Temporal difference models describe higher-order learning in humans.

The ability to use environmental stimuli to predict impending harm is critical for survival. Such predictions should be available as early as they are reliable. In pavlovian conditioning, chains of successively earlier predictors are studied in terms of higher-order relationships, and have inspired computational theories such as temporal difference learning. However, there is at present no adequate neurobiological account of how this learning occurs. Here, in a functional magnetic resonance imaging (fMRI) study of higher-order aversive conditioning, we describe a key computational strategy that humans use to learn predictions about pain. We show that neural activity in the ventral striatum and the anterior insula displays a marked correspondence to the signals for sequential learning predicted by temporal difference models. This result reveals a flexible aversive learning process ideally suited to the changing and uncertain nature of real-world environments. Taken with existing data on reward learning, our results suggest a critical role for the ventral striatum in integrating complex appetitive and aversive predictions to coordinate behaviour.

Fast Perceptual Learning in Visual Hyperacuity

In many different spatial discrimination tasks, such as in determining the sign of the offset in a vernier stimulus, the human visual system exhibits hyperacuity-level performance by evaluating spatial relations with the precision of a fraction of a photoreceptor"s diameter. We propose that this impressive performance depends in part on a fast learning process that uses relatively few examples and occurs at an early processing stage in the visual pathway. We show that this hypothesis is plausible by demonstrating that it is possible to synthesize, from a small number of examples of a given task, a simple (HyperBF) network that attains the required performance level. We then verify with psychophysical experiments some of the key predictions of our conjecture. In particular, we show that fast timulus-specific learning indeed takes place in the human visual system and that this learning does not transfer between two slightly different hyperacuity tasks.

Novelty and Habituation: the Driving Forces in Early Stage Learning for Developmental Robotics

Meng, Q., & Lee, M. (2005). Novelty and Habituation: the Driving Forces in Early Stage Learning for Developmental Robotics. Wermter, S., Palm, G., & Elshaw, M. (Eds.), In: Biomimetic Neural Learning for Intelligent Robots: Intelligent Systems, Cognitive Robotics, and Neuroscience. (pp. 315-332). (Lecture Notes in Computer Science). Springer Berlin Heidelberg.

Novelty and Habituation: the Driving Forces in Early Stage Learning for Developmental Robotics

Q. Meng and M. H. Lee, Novelty and Habituation: the Driving Forces in Early Stage Learning for Developmental Robotics, AI-Workshop on NeuroBotics, University of Ulm, Germany. September 2004.

Construction of Robot Intra-modal and Inter-modal Coordination Skills by Developmental Learning

Q. Meng and M. H. Lee, 'Construction of Robot Intra-modal and Inter-modal Coordination Skills by Developmental Learning', Journal of Intelligent and Robotic Systems, 48(1), pp 97-114, 2007.

Total quality leadership : Employing organisational learning as a conduit

Oxtoby, B.; Morgan, R.; McGuinness, T.; and Jones, M. (2001). Total quality leadership: Employing organisational learning as a conduit. International Journal of Management. 18(2), pp.245-251 RAE2008

Robert Heinich, Michael Molenda, James D. Russell, Sharon E. Smaldino, Instructional Media and Technologies for Learning, Prentice-Hall, Inc., Englewood Cliffs, New Jersey 1996, ss. 446. Steven Hackbarth, The Educational Technology Handbook. A Comprehensive Guide, Educational Technology Publications Inc., Englewood Cliffs, New Jersey 1996, ss. 351

Recenzje i sprawozdania z książek

Neural Representations for Sensory-Motor Control, III: Learning a Body-Centered Representation of 3-D Target Position

A neural model is described of how the brain may autonomously learn a body-centered representation of 3-D target position by combining information about retinal target position, eye position, and head position in real time. Such a body-centered spatial representation enables accurate movement commands to the limbs to be generated despite changes in the spatial relationships between the eyes, head, body, and limbs through time. The model learns a vector representation--otherwise known as a parcellated distributed representation--of target vergence with respect to the two eyes, and of the horizontal and vertical spherical angles of the target with respect to a cyclopean egocenter. Such a vergence-spherical representation has been reported in the caudal midbrain and medulla of the frog, as well as in psychophysical movement studies in humans. A head-centered vergence-spherical representation of foveated target position can be generated by two stages of opponent processing that combine corollary discharges of outflow movement signals to the two eyes. Sums and differences of opponent signals define angular and vergence coordinates, respectively. The head-centered representation interacts with a binocular visual representation of non-foveated target position to learn a visuomotor representation of both foveated and non-foveated target position that is capable of commanding yoked eye movementes. This head-centered vector representation also interacts with representations of neck movement commands to learn a body-centered estimate of target position that is capable of commanding coordinated arm movements. Learning occurs during head movements made while gaze remains fixed on a foveated target. An initial estimate is stored and a VOR-mediated gating signal prevents the stored estimate from being reset during a gaze-maintaining head movement. As the head moves, new estimates arc compared with the stored estimate to compute difference vectors which act as error signals that drive the learning process, as well as control the on-line merging of multimodal information.

Space, Time and Learning in the Hippocampus: How Fine Spatial and Temporal Scales Are Expanded into Population Codes for Behavioral Control

The hippocampus participates in multiple functions, including spatial navigation, adaptive timing, and declarative (notably, episodic) memory. How does it carry out these particular functions? The present article proposes that hippocampal spatial and temporal processing are carried out by parallel circuits within entorhinal cortex, dentate gyrus, and CA3 that are variations of the same circuit design. In particular, interactions between these brain regions transform fine spatial and temporal scales into population codes that are capable of representing the much larger spatial and temporal scales that are needed to control adaptive behaviors. Previous models of adaptively timed learning propose how a spectrum of cells tuned to brief but different delays are combined and modulated by learning to create a population code for controlling goal-oriented behaviors that span hundreds of milliseconds or even seconds. Here it is proposed how projections from entorhinal grid cells can undergo a similar learning process to create hippocampal place cells that can cover a space of many meters that are needed to control navigational behaviors. The suggested homology between spatial and temporal processing may clarify how spatial and temporal information may be integrated into an episodic memory.

Use of Web Logs (Blogs) in Learning Environments: Blogs for Student Support

Innovation in technology and communications and particularly the advent of the Web is changing the structure of teaching and learning today. While there is much debate about the use of technology in learning and how e-learning is creating new approaches to delivery of learning there is been very little if any work on the use of of the emerging technologies in providing student support through their learning process. This paper reports on research and development undertaken by the eCentre based at the University of Greenwich School Of Computing in designing and developing a "Project Blog System" in order to address some long standing issues related to supervision of final year degree student projects. The paper will report on the methodology used to design the system and will discuss some of the results from students and staff evaluation of the system developed.

Lifelong learning through digital storytelling in corporate training

In this paper we discuss collaborative learning strategies based on the use of digital stories in corporate training and lifelong learning. The text starts with a concise review on theoretical and technical foundations about the use of digital technologies in collaborative strategies in lifelong learning. We will also discuss if the corporate training may be improved by the use of individual audio-visual experience in learning process. Careful planning, scripting and production of audio-visual digital stories can help in the construction of collaborative learning spaces in which adults are in the context of vocational training throughout life. Our analysis concludes emphasizing on the need to experience the routing performance of digital stories in the context of corporate training, following the reference levels mentioned here, so we can have in a future more theoretical and empirical elements for the validation and conceptualization in the use of digital stories in the context of corporate training. Ultimately we believe that lifelong learning can be improved with the use of strategies that promote the production of personal audio-visual for those involved in teaching and learning process in organizational context.

Blogs as virtual environments for teaching and learning in Higher Education

The transformation of the Information and Communication Technologies (ICT) into knowledge and learning technologies is increasingly becoming a matter of concern. Teaching settings associated with the use of blogs in Higher Education are presented in this paper, proceeding from an innovative learning experience of projects carried out by a group of professors between 2009 and 2013. Both, teachers and students who took part in the subjects that implemented the blog, considered it as helpful resource to create a virtual and learning-teaching environment due to the multiple potentialities it offers. Among some of these potentialities, some stand out: it makes easier the access to knowledge, promotes a more active and reflective learning, expands the social experience of learning, provides evidence about the students’ progress which helps to reorient the teaching-learning process, and encourages the critical judgment. Nevertheless, several problems related with the students’ participation and the teacher’s blog management have been identified.

Evaluating the Effectiveness of Social Work Education: Preparing Students for Practice Learning

Evaluating the effectiveness of social work education has become a topic of major interest in the UK in the wake of a succession of child-care tragedies that have undermined confidence in the profession. However, many key aspects of social work education remain under-researched and/or contested and our knowledge of how students acquire and develop professional expertise remains limited. This paper reports on the first part of a longitudinal study aimed at developing evidence-based knowledge in this area by considering student perceptions at different stages of their social work education at Queen’s University Belfast. Focusing on the strengths and limitations of preparatory teaching, and their first experience of practice learning, this article considers the impact of demographic factors, including age, gender and experience, on how students experience the learning process. The findings indicate a significant level of disjunction between academic and practice learning and suggest that better integration between these two domains of learning is needed if social work students are to be more effectively prepared for the challenges they are likely to encounter in practice.

Dual-processes in learning and judgment:Evidence from the multiple cue probability learning paradigm

Multiple cue probability learning (MCPL) involves learning to predict a criterion based on a set of novel cues when feedback is provided in response to each judgment made. But to what extent does MCPL require controlled attention and explicit hypothesis testing? The results of two experiments show that this depends on cue polarity. Learning about cues that predict positively is aided by automatic cognitive processes, whereas learning about cues that predict negatively is especially demanding on controlled attention and hypothesis testing processes. In the studies reported here, negative, but not positive cue learning related to individual differences in working memory capacity both on measures of overall judgment performance and modelling of the implicit learning process. However, the introduction of a novel method to monitor participants' explicit beliefs about a set of cues on a trial-by-trial basis revealed that participants were engaged in explicit hypothesis testing about positive and negative cues, and explicit beliefs about both types of cues were linked to working memory capacity. Taken together, our results indicate that while people are engaged in explicit hypothesis testing during cue learning, explicit beliefs are applied to judgment only when cues are negative. © 2012 Elsevier Inc.

The role of working memory capacity in multiple-cue probability learning

Multiple-cue probability learning (MCPL) involves learning to predict a criterion when outcome feedback is provided for multiple cues. A great deal of research suggests that working memory capacity (WMC) is involved in a wide range of tasks that draw on higher level cognitive processes. In three experiments, we examined the role of WMC in MCPL by introducing measures of working memory capacity, as well as other task manipulations. While individual differences in WMC positively predicted performance in some kinds of multiple-cue tasks, performance on other tasks was entirely unrelated to these differences. Performance on tasks that contained negative cues was correlated with working memory capacity, as well as measures of explicit knowledge obtained in the learning process. When the relevant cues predicted positively, however, WMC became irrelevant. The results are discussed in terms of controlled and automatic processes in learning and judgement. © 2011 The Experimental Psychology Society.