This grounded study investigated the negotiation of authorship by faculty members, graduate student mentors, and their undergraduate protégés in undergraduate research experiences at a private research university in the northeastern United States. Semi-structured interviews using complementary scripts were conducted separately with 42 participants over a 3 year period to probe their knowledge and understanding of responsible authorship and publication practices and learn how faculty and students entered into authorship decision-making intended to lead to the publication of peer-reviewed technical papers. Herein (...) the theoretical model for the negotiation of authorship developed through the analysis of these interviews is reported. The model identifies critical causal and intervening conditions responsible for the coping strategies faculty and students employ, which, in our study, appear to often produce unfortunate consequences for all involved. The undergraduate student researchers and their graduate student mentors interviewed in this study exhibited a limited understanding of authorship and the requirements for authorship in their research groups. The power differential between faculty and students, the students’ limited epistemic development, the busy-ness of the faculty, and the faculty’s failure to prioritize authorship have been identified as key factors inhibiting both undergraduate and graduate students from developing a deeper understanding of responsible authorship and publication practices. Implications for graduate education and undergraduate research are discussed, and strategies for helping all students to develop a deeper understanding of authorship are identified. (shrink)

Abstract:Artificial intelligence is profoundly transforming the world in various spheres and already finding its way into educational institutions. This essay aims to examine whether the Deweyan ideal of education can be achieved through such digital means. By analyzing how both the aims and means of education, as defined by Dewey, can be understood in the context of learning with artificial intelligence, the inherent differences of both educational approaches are brought out. It becomes apparent that important concepts that characterize successful (...) education for Dewey, such as intrinsic inquiry, experience-based schooling, and thinking as the method for learning, do not go hand in hand with intelligent teaching systems. (shrink)

Objective: Mindfulness-based intervention has been proposed to alleviate loneliness and improve social connectedness. Several randomized controlled trials have been conducted to evaluate the effectiveness of MBI. This study aimed to critically evaluate and determine the effectiveness and safety of MBI in alleviating the feeling of loneliness.Methods: We searched Medline, Embase, PsycInfo, Cochrane CENTRAL, and AMED for publications from inception to May 2020. We included RCTs with human subjects who were enrolled in MBI with loneliness as an outcome. The quality of (...) evidence was assessed using Cochrane's Risk of Bias tool and Grading of Recommendations Assessment, Development, and Evaluation. A random-effects model was used for meta-analysis.Results: Out of 92 articles identified, eight studies involving 815 participants were included in this study. Most trials conducted a minimum of 8 weeks of MBI. Most of the trials used UCLA-Loneliness Scale. A pooled analysis combining three trials and compared with wait-list showed significant improvement in loneliness score reduction using the UCLA-R scale with MD of −6.33 [95% confidence interval : −9.39, −3.26]. Subgroup analysis with only two Cognitively-Based Compassion Training trials also showed similar MD of −6.05. The overall quality of evidence was low.Conclusions: Mindfulness intervention with an average length of 8-week duration significantly improved the population's loneliness level with no mental health issue. However, this evidence had a low GRADE level. (shrink)

This study aimed to identify the learning and business incubation processes and their impact on developing the performance of business incubators in Gaza Strip, and the study relied on the descriptive analytical approach, and the study population consisted of all employees working in business incubators in Gaza Strip in addition to experts and consultants in incubators where their total number reached (62) individuals, and the researchers used the questionnaire as a main tool to collect data through the comprehensive survey (...) method, where (55) questionnaires were retrieved with a recovery rate of (88.7%). The results of the study showed that there is a high approval of the variables of the study, where the dimension of incubation operations came with a relative weight (84.89%), followed by the learning dimension which got a relative weight (82.50%), while the performance level of the incubators got a relative weight (80.12%) and as the study results showed There is a positive relationship statistically between the two dimensions of learning and business incubation processes and performance development in business incubators, and the linear regression analysis model showed that the main effect in improving the performance of business incubators is "after business incubation operations" while it showed weak effect after learning. The study came out with several recommendations, the most important of which is that business incubators seek to reinforce the learning process and incubation processes for business, and the need to periodically evaluate their performance, and that a more effective mechanism be designed to follow up with companies after the end of the incubation period and monitor the progress of these companies. (shrink)

Just as humans can draw conclusions responsibly or irresponsibly, so too can computers. Machine learning systems that have been trained on data sets that include irresponsible judgments are likely to yield irresponsible predictions as outputs. In this paper I focus on a particular kind of inference a computer system might make: identification of the intentions with which a person acted on the basis of photographic evidence. Such inferences are liable to be morally objectionable, because of a way in which (...) they are presumptuous. After elaborating this moral concern, I explore the possibility that carefully procuring the training data for image recognition systems could ensure that the systems avoid the problem. The lesson of this paper extends beyond just the particular case of image recognition systems and the challenge of responsibly identifying a person’s intentions. Reflection on this particular case demonstrates the importance (as well as the difficulty) of evaluating machine learning systems and their training data from the standpoint of moral considerations that are not encompassed by ordinary assessments of predictive accuracy. (shrink)

We consider the following signaling game. Nature plays first from the set {1, 2}. Player 1 (the Sender) sees this and plays from the set {A, B}. Player 2 (the Receiver) sees only Player 1’s play and plays from the set {1, 2}. Both players win if Player 2’s play equals Nature’s play and lose otherwise. Players are told whether they have won or lost, and the game is repeated. An urn scheme for learning coordination in this game is (...) as follows. Each node of the desicion tree for Players 1 and 2 contains an urn with balls of two colors for the two possible decisions. Players make decisions by drawing from the appropriate urns. After a win, each ball that was drawn is reinforced by adding another of the same color to the urn. A number of equilibria are possible for this game other than the optimal ones. However, we show that the urn scheme achieves asymptotically optimal coordination. (shrink)

Over the past decade, teaching and learning in virtual worlds has been at the forefront of many higher education institutions around the world. The DEHub Virtual Worlds Working Group (VWWG) consisting of Australian and New Zealand higher education academics was formed in 2009. These educators are investigating the role that virtual worlds play in the future of education and actively changing the direction of their own teaching practice and curricula. 47 academics reporting on 28 Australian higher education institutions present (...) an overview of how they have changed directions through the effective use of virtual worlds for diverse teaching and learning activities such as business scenarios and virtual excursions, role-play simulations, experimentation and language development. The case studies offer insights into the ways in which institutions are continuing to change directions in their teaching to meet changing demands for innovative teaching, learning and research in virtual worlds. This paper highlights the ways in which the authors are using virtual worlds to create opportunities for rich, immersive and authentic activities that would be difficult or not possible to achieve through more traditional approaches. (shrink)

The target paper of Schoeller, Perlovsky, and Arseniev is an essential and timely contribution to a current shift of focus in neuroscience aiming to merge neurophysiological, psychological and physical principles in order to build the foundation for the physics of mind. Extending on previous work of Perlovsky et al. and Badre, the authors of the target paper present interesting mathematical models of several basic principles of the physics of mind, such as perception and cognition, concepts and emotions, instincts and (...) class='Hi'>learning. Their conceptualization helps to clarify the distinction between conscious and unconscious aspects of mind that is often neglected and further provide a clear description of the mental hierarchy, which extends from physical objects in the physical world to abstract ideas in the mental/subjective realm. While we agree that identification of a few fundamental principles is a first step toward developing the physics of the mind, and we concur with the selection of those principles in the target review paper, we think that the theory of the physics of mind would much benefit from considering also the most basic principles that are common for the physics/matter/brain and the mind/subjectivity/cognition. In this respect, such basic principles as time and space, as well as criticality, self-organization, and emergence seem to be the most interesting. (shrink)

We live in the self-proclaimed time of difference, when particular identities and localities worry about or actively resist the global forces of modernization. This is the time of the other, the exception, the multicultural. Why, then, look again at Hegel, who is reputed to be the philosopher of unity, sameness, and absorption into the whole? Things may not be what they seem. Hegel may be surprisingly relevant; in a world in which particularity is alternately triumphant and resentful, Hegel offers more (...) sophisticated ways to think about individual and social unity. Agree with him or not, you can learn from him. (shrink)

The article summarizes the theoretical and methodological knowledge about ecotourism as one of the viable types of tourism in the postmodern era, clarifies the patterns of its formation and development, a variety of approaches to its interpretation, interaction with other types of tourism, features of motivation and management in ecotourism. On the basis of the generalized data a number of perspective educational conditions is modeled: the orientation of the maintenance of pedagogical education on formation of steady positive motivation; updating the (...) content of professional training; availability of educational and methodical support; interrelation of competence, system and personal-activity approaches. Diagnostic methods relevant for postmodern education are selected: methods of diagnosing students' learning motivation; methods of diagnosis of value orientations in career; tests to determine the levels of formation of the components of professional competence. It is proved that the professional training of teachers who are able to use ecological tourism in their professional activities at a high level is one of the important conditions for increasing the interest of children and young people in physical education, tourism and sports, ensuring the necessary physical activity, developing, maintaining and strengthening their health, adaptation to the flexible postmodern society. (shrink)

Frank Mitjans is an architect who has worked in London since 1976. He was introduced to the significance of the figure of St. Thomas More by Andrés Vázquez de Prada, author of the biography, Sir Tomás Moro, Lord Canciller de Inglaterra. In 1977 Vázquez de Prada invited Mitjans to visit with him the Thomas More Exhibition at the National Portrait Gallery, which stimulated his interest in representations of More, his family and his friends. Since August 2002 he has given many (...) presentations and talks on the topography of More’s London to groups of students and other interested people in Britain, Ireland, and Sweden. Frank Mitjans, architecte qui travaille à Londres depuis 1976, fut initié au personnage de Saint Thomas More par Andrés Vázquez de Prada, auteur de la biographie Sir Tomás Moro, Lord Canciller de Inglaterra. En 1977 Vázquez de Prada l’invita à l’accompagner dans sa visite de l’exposition Thomas More à la National Portrait Gallery, évènement qui aiguisa son intérêt pour les représentations de More, de sa famille et de ses amis. Depuis août 2002 il a régulièrement fait des présentations et des exposés à des groupes d’étudiants et d’autres en Grande-Bretagne, Irlande et Suède au sujet de la topographie du Londres de Thomas More. The recent exhibitions of the works of Holbein in London and Basel, and scholarly publications such as Holbein and England, have rekindled interest in the Portrait of Thomas More and his Family, and in the late sixteenth-century versions of Holbein’s presumed lost original. The present paper analyses the differences between the sixteenth-century versions and Holbein’s 1527 composition sketch, and concludes that the sketch is a more reliable witness to Holbein’s lost painting than the later versions, as well as the only authentic witness to More’s intentions; though anything we learn from the drawing must be checked against what we know from More’s letters and writings and other biographical data. From the later versions, however, and in particular from the references to Seneca on them, we can learn about those who commissioned them and England’s peculiar historical circumstances. (shrink)

In this new volume in the Oxford Psychology Series, the author presents a highly readable account of the cognitive unconscious, focusing in particular on the problem of implicit learning. Implicit learning is defined as the acquisition of knowledge that takes place independently of the conscious attempts to learn and largely in the absence of explicit knowledge about what was acquired. One of the core assumptions of this argument is that implicit learning is a fundamental, "root" process, one (...) that lies at the very heart of the adaptive behavioral repertoire of every complex organism. The author's goals are to outline the essential features of implicit learning that have emerged from the many studies that have been carried out in a variety of experimental laboratories over the past several decades; to present the various alternative perspectives on this issue that have been proposed by other researchers and to try to accommodate these views with his own; to structure the literature so that it can be seen in the context of standard heuristics of evolutionary biology; to present the material within a functionalist approach and to try to show why the experimental data should be seen as entailing particular epistemological perspectives; and to present implicit processing as encompassing a general and ubiquitous set of operations that have wide currency and several possible applications. Chapter 1 begins with the core problem under consideration in this book, a characterization of "implicit learning" as it has come to be used in the literature. Reber puts this seemingly specialized topic into a general framework and suggests a theoretical model based on standard heuristics of evolutionary biology. In his account, Reber weaves a capsule history of interest in and work on the cognitive unconscious. Chapter 2 turns to a detailed overview of the experimental work on the acquisition of implicit knowledge, which currently is of great interest. Chapter 3 develops the evolutionary model within which one can see learning and cognition as richly intertwining issues and not as two distinct fields with one dominating the other. Finally, Chapter 4 explores a variety of entailments and speculations concerning implicit cognitive processes and their general role in the larger scope of human performance. (shrink)

I examine the phenomenon of implicit learning, the process by which knowledge about the rule-governed complexities of the stimulus environment is acquired independently of conscious attempts to do so. Our research with the two seemingly disparate experimental paradigms of synthetic grammar learning and probability learning, is reviewed and integrated with other approaches to the general problem of unconscious cognition. The conclusions reached are as follows: Implicit learning produces a tacit knowledge base that is abstract and representative (...) of the structure of the environment; such knowledge is optimally acquired independently of conscious efforts to learn; and it can be used implicitly to solve problems and make accurate decisions about novel stimulus circumstances. Various epistemological issues and related problems such as intuition, neuroclinical disorders of learning and memory, and the relationship of evolutionary processes to cognitive science are also discussed. (shrink)

The scientific study of consciousness or subjective experiencing is a rapidly expanding research program engaging philosophers of mind, psychologists, cognitive scientists, neurobiologists, evolutionary biologists and biosemioticians. Here we outline an evolutionary approach that we have developed over the last two decades, focusing on the evolutionary transition from non-conscious to minimally conscious, subjectively experiencing organisms. We propose that the evolution of subjective experiencing was driven by the evolution of learning and we identify an open-ended, representational, generative and recursive form of (...) associative learning, which we call Unlimited Associative Learning (UAL), as an evolutionary transition marker of minimal consciousness. This evolutionary marker provides evidence that the evolutionary transition to consciousness has gone to completion and allows reverse-engineering from this learning capacity to the system that enables it – making possible the construction of a toy model of UAL. The model allows us to identify some of the key processes and structures that constitute minimal consciousness, points its taxonomic distribution and the ecological context in which it first emerged, highlights its function and suggests a framework for exploring developmental and evolutionary modifications of consciousness. We point to ways of experimentally testing the relationship between UAL and consciousness in human and in non-human animals and discuss the theoretical and ethical implications of our approach. The framework we offer allows the exploration of the evolutionary changes in agency, value systems, selective processes and goals that were involved in the transition to subjective experiencing from a perspective that resonates with the approaches of bio-semioticians. (shrink)

Deep learning algorithms are rapidly changing the way in which audiovisual media can be produced. Synthetic audiovisual media generated with deep learning—often subsumed colloquially under the label “deepfakes”—have a number of impressive characteristics; they are increasingly trivial to produce, and can be indistinguishable from real sounds and images recorded with a sensor. Much attention has been dedicated to ethical concerns raised by this technological development. Here, I focus instead on a set of issues related to the notion of (...) synthetic audiovisual media, its place within a broader taxonomy of audiovisual media, and how deep learning techniques differ from more traditional approaches to media synthesis. After reviewing important etiological features of deep learning pipelines for media manipulation and generation, I argue that “deepfakes” and related synthetic media produced with such pipelines do not merely offer incremental improvements over previous methods, but challenge traditional taxonomical distinctions, and pave the way for genuinely novel kinds of audiovisual media. (shrink)

Implicit Learning and Consciousness challenges conventional wisdom and presents the most up-to-date studies to define, quantify and test the predictions of the main models of implicit learning. The chapters include a variety of research from computer modeling, experimental psychology and neural imaging to the clinical data resulting from work with amnesics. The result is a topical book that provides an overview of the debate on implicit learning, and the various philosophical, psychological and neurological frameworks in which it (...) can be placed. It will be of interest to undergraduates, postgraduates and the philosophical, psychological and modeling research community. (shrink)

In recent years, the family of algorithms collected under the term ``deep learning'' has revolutionized artificial intelligence, enabling machines to reach human-like performances in many complex cognitive tasks. Although deep learning models are grounded in the connectionist paradigm, their recent advances were basically developed with engineering goals in mind. Despite of their applied focus, deep learning models eventually seem fruitful for cognitive purposes. This can be thought as a kind of biological exaptation, where a physiological structure becomes (...) applicable for a function different from that for which it was selected. In this paper, it will be argued that it is time for cognitive science to seriously come to terms with deep learning, and we try to spell out the reasons why this is the case. First, the path of the evolution of deep learning from the connectionist project is traced, demonstrating the remarkable continuity, and the differences as well. Then, it will be considered how deep learning models can be useful for many cognitive topics, especially those where it has achieved performance comparable to humans, from perception to language. It will be maintained that deep learning poses questions that cognitive sciences should try to answer. One of such questions is the reasons why deep convolutional models that are disembodied, inactive, unaware of context, and static, are by far the closest to the patterns of activation in the brain visual system. (shrink)

Machine learning and essentialism have been connected in the past by various researchers, in order to state that the main paradigm in machine learning processes is equivalent to choosing the “essential” attributes for the machine to search for. Our goal in this paper is to show that there are connections between machine learning and essentialism, but only for some kinds of machine learning, and often not including deep learning methods. Similarity-based approaches, more connected to the (...) overall prototype theory, spanning from psychology and linguistics, seem more suited for pattern recognition and complex deep-learning issues, while for classification problems, mostly for unsupervised learning, essentialism seems like the best choice. In order to illustrate the difference better, we will connect both paths to their sources in other disciplines and see how human psychology influences our decision in machine-learning modeling as well. This leads to a philosophically very interesting consequence: even in the setting of supervised machine learning, essences are not present in data, but in targets, which in turn means that the categories which purport to be essences are in fact human-made, and hand-coded in the targets. The success of machine learning, therefore, does not give any substantial evidence for the independent existence of essential properties. Our stance here is to state that neither the existence nor the lack of “essential” properties in machine learning can lead to metaphysical, i.e., ontological claims. (shrink)

In this chapter, I consider three ‘promises’ of bringing phenomenology into dialogue with networked learning. First, a ‘conceptual promise’, which draws attention to conceptual resources in phenomenology that can inspire and inform how we understand, conceive of, and uncover experiences of participants in networked learning activities and environments. Second, a ‘methodological promise’, which outlines a variety of ways that phenomenological methodologies and concepts can be put to use in empirical research in networked learning. And third, a ‘critical (...) promise’, which suggests how work done in the realm of critical phenomenology is complementary to and useful for exploring the social justice and emancipatory aims of networked learning researchers. Through this framework, I reflect upon multiple ways in which phenomenology has and might continue to fruitfully inform and shape networked learning research. In doing so, I emphasise that deploying phenomenology in the context of networked learning not only enriches our understanding of networked learning but that phenomenology itself is enriched through testing, finetuning, and expanding its methods, concepts, and understanding, as well as unveiling its own limitations and constraints. (shrink)

Accuracy-first accounts of rational learning attempt to vindicate the intuitive idea that, while rationally-formed belief need not be true, it is nevertheless likely to be true. To this end, they attempt to show that the Bayesian's rational learning norms are a consequence of the rational pursuit of accuracy. Existing accounts fall short of this goal, for they presuppose evidential norms which are not and cannot be vindicated in terms of the single-minded pursuit of accuracy. I propose an alternative (...) account, according to which learning experiences rationalize changes in the way you value accuracy, which in turn rationalize changes in belief. I show that this account is capable of vindicating the Bayesian's rational learning norms in terms of the single-minded pursuit of accuracy, so long as accuracy is rationally valued. (shrink)

While the study of implicit learning is nothing new, the field as a whole has come to embody — over the last decade or so — ongoing questioning about three of the most fundamental debates in the cognitive sciences: The nature of consciousness, the nature of mental representation (in particular the difficult issue of abstraction), and the role of experience in shaping the cognitive system. Our main goal in this chapter is to offer a framework that attempts to integrate (...) current thinking about these three issues in a way that specifically links consciousness with adaptation and learning. Our assumptions about this relationship are rooted in further assumptions about the nature of processing and of representation in cognitive systems. When considered together, we believe that these assumptions offer a new perspective on the relationships between conscious and unconscious processing and on the function of consciousness in cognitive systems. (shrink)

The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the success of this approach has transformed the engineering methods of machine learning in artificial intelligence, the significance of these achievements for the modelling of human learning and representation remains unclear. Deep Learning and Linguistic Representation (...) looks at the application of a variety of deep learning systems to several cognitively interesting NLP tasks. It also considers the extent to which this work illuminates our understanding of the way in which humans acquire and represent linguistic knowledge. Key Features: combines an introduction to deep learning in AI and NLP with current research on Deep Neural Networks in computational linguistics. is self-contained and suitable for teaching in computer science, AI, and cognitive science courses; it does not assume extensive technical training in these areas. provides a compact guide to work on state of the art systems that are producing a revolution across a range of difficult natural language tasks. (shrink)

As a form of experiential education, service learning shows great potential for promoting students' knowledge transfer as it offers students opportunities to apply what they have learned in classrooms to serve communities in real-life contexts. To explore how students' knowledge transfer evolves during SL, we collected longitudinal survey data from 96 Chinese college students in a 9-week SL program. Results indicate that students' perceived knowledge transfer in SL did not follow a linear trajectory. Although students' perceived knowledge transfer at (...) the end of SL was significantly higher than those at the beginning, a slight drop was observed in the middle of SL; the developmental pattern of perceived knowledge transfer varied across students; and students' perceived knowledge transfer development during SL was associated with mastery goal orientation and perceptions of psychologically controlling behaviors from their SL supervisors. By providing evidence of the dynamic process and mechanisms of students' knowledge transfer development, the present study adds to our understanding of how, when, and why the benefits of SL are realized. (shrink)

In this concise book based on his Arne Ryde Lectures in 2002, Young suggests a conceptual framework for studying strategic learning and highlights theoretical developments in the area. He discusses the interactive learning problem; reinforcement and regret; equilibrium; conditional no-regret learning; prediction, postdiction, and calibration; fictitious play and its variants; Bayesian learning; and hypothesis testing. Young's framework emphasizes the amount of information required to implement different types of learning rules, criteria for evaluating their performance, and (...) alternative notions of equilibrium to which they converge. He also stresses the limits of what can be achieved: for a given type of game and a given amount of information, there may exist no learning procedure that satisfies certain reasonable criteria of performance and convergence. In short, Young has provided a valuable primer that delineates what we know, what we would like to know, and the limits of what we can know, when we try to learn about a system that is composed of other learners. (shrink)

Birch, Ginsburg, and Jablonka suggest that Unlimited Associative Learning is a “transition marker” in the evolutionary process that produced consciousness, and organizes research by tying together a range of “hallmarks” of consciousness. I argue that the features they recognize as “hallmarks” are indeed important in the evolution of consciousness, but UAL may have a more limited role.

This book presents the psychological basis, methodology, and application of Marton's phenomenographic approach to the theory of learning.

For Sellars, the possibility of empirical knowledge presupposes the existence of "sense impressions" in the perceiver, i.e., non-conceptual states of perceptual consciousness. But this role for sense impressions does not implicate Sellars' account in the Myth of the Given: sense impressions do not stand in a justificatory relation to instances of perceptual knowledge; their existence is rather a condition for the possibility of the acquisition of empirical concepts. Sellars suggests that learning empirical concepts presupposes that we can remember certain (...) past facts that we could not conceptualize at the time they obtained. And such memory presupposes, in turn, the existence of certain (past) non-conceptual sensory states that can be conceptualized. (shrink)

There is increasing evidence that some behavioural differences between groups of chimpanzees can be attributed neither to genetic nor to ecological variation. Such differences are likely to be maintained by social learning. While humans teach their offspring, and acquire cultural traits through imitative learning, there is little evidence of such behaviours in chimpanzees. However, by appealing only to incremental changes in motivation, attention and attention-soliciting behaviour, and without expensive changes in cognition, we can hypothesise the possible emergence of (...) imitation and pedagogy in evolutionary history. (shrink)

Perceptual experiences are not immediately responsive to reasons. You see a stick submerged in a glass of water as bent no matter how much you know about light refraction. Due to this isolation from reasons, perception is traditionally considered outside the scope of epistemic evaluability as justified or unjustified. Is perception really as independent from reasons as visual illusions make it out to be? I argue no, drawing on psychological evidence from perceptual learning. The flexibility of perceptual learning (...) is a way of responding to new epistemic reasons. The resulting perceptual experiences are epistemically evaluable as justified or unjustified. (shrink)

The use of context can considerably facilitate reasoning by restricting the beliefs reasoned upon to those relevant and providing extra information specific to the context. Despite the use and formalization of context being extensively studied both in AI and ML, context has not been much utilized in agents. This may be because many agents are only applied in a single context, and so these aspects are implicit in their design, or it may be that the need to explicitly encode information (...) about various contexts is onerous. An algorithm to learn the appropriate context along with knowledge relevant to that context gets around these difficulties and opens the way for the exploitation of context in agent design. The algorithm is described and the agents compared with agents that learn and apply knowledge in a generic way within an artificial stock market. The potential for context as a principled manner of closely integrating crisp reasoning and fuzzy learning is discussed. (shrink)

John Field’s Lifelong Learning and the New Educational Order (2000) represents a substantial contribution to the literature on lifelong learning. Whilst Field brings a wealth of policy-related and sociological learning to his work, this article focuses on a number of philosophical questions arising from the study. It is suggested that Field’s argument raises familiar questions about notions of ‘learning’, ‘reflexivity’, ‘personal autonomy’ and the conditions for knowledge. In each case, a number of considerations present themselves which (...) have implications for our understanding of lifelong learning: its nature, prevalence and significance. (shrink)