Embodying feminist research: learning from action research, political practices, diffractions and collective knowledge.

In the past three decades, feminists and critical theorists have discussed and argued the importance of deconstructing and problematizing social science research methodology in order to question normalized hierarchies concerning the production of knowledge and the status of truth claims. Nevertheless, often, these ideas have basically remained theoretical propositions not embodied in research practices. In fact there is very little published discussion about the difficulties and limits of their practical application. In this paper we introduce some interconnected reflections starting from two different but related experiences of embodying 'feminist activist research'. Our aim is to emphasise the importance of attending to process, making mistakes and learning during fieldwork, as well as experimenting with personalized forms of analysis, such as the construction of narratives and the story-telling process.

Self construction, cognitive conflicts and polarization in bulimia nervosa

This study explores the cognitive structures, understood as construct systems, of patients suffering from bulimia nervosa (BN). Previous studies investigated the construct systems of disordered eaters suggesting that they had a higher distance between their construction of the self and the «ideal self», and also more rigidity. In addition to these aspects, this study explored the presence of implicative dilemmas (ID). Thirty two women who met criteria for BN and were treated in a specialized center were compared to a non clinical group composed by 32 women matched by age. All participants were assessed using Repertory Grid Technique (RGT). In BN patients it was more common (71.9%) to find IDs than in controls (18.8%). They also showed higher polarization and higher self-ideal discrepancies (even more for those with a long history of BN). The measures provided by the RGT can be useful for the assessment of self-construction and cognitive conflicts in BN patients and to appreciate their role in this disorder. In addition, this technique could be helpful for clinicians to explore the patient"s constructs system, and specially to identify IDs that could be maintaining the symptoms or hindering change in order to focus on them to facilitate improvement.

On the relationship between performance-approach goals and cognitive performance : when the desire to outperform others becomes interfering

Cette thèse explore dans quelle mesure la poursuite d'un but de performance-approche (i.e., le désir de surpasser autrui et de démontrer ses compétences) favorise, ou au contraire endommage, la réussite et l'apprentissage-une question toujours largement débattue dans la littérature. Quatre études menées en laboratoire ont confirmé cette hypothèse et démontré que la poursuite du but de performance-approche amène les individus à diviser leur attention entre d'une part la réalisation de la tâche évaluée, et d'autre part la gestion de préoccupations liées à l'atteinte du but-ceci empêchant une concentration efficace sur les processus de résolution de la tâche. Dans une deuxième ligne de recherche, nous avons ensuite démontré que cette distraction est exacerbée chez les individus les plus performants et ayant le plus l'habitude de réussir, ceci dérivant d'une pression supplémentaire liée au souhait de maintenir le statut positif de « bon élève ». Enfin, notre troisième ligne de recherche a cherché à réconcilier ces résultats-pointant l'aspect distractif du but de performance-approche-avec le profil se dégageant des études longitudinales rapportées dans la littérature-associant ce but avec la réussite académique. Ainsi, nous avons mené une étude longitudinale testant si l'adoption du but de performance-approche en classe pourrait augmenter la mise en oeuvre de stratégies d'étude tactiquement dirigées vers la performance-favorisant une réussite optimale aux tests. Nos résultats ont apporté des éléments en faveur de cette hypothèse, mais uniquement chez les élèves de bas niveau. Ainsi, l'ensemble de nos résultats permet de mettre en lumière les processus cognitifs à l'oeuvre lors de la poursuite du but de performance-approche, ainsi que d'alimenter le débat concernant leur aspect bénéfique ou nuisible en contexte éducatif. -- In this dissertation, we propose to investigate whether the pursuit of performance-approach goals (i.e., the desire to outperform others and appear talented) facilitates or rather endangers achievement and learning-an issue that is still widely discussed in the achievement goal literature. Four experiments carried out in a laboratory setting have provided evidence that performance- approach goals create a divided-attention situation that leads cognitive resources to be divided between task processing and the activation of goal-attainment concerns-which jeopardizes full cognitive immersion in the task. Then, in a second research line, we found evidence that high- achievers (i.e., those individuals who are the most used to succeed) experience, under evaluative contexts, heightened pressure to excel at the task, deriving from concerns associated with the preservation of their "high-achiever" status. Finally, a third research line was designed to try to reconcile results stemming from our laboratory studies with the overall profile emerging from longitudinal research-which have consistently found performance-approach goals to be a positive predictor of students' test scores. We thus set up a longitudinal study so as to test whether students' adoption of performance-approach goals in a long-term classroom setting enhances the implementation of strategic study behaviors tactically directed toward goal-attainment, hence favoring test performance. Our findings brought support for this hypothesis, but only for low-achieving students. Taken together, our findings shed new light on the cognitive processes at play during the pursuit of performance-approach goals, and are likely to fuel the debate regarding whether performance-approach goals should be encouraged or not in educational settings.

Descartes wears Converse: Liberal Arts for the Twenty-First Century Student

Students today have a different way of relating to information due to the new media channels that have arisen in the last decades. These have changed the way high-school and undergraduate students learn and they have altered the manner by which they perceive the world. Today’s Education Theory must take this fact into account in order to enhance the student’s learning process. The objective of this project is to give an example of how this enhancement may be achieved. First, it will give a brief overview of the relation between today’s young generations and the different channels of information; secondly, it will analyze the cognitive, psychological and educational theories that explain how the human brain learns and the important value that nonverbal information has for the memory system; afterwards, it will focus on this nonverbal information, looking at the possible effects that it may have on human memory and learning; finally, it will give an example of the practical implementation of this theory through the presentation of three animated instructional videos that have been created with the specific aim of enhancing the young generation’s understanding of some complex subjects of the Liberal Arts.

Dietary restriction affects lifespan but not cognitive aging in Drosophila melanogaster.

Summary Dietary restriction extends lifespan in a wide variety of animals, including Drosophila, but its relationship to functional and cognitive aging is unclear. Here, we study the effects of dietary yeast content on fly performance in an aversive learning task (association between odor and mechanical shock). Learning performance declined at old age, but 50-day-old dietary-restricted flies learned as poorly as equal-aged flies maintained on yeast-rich diet, even though the former lived on average 9 days (14%) longer. Furthermore, at the middle age of 21 days, flies on low-yeast diets showed poorer short-term (5 min) memory than flies on rich diet. In contrast, dietary restriction enhanced 60-min memory of young (5 days old) flies. Thus, while dietary restriction had complex effects on learning performance in young to middle-aged flies, it did not attenuate aging-related decline of aversive learning performance. These results are consistent with the hypothesis that, in Drosophila, dietary restriction reduces mortality and thus leads to lifespan extension, but does not affect the rate with which somatic damage relevant for cognitive performance accumulates with age.

Did tool-use evolve with enhanced physical cognitive abilities?

The use and manufacture of tools have been considered to be cognitively demanding and thus a possible driving factor in the evolution of intelligence. In this study, we tested the hypothesis that enhanced physical cognitive abilities evolved in conjunction with the use of tools, by comparing the performance of naturally tool-using and non-tool-using species in a suite of physical and general learning tasks. We predicted that the habitually tool-using species, New Caledonian crows and Galápagos woodpecker finches, should outperform their non-tool-using relatives, the small tree finches and the carrion crows in a physical problem but not in general learning tasks. We only found a divergence in the predicted direction for corvids. That only one of our comparisons supports the predictions under this hypothesis might be attributable to different complexities of tool-use in the two tool-using species. A critical evaluation is offered of the conceptual and methodological problems inherent in comparative studies on tool-related cognitive abilities.

Cerebellar lesions: is there a lateralisation effect on memory deficits?

BACKGROUND: Until recently, neurosurgeons eagerly removed cerebellar lesions without consideration of future cognitive impairment that might be caused by the resection. In children, transient cerebellar mutism after resection has lead to a diminished use of midline approaches and vermis transection, as well as reduced retraction of the cerebellar hemispheres. The role of the cerebellum in higher cognitive functions beyond coordination and motor control has recently attracted significant interest in the scientific community, and might change the neurosurgical approach to these lesions. The aim of this study was to investigate the specific effects of cerebellar lesions on memory, and to assess a possible lateralisation effect. METHODS: We studied 16 patients diagnosed with a cerebellar lesion, from January 1997 to April 2005, in the "Centre Hospitalier Universitaire Vaudois (CHUV)", Lausanne, Switzerland. Different neuropsychological tests assessing short term and anterograde memory, verbal and visuo-spatial modalities were performed pre-operatively. RESULTS: Severe memory deficits in at least one modality were identified in a majority (81%) of patients with cerebellar lesions. Only 1 patient (6%) had no memory deficit. In our series lateralisation of the lesion did not lead to a significant difference in verbal or visuo-spatial memory deficits. FINDINGS: These findings are consistent with findings in the literature concerning memory deficits in isolated cerebellar lesions. These can be explained by anatomical pathways. However, the cross-lateralisation theory cannot be demonstrated in our series. The high percentage of patients with a cerebellar lesion who demonstrate memory deficits should lead us to assess memory in all patients with cerebellar lesions.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

On learning dynamics underlying the evolution of learning rules.

In order to understand the development of non-genetically encoded actions during an animal's lifespan, it is necessary to analyze the dynamics and evolution of learning rules producing behavior. Owing to the intrinsic stochastic and frequency-dependent nature of learning dynamics, these rules are often studied in evolutionary biology via agent-based computer simulations. In this paper, we show that stochastic approximation theory can help to qualitatively understand learning dynamics and formulate analytical models for the evolution of learning rules. We consider a population of individuals repeatedly interacting during their lifespan, and where the stage game faced by the individuals fluctuates according to an environmental stochastic process. Individuals adjust their behavioral actions according to learning rules belonging to the class of experience-weighted attraction learning mechanisms, which includes standard reinforcement and Bayesian learning as special cases. We use stochastic approximation theory in order to derive differential equations governing action play probabilities, which turn out to have qualitative features of mutator-selection equations. We then perform agent-based simulations to find the conditions where the deterministic approximation is closest to the original stochastic learning process for standard 2-action 2-player fluctuating games, where interaction between learning rules and preference reversal may occur. Finally, we analyze a simplified model for the evolution of learning in a producer-scrounger game, which shows that the exploration rate can interact in a non-intuitive way with other features of co-evolving learning rules. Overall, our analyses illustrate the usefulness of applying stochastic approximation theory in the study of animal learning.

Virulent bacterial infection improves aversive learning performance in Drosophila melanogaster.

Virulent infections are expected to impair learning ability, either as a direct consequence of stressed physiological state or as an adaptive response that minimizes diversion of energy from immune defense. This prediction has been well supported for mammals and bees. Here, we report an opposite result in Drosophila melanogaster. Using an odor-mechanical shock conditioning paradigm, we found that intestinal infection with bacterial pathogens Pseudomonas entomophila or Erwinia c. carotovora improved flies' learning performance after a 1h retention interval. Infection with P. entomophila (but not E. c. carotovora) also improved learning performance after 5 min retention. No effect on learning performance was detected for intestinal infections with an avirulent GacA mutant of P. entomophila or for virulent systemic (hemocoel) infection with E. c. carotovora. Assays of unconditioned responses to odorants and shock do not support a major role for changes in general responsiveness to stimuli in explaining the changes in learning performance, although differences in their specific salience for learning cannot be excluded. Our results demonstrate that the effects of pathogens on learning performance in insects are less predictable than suggested by previous studies, and support the notion that immune stress can sometimes boost cognitive abilities.

Reinforcement learning utilizes proxemics: an avatar learns to manipulate the position of people in immersive virtual reality

A reinforcement learning (RL) method was used to train a virtual character to move participants to a specified location. The virtual environment depicted an alleyway displayed through a wide field-of-view head-tracked stereo head-mounted display. Based on proxemics theory, we predicted that when the character approached within a personal or intimate distance to the participants, they would be inclined to move backwards out of the way. We carried out a between-groups experiment with 30 female participants, with 10 assigned arbitrarily to each of the following three groups: In the Intimate condition the character could approach within 0.38m and in the Social condition no nearer than 1.2m. In the Random condition the actions of the virtual character were chosen randomly from among the same set as in the RL method, and the virtual character could approach within 0.38m. The experiment continued in each case until the participant either reached the target or 7 minutes had elapsed. The distributions of the times taken to reach the target showed significant differences between the three groups, with 9 out of 10 in the Intimate condition reaching the target significantly faster than the 6 out of 10 who reached the target in the Social condition. Only 1 out of 10 in the Random condition reached the target. The experiment is an example of applied presence theory: we rely on the many findings that people tend to respond realistically in immersive virtual environments, and use this to get people to achieve a task of which they had been unaware. This method opens up the door for many such applications where the virtual environment adapts to the responses of the human participants with the aim of achieving particular goals.

Quality of Learners' Time and Learning Performance Beyond Quantitative Time-on-Task

Peer-reviewed

Drosophila rely on learning while foraging under semi-natural conditions

Learning is predicted to affect manifold ecological and evolutionary processes, but the extent to which animals rely on learning in nature remains poorly known, especially for short-lived non-social invertebrates. This is in particular the case for Drosophila, a favourite laboratory system to study molecular mechanisms of learning. Here we tested whether Drosophila melanogaster use learned information to choose food while free-flying in a large greenhouse emulating the natural environment. In a series of experiments flies were first given an opportunity to learn which of two food odours was associated with good versus unpalatable taste; subsequently, their preference for the two odours was assessed with olfactory traps set up in the greenhouse. Flies that had experienced palatable apple-flavoured food and unpalatable orange-flavoured food were more likely to be attracted to the odour of apple than flies with the opposite experience. This was true both when the flies first learned in the laboratory and were then released and recaptured in the greenhouse, and when the learning occurred under free-flying conditions in the greenhouse. Furthermore, flies retained the memory of their experience while exploring the greenhouse overnight in the absence of focal odours, pointing to the involvement of consolidated memory. These results support the notion that even small, short lived insects which are not central-place foragers make use of learned cues in their natural environments.

Reinforcement learning utilizes proxemics: an avatar learns to manipulate the position of people in immersive virtual reality

A reinforcement learning (RL) method was used to train a virtual character to move participants to a specified location. The virtual environment depicted an alleyway displayed through a wide field-of-view head-tracked stereo head-mounted display. Based on proxemics theory, we predicted that when the character approached within a personal or intimate distance to the participants, they would be inclined to move backwards out of the way. We carried out a between-groups experiment with 30 female participants, with 10 assigned arbitrarily to each of the following three groups: In the Intimate condition the character could approach within 0.38m and in the Social condition no nearer than 1.2m. In the Random condition the actions of the virtual character were chosen randomly from among the same set as in the RL method, and the virtual character could approach within 0.38m. The experiment continued in each case until the participant either reached the target or 7 minutes had elapsed. The distributions of the times taken to reach the target showed significant differences between the three groups, with 9 out of 10 in the Intimate condition reaching the target significantly faster than the 6 out of 10 who reached the target in the Social condition. Only 1 out of 10 in the Random condition reached the target. The experiment is an example of applied presence theory: we rely on the many findings that people tend to respond realistically in immersive virtual environments, and use this to get people to achieve a task of which they had been unaware. This method opens up the door for many such applications where the virtual environment adapts to the responses of the human participants with the aim of achieving particular goals.

Information System ProcessInnovation Adoption, Adaptation, Learning, and Unlearning: A Longitudinal Case Study

This thesis examines the history and evolution of information system process innovation (ISPI) processes (adoption, adaptation, and unlearning) within the information system development (ISD) work in an internal information system (IS) department and in two IS software house organisations in Finland over a 43-year time-period. The study offers insights into influential actors and their dependencies in deciding over ISPIs. The research usesa qualitative research approach, and the research methodology involves the description of the ISPI processes, how the actors searched for ISPIs, and how the relationships between the actors changed over time. The existing theories were evaluated using the conceptual models of the ISPI processes based on the innovationliterature in the IS area. The main focus of the study was to observe changes in the main ISPI processes over time. The main contribution of the thesis is a new theory. The term theory should be understood as 1) a new conceptual framework of the ISPI processes, 2) new ISPI concepts and categories, and the relationships between the ISPI concepts inside the ISPI processes. The study gives a comprehensive and systematic study on the history and evolution of the ISPI processes; reveals the factors that affected ISPI adoption; studies ISPI knowledge acquisition, information transfer, and adaptation mechanisms; and reveals the mechanismsaffecting ISPI unlearning; changes in the ISPI processes; and diverse actors involved in the processes. The results show that both the internal IS department and the two IS software houses sought opportunities to improve their technical skills and career paths and this created an innovative culture. When new technology generations come to the market the platform systems need to be renewed, and therefore the organisations invest in ISPIs in cycles. The extent of internal learning and experiments was higher than the external knowledge acquisition. Until the outsourcing event (1984) the decision-making was centralised and the internalIS department was very influential over ISPIs. After outsourcing, decision-making became distributed between the two IS software houses, the IS client, and itsinternal IT department. The IS client wanted to assure that information systemswould serve the business of the company and thus wanted to co-operate closely with the software organisations.