study Skills Inventory for Students (ASSIST)

The aim of this study is to measure the psychometric properties of a Catalan translation of the Approaches and Study Skills Inventory for Students (ASSIST), and to analyse the different learning styles used by university students, considering the influence of gender and type of studies. The instrument was administered to 834 students at the University of Girona. The results showed that most students interviewed had a deep approach to learning, although the analysis by gender showed that females tended to use a more strategic approach, while males used a deep approach predominantly. As to whether the type of studies influenced learning styles, a prevalence of deep approach was found among Science and Technology students, while a more strategic approach was found among Humanities and Education students

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

Análisis de una experiencia de aprendizaje basado en proyectos en la asignatura de Fundamentos de Física = Project based learning: analysis of an experience in Introductory Physics courses

In this work we present and analyze the application of an experience of Project Based Learning (PBL) in the matter of Physics II of the Industrial Design university degree (Girona University) during 2005-2006 courses. This methodology was applied to the Electrostatic and Direct Current subjects. Furthermore, evaluation and self evaluation results were shown and the academic results were compared with results obtained in the same subjects applying conventional teaching methods

Gestión de la actividad no presencial y autoevaluación de competencias = Management of Student Learning Outside the Classroom and the Self-evaluation of Competences

Psychological Assessment is a core subject of Psychology studies, and of the university degree Human development, at the University of Girona and according to the University regulations, had 12 credits. Until the 2005-06 academic years, the student work conducted outside the classroom consisted of undertaking a psychological assessment that was written and handed in at the end of the course. From this the student obtained a qualification and a review if they applied for it In accordance with the European Credits for Higher Education, this subject was reduced to 9 credits, which is the equivalent of a total of 255 hours of in-class work and outside the classroom. In the 2006-07 academic year we created a guide to manage the student’s experiences outside the classroom, with the objective of encouraging the application of problem solving/critical thinking (Bloom, 1975), in line with the recommendations of the Catalonia Agency for University System Quality (2005). The guide includes: learning objectives, evaluation criterions, a description of activities, work week timetable for the whole course, programmed tutorials to review all steps of the psychological assessment process, and the use of a web-based virtual forum for the transfer of knowledge, analysis and constructive critiques of the assessment done by themselves and their colleagues

The motivation to learn as a self-presentation tool among Swiss high school students : The moderating role of mastery goals' perceived social value on learning

Although it has been assumed that the motivation to learn - or mastery goal endorsement - positively predicts learning achievement, most empirical findings fail to demonstrate this relationship. In the present research, conducted in a Swiss high school, we adopted a social value approach to test the hypothesis that adolescent students' mastery goals do in fact predict learning, but only if these goals are perceived as highly useful for scholarly success (high social utility), and are not endorsed as a means to be appreciated by the teachers (low social desirability), a finding that has previously been observed among college students and on teacher-graded achievement measures only. Results demonstrate that in spite of potential peculiarities of an adolescent population, individual differences in mastery goals' perceived social utility and desirability moderate the mastery goal endorsement-learning achievement relation. Findings are discussed with regard to both theory development and educational practice.

Literature report : a review of policy learning in five strands of political science research

This paper reviews the policy learning literature in political science. In recent years, the number of publications on learning in the political realm increased dramatically. Researchers have focused on how policymakers and administrators adapt policies based on learning processes or experiences. Thereby, learning has been discussed in very different ways. Authors have referred to learning in the context of ideas, understood as deeply held beliefs, and, as change and adaptation of policy instruments. Two other strands of literature have taken into consideration learning, namely the diffusion literature and research on transfer, which put forward learning to understand who learns from whom and what. Opposed to these views, political learning emphasizes the adaptation of new strategies by policymakers over the transfer of knowledge or broad ideas. In this approach, learning occurs due to the failure of existing policies, increasing problem pressure, scientific innovations, or a combination of these elements.

What can ecosystems learn? Expanding evolutionary ecology with learning theory.

BACKGROUND: The structure and organisation of ecological interactions within an ecosystem is modified by the evolution and coevolution of the individual species it contains. Understanding how historical conditions have shaped this architecture is vital for understanding system responses to change at scales from the microbial upwards. However, in the absence of a group selection process, the collective behaviours and ecosystem functions exhibited by the whole community cannot be organised or adapted in a Darwinian sense. A long-standing open question thus persists: Are there alternative organising principles that enable us to understand and predict how the coevolution of the component species creates and maintains complex collective behaviours exhibited by the ecosystem as a whole? RESULTS: Here we answer this question by incorporating principles from connectionist learning, a previously unrelated discipline already using well-developed theories on how emergent behaviours arise in simple networks. Specifically, we show conditions where natural selection on ecological interactions is functionally equivalent to a simple type of connectionist learning, 'unsupervised learning', well-known in neural-network models of cognitive systems to produce many non-trivial collective behaviours. Accordingly, we find that a community can self-organise in a well-defined and non-trivial sense without selection at the community level; its organisation can be conditioned by past experience in the same sense as connectionist learning models habituate to stimuli. This conditioning drives the community to form a distributed ecological memory of multiple past states, causing the community to: a) converge to these states from any random initial composition; b) accurately restore historical compositions from small fragments; c) recover a state composition following disturbance; and d) to correctly classify ambiguous initial compositions according to their similarity to learned compositions. We examine how the formation of alternative stable states alters the community's response to changing environmental forcing, and we identify conditions under which the ecosystem exhibits hysteresis with potential for catastrophic regime shifts. CONCLUSIONS: This work highlights the potential of connectionist theory to expand our understanding of evo-eco dynamics and collective ecological behaviours. Within this framework we find that, despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal conditions that habituate to past environmental conditions and actively recalling those conditions. REVIEWERS: This article was reviewed by Prof. Ricard V Solé, Universitat Pompeu Fabra, Barcelona and Prof. Rob Knight, University of Colorado, Boulder.

Labour market innovations and policy learning : report on Switzerland

This paper analyses learning and implementation of labour market reforms in Switzerland.

Best practice report on policy learning infrastructures in innovative labour market policies

This report synthesizes the findings of 11 country reports on policy learning in labour market and social policies that were conducted as part of WP5 of the INSPIRES project, which is funded by the 7th Framework Program of the EU-Commission. Notably, this report puts forward objectives of policy learning, discusses tools, processes and institutions of policy learning and presents the impacts of various tools and structures of the policy learning infrastructure for the actual policy learning process. The report defines three objectives of policy learning: evaluation and assessment of policy effectiveness, vision building and planning, and consensus building. In the 11 countries under consideration, the tools and processes of the policy learning, infrastructure can be classified into three broad groups: public bodies, expert councils, and parties, interest groups and the private sector. Finally, we develop four recommendations for policy learning: Firstly, learning processes should keep the balance between centralisation and plurality. Secondly, learning processes should be kept stable beyond the usual political business cycles. Thirdly, policy learning tools and infrastructures should be sufficiently independent from political influence or bias. Fourth, Policy learning tools and infrastructures should balance out mere effectiveness, evaluation and vision building.

Policy learning in Europe : social policy and labour market reforms in 11 countries

This report compares policy learning processes in 11 European countries. Based on the country reports that were produced by the national teams of the INSPIRES project, this paper develops an argument that connects problem pressure and politicization to learning in different labor market innovations. In short, we argue that learning efforts are most likely to impact on policy change if there is a certain problem pressure that clearly necessitates political action. On the other hand, if problem pressure is very low, or so high that governments need to react immediately, chances are low that learning impacts on policy change. The second part of our argument contends that learning impacts on policy change especially if a problem is not very politicized, i.e. there are no main conflicts concerning a reform, because then, solutions are wound up in the search for a compromise. Our results confirm our first hypothesis regarding the connection between problem pressure and policy learning. Governments learn indeed up to a certain degree of problem pressure. However, once political action becomes really urgent, i.e. in anti-crisis policies, there is no time and room for learning. On the other hand, learning occurred independently from the politicization of problem. In fact, in countries that have a consensual political system, learning occurred before the decision on a reform, whereas in majoritarian systems, learning happened after the adoption of a policy during the process of implementation.

Debating as a classroom tool for adapting learning outcomes to the European higher education area

The creation of the European Higher Education Area has meant a number of significant changes to the educational structures of the university community. In particular, the new system of European credits has generated the need for innovation in the design of curricula and teaching methods. In this paper, we propose debating as a classroom tool that can help fulfill these objectives by promoting an active student role in learning. To demonstrate the potential of this tool, a classroom experiment was conducted in a bachelor’s degree course in Industrial Economics -Regulation and Competition-, involving a case study in competition policy and incorporating the techniques of a conventional debate -presentation of standpoints, turns, right to reply and summing up-. The experiment yielded gains in student attainment and positive assessments of the subject. In conclusion, the incorporation of debating activities helps students to acquire the skills, be they general or specific, required to graduate successfully in Economics.