Absence of snapshot memory of the target view interferes with place navigation learning by rats in the water maze

Contribution of visual and nonvisual mechanisms to spatial behavior of rats in the Morris water maze was studied with a computerized infrared tracking system, which switched off the room lights when the subject entered the inner circular area of the pool with an escape platform. Naive rats trained under light-dark conditions (L-D) found the escape platform more slowly than rats trained in permanent light (L). After group members were swapped, the L-pretrained rats found under L-D conditions the same target faster and eventually approached latencies attained during L navigation. Performance of L-D-trained rats deteriorated in permanent darkness (D) but improved with continued D training. Thus L-D navigation improves gradually by procedural learning (extrapolation of the start-target azimuth into the zero-visibility zone) but remains impaired by lack of immediate visual feedback rather than by absence of the snapshot memory of the target view.

Spatial learning by rats across visually disconnected environments

Two spatial tasks were designed to test specific properties of spatial representation in rats. In the first task, rats were trained to locate an escape hole at a fixed position in a visually homogeneous arena. This arena was connected with a periphery where a full view of the room environment existed. Therefore, rats were dependent on their memory trace of the previous position in the periphery to discriminate a position within the central region. Under these experimental conditions, the test animals showed a significant discrimination of the training position without a specific local view. In the second task, rats were trained in a radial maze consisting of tunnels that were transparent at their distal ends only. Because the central part of the maze was non-transparent, rats had to plan and execute appropriate trajectories without specific visual feedback from the environment. This situation was intended to encourage the reliance on prospective memory of the non-visited arms in selecting the following move. Our results show that acquisition performance was only slightly decreased compared to that shown in a completely transparent maze and considerably higher than in a translucent maze or in darkness. These two series of experiments indicate (1) that rats can learn about the relative position of different places with no common visual panorama, and (2) that they are able to plan and execute a sequence of visits to several places without direct visual feed-back about their relative position.

Online Learning and Teaching in Higher Education, a book review

Peer-reviewed

Reinforcement Learning by Policy Search

One objective of artificial intelligence is to model the behavior of an intelligent agent interacting with its environment. The environment's transformations can be modeled as a Markov chain, whose state is partially observable to the agent and affected by its actions; such processes are known as partially observable Markov decision processes (POMDPs). While the environment's dynamics are assumed to obey certain rules, the agent does not know them and must learn. In this dissertation we focus on the agent's adaptation as captured by the reinforcement learning framework. This means learning a policy---a mapping of observations into actions---based on feedback from the environment. The learning can be viewed as browsing a set of policies while evaluating them by trial through interaction with the environment. The set of policies is constrained by the architecture of the agent's controller. POMDPs require a controller to have a memory. We investigate controllers with memory, including controllers with external memory, finite state controllers and distributed controllers for multi-agent systems. For these various controllers we work out the details of the algorithms which learn by ascending the gradient of expected cumulative reinforcement. Building on statistical learning theory and experiment design theory, a policy evaluation algorithm is developed for the case of experience re-use. We address the question of sufficient experience for uniform convergence of policy evaluation and obtain sample complexity bounds for various estimators. Finally, we demonstrate the performance of the proposed algorithms on several domains, the most complex of which is simulated adaptive packet routing in a telecommunication network.

Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks

Sigmoid type belief networks, a class of probabilistic neural networks, provide a natural framework for compactly representing probabilistic information in a variety of unsupervised and supervised learning problems. Often the parameters used in these networks need to be learned from examples. Unfortunately, estimating the parameters via exact probabilistic calculations (i.e, the EM-algorithm) is intractable even for networks with fairly small numbers of hidden units. We propose to avoid the infeasibility of the E step by bounding likelihoods instead of computing them exactly. We introduce extended and complementary representations for these networks and show that the estimation of the network parameters can be made fast (reduced to quadratic optimization) by performing the estimation in either of the alternative domains. The complementary networks can be used for continuous density estimation as well.

EdShare: collaboration in a University for learning and teaching

Poster for the School of Electronics and Computer Science, Learning Societies Lab Open Day, 27 February 2008 at the University of Southampton. Profile and presentation of the EdShare resource. The poster illustrates the philosophy of EdShare, how it relates to the Web 2.0 environment and its relationship to the education agenda in a University.

EdShare: collaboration in a University for learning and teaching

Poster for the Learning Societies Laboratory, School of Electronics and Computer Science, University of Southampton Open Day, Wednesday 27 February 2008.

EdShare: collaboration in a University for learning and teaching

Poster for the Learning Societies Laboratory, School of Electronics and Computer Science, University of Southampton Open Day, Wednesday 27 February 2008.

Learning and teaching with maps.

El rápido crecimiento de mapas en internet, atlas digitales y Sistemas de Información Geográfica (SIG) exige nuevas habilidades, junto con las tradicionales como la localización de lugares utilizando un mapa. Este recurso explica a los niños y a los jóvenes cómo leer, comprender e interpretar los mapas y a los profesores la manera más eficaz de enseñar con los mapas. Presta especial atención a la forma de aprendizaje con este material que puede contribuir al desarrollo cognitivo y a desarrollar habilidades en aritmética. Describe cómo los profesores pueden planificar un programa de estudios y sugiere actividades para los alumnos desde la escuela primaria a la secundaria. Incluye todos los aspectos del uso de mapas, que abarca todas las modalidades, incluidas los globos y atlas. El texto está ampliamente ilustrado con ejemplos, incluyendo los mapas realizados por los propios niños con materiales convencionales, así como programas informáticos. Una característica particular de este recurso es la integración de mapas digitales y convencionales, de internet adecuados a las necesidades de educación primaria y secundaria. Tiene apéndice con web de organizaciones y recursos citados en el texto, bibliografía e índice alfabético.

The digital classroom : harnessing technology for the future of learning and teaching.

Basado en el Proyecto Interactivo de investigación, este libro examina e ilustra cómo las tecnologías digitales pueden transformar el aprendizaje a través del currículo. Al observar los distintos entornos educativos en los que se mueven los niños y jóvenes-la casa y la escuela en las etapas primaria y secundaria-los profesionales piensan en que las nuevas tecnologías (TIC) suponen una ayuda para planificar y apoyar un aprendizaje eficaz en las clases. También, se ofrece a los lectores, a través de ejercicios prácticos y temas para la reflexión, la oportunidad de examinar su propia práctica docente y su experiencia en relación con estas tecnologías y entender sus beneficios y limitaciones.

Learning and teaching in primary schools.

Esta guía ayuda a los profesores en período de formación inicial a superar el Qualified Teacher Status (QTS). Trata temas fundamentales para el aprendizaje y la enseñanza en las escuelas primarias de hoy y en la formación del docente, tales como el comportamiento, la comunicación y la creatividad.

Science learning, science teaching.

Esta publicación ofrece a los maestros una guía completa y crítica en la enseñanza y el aprendizaje de la ciencia. Combina una visión general de la investigación actual con los cambios del plan de estudios para proporcionar una guía práctica de la enseñanza en el aula. Da consejos útiles e ideas para explorar más sobre los problemas actuales en la enseñanza de la ciencia. Incluye planificación de la enseñanza, establece objetivos de evaluación, el uso de las TIC. Cada capítulo ofrece referencias, bibliografía y sitios Web.