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)

When making decisions, humans can observe many kinds of information about others' activities, but their effects on performance are not well understood. We investigated social learning strategies using a simple problem-solving task in which participants search a complex space, and each can view and imitate others' solutions. Results showed that participants combined multiple sources of information to guide learning, including payoffs of peers' solutions, popularity of solution elements among peers, similarity of peers' solutions to their own, and relative payoffs from (...) individual exploration. Furthermore, performance was positively associated with imitation rates at both the individual and group levels. When peers' payoffs were hidden, popularity and similarity biases reversed, participants searched more broadly and randomly, and both quality and equity of exploration suffered. We conclude that when peers' solutions can be effectively compared, imitation does not simply permit scrounging, but it can also facilitate propagation of good solutions for further cumulative exploration. (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)

"This comprehensive volume - containing 27 chapters and contributions from six continents - presents and discusses key principles, perspectives, and practices of social learning in the context of sustainability. Social learning is explored from a range of fields challenged by sustainability including: organizational learning, environmental management and corporate social responsibility; multi-stakeholder governance; education, learning and educational psychology; multiple land-use and integrated rural development; and consumerism and critical consumer education. An entire section of the book is devoted to a number of (...) reflective case studies of people, organizations and communities using forms of social learning in moving towards sustainability. 'This book brings together a range of ideas, stories, and discussions about purposeful learning in communities aimed at creating a world that is more sustainable than the one currently in prospect....The book is designed to expand the network of conversations through which our society can confront various perspectives, discover emerging patterns, and apply learning to a variety of emotional and social contexts.' From the Foreword by Fritjof Capra, co-founder of the Center of Ecoliteracy. 'Joining what is so clear and refreshing in this book with the larger movements toward a critically democratic and activist education that is worthy of its name, is but one step in the struggle for sustainability. But it is an essential step if we are to use the insights that are included in this book.' From the Afterword by Michael Apple, author of 'Educating the ""Right"" Way: Markets, Standards, God, and Inequality'.". (shrink)

We live in turbulent times, our world is changing at accelerating speed. Information is everywhere, but wisdom appears in short supply when trying to address key inter-related challenges of our time such as; runaway climate change, the loss of biodiversity, the depletion of natural resources, the on-going homogenization of culture, and rising inequity. Living in such times has implications for education and learning. This book explores the possibilities of designing and facilitating learning-based change and transitions towards sustainability. In 31 chapters (...) contributors from across the world discuss (re)emerging forms of learning that not only assist in breaking down unsustainable routines, forms of governance, production and consumption, but also can help create ones that are more sustainable. The book has been divided into three parts: re-orienting science and society, re-connecting people and planet and re-imagining education and learning. This is essential reading for educators, educational designers, change agents, researchers, students, policymakers and entrepreneurs alike, who are concerned about the well-being of the planet and convinced of our ability to do better. The content and related issues can be discussed on the blog by editor Arjen Wals: Transformative learning. 'We are living in times of incertitude, complexity, and contestation, but also of connectivity, responsibility, and new opportunities. This book analyses the consequences of these times for learning in formal, non-formal, and informal education. It explores the possibilities offered by the concept of sustainability as a central category of a holistic paradigm which harmonizes human beings with Earth. To change people and to change the world are interdependent processes - this book contributes to both.' Moacir Gadotti, Director of Paulo Freire Institute, São Paulo, Brazil 'I hope you share my excitement about the innovations for sustainability that this book catalogues and analyses. While the ecological news is grim, the human news is not. Even in a time of accelerating change, people are showing their enormous capacities to learn, adapt, restore and protect.' From the Foreword by Juliet Schor, author of ‘True Wealth: how and why millions of Americans are creating a time-rich, ecologically-light, small-scale high-satisfaction economy’ 'This book implies a ‘culture of critical commitment’ in educational thinking and practice - engaged enough to make a real difference to social-ecological resilience and sustainability but reflexively critical enough to learn constantly from experience and to keep options open in working for a sustainability transformation.' From the Afterword by Stephen Sterling, Professor of Sustainability Education, Centre for Sustainable Futures, Plymouth University, United Kingdom. (shrink)

Lifelong learning has become a key concern as the focus of educational policy has shifted from mass schooling toward the learning society. The shift started in the mid 1960s and early 1970s under the impetus of a group of writers and adult educators, gravitating around UNESCO, with a humanist philosophy and a leftist agenda. The vocabulary of that movement was appropriated in the 1990s by other interests with a very different performativist agenda emphasizing effectiveness and economic outcomes. This change of (...) interest, described in the book, has signified the death of education. The Learning Society in a Postmodern World explores different theoretical resources to respond to this situation, mainly those that propose some restoration of an educated public or, to the contrary, individual self-creation, and uses the works of a broad range of philosophers and thinkers - notably MacIntyre, Habermas, Foucault, Derrida, Rorty, and Baudrillard. In addition, it raises important questions about postmodern and poststructuralist responses to education in the postmodern world. Its comprehensiveness and historical background make it an essential textbook for theoretical courses in lifelong learning and in educational theory in general. A broad range of interests and subject matter make it important reading for educators, policy specialists, media specialists, researchers on the subject of lifelong learning and on the relation between education and the postmodern world, political theorists, philosophers, and philosophers of education. (shrink)

We introduce a set of biologically and computationally motivated design choices for modeling the learning of language, or of other types of sequential, hierarchically structured experience and behavior, and describe an implemented system that conforms to these choices and is capable of unsupervised learning from raw natural‐language corpora. Given a stream of linguistic input, our model incrementally learns a grammar that captures its statistical patterns, which can then be used to parse or generate new data. The grammar constructed in this (...) manner takes the form of a directed weighted graph, whose nodes are recursively (hierarchically) defined patterns over the elements of the input stream. We evaluated the model in seventeen experiments, grouped into five studies, which examined, respectively, (a) the generative ability of grammar learned from a corpus of natural language, (b) the characteristics of the learned representation, (c) sequence segmentation and chunking, (d) artificial grammar learning, and (e) certain types of structure dependence. The model's performance largely vindicates our design choices, suggesting that progress in modeling language acquisition can be made on a broad front—ranging from issues of generativity to the replication of human experimental findings—by bringing biological and computational considerations, as well as lessons from prior efforts, to bear on the modeling approach. (shrink)

Two aspects of dynamic systems approaches that are pertinent to developmental models of reading are the emergence of a system with self-organizing characteristics, and its evolution over time to a stable state that is not easily modified or perturbed. The effects of dynamic stability may be seen in the differences obtained in the processing of print by beginner readers taught by different approaches to reading (phonics and text-centered), and more long-term effects on adults, consistent with these differences. However, there is (...) little direct evidence collected over time for the same participants. In this study, lexicalized (implicit) phonological processing, and explicit phonological and letter-sound skills are further examined in a precocious reader whose early development at 3 and 5 years has been extensively described (Cognition, 2000, 2004). At ages 10 and 14 years, comparisons were made with these earlier reports and skilled adult readers, using the same tasks for evidence of changes in reading processes. The results showed that along with an increase of reading accuracy and speed, her pattern of lexicalized phonological responses for reading did not change over time. Neither did her pattern of explicit phonological and letter-sound skills, aspects of which were inferior to her lexicalized phonological processing, and word reading. These results suggest dynamic stability of the word reading system. The early emergence of this system with minimal explicit skill development calls into question developmental reading theories that require such skills for learning to read. Currently, only the Knowledge Sources theory of reading acquisition can account for such findings. Consideration of these aspects of dynamic systems raise theoretical issues that could result in a paradigm shift with regard to best practice and intervention. (shrink)

Philosophers have recently focused on critical, epistemological challenges that arise from the opacity of deep neural networks. One might conclude from this literature that doing good science with opaque models is exceptionally challenging, if not impossible. Yet, this is hard to square with the recent boom in optimism for AI in science alongside a flood of recent scientific breakthroughs driven by AI methods. In this paper, I argue that the disconnect between philosophical pessimism and scientific optimism is driven by a (...) failure to examine how AI is actually used in science. I show that, in order to understand the epistemic justification for AI-powered breakthroughs, philosophers must examine the role played by deep learning as part of a wider process of discovery. The philosophical distinction between the 'context of discovery' and the 'context of justification' is helpful in this regard. I demonstrate the importance of attending to this distinction with two cases drawn from the scientific literature, and show that epistemic opacity need not diminish AI's capacity to lead scientists to significant and justifiable breakthroughs. (shrink)

In 1968, Jürgen Habermas claimed that, in an advanced technological society, the emancipatory force of knowledge can only be regained by actively recovering the ‘forgotten experience of reflection’. In this article, we argue that, in the contemporary situation, critical reflection requires a deliberative ambiance, a process of mutual learning, a consciously organised process of deliberative and distributed reflection. And this especially applies, we argue, to critical reflection concerning a specific subset of technologies which are actually oriented towards optimising human cognition. (...) In order to create a deliberative ambiance, fostering critical upstream reflection on emerging technologies, we developed the concept of a mutual learning exercise. Building on a number of case studies, we analyse what an MLE involves, both practically and conceptually, focussing on key aspects such as ambiance and expertise, the role of ‘genres of the imagination’ and the profiles of various ‘subcultures of debate’. Ideally, an MLE becomes a contemporary version of the Socratic agora, providing a stage where multiple and sometimes unexpected voices and perspectives mutually challenge each other, in order to strength-en the societal robustness and responsiveness of emerg-ing technologies. (shrink)

(2004). Workplace Learning in America: Shifting roles of households, schools and firms. Educational Philosophy and Theory: Vol. 36, No. 5, pp. 563-577.

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)

Understanding causal structure is a central task of human cognition. Causal learning underpins the development of our concepts and categories, our intuitive theories, and our capacities for planning, imagination and inference. During the last few years, there has been an interdisciplinary revolution in our understanding of learning and reasoning: Researchers in philosophy, psychology, and computation have discovered new mechanisms for learning the causal structure of the world. This new work provides a rigorous, formal basis for theory theories of concepts and (...) cognitive development, and moreover, the causal learning mechanisms it has uncovered go dramatically beyond the traditional mechanisms of both nativist theories, such as modularity theories, and empiricist ones, such as association or connectionism. (shrink)

In typical statistical learning studies, researchers define sequences in terms of the probability of the next item in the sequence given the current item (or items), and they show that high probability sequences are treated as more familiar than low probability sequences. Existing accounts of these phenomena all assume that participants represent statistical regularities more or less as they are defined by the experimenters—as sequential probabilities of symbols in a string. Here we offer an alternative, or possibly supplementary, hypothesis. Specifically, (...) rather than identifying or labeling individual stimuli discretely in order to predict the next item in a sequence, we need only assume that the participant is able to represent the stimuli as evincing particular similarity relations to one another, with sequences represented as trajectories through this similarity space. We present experiments in which this hypothesis makes sharply different predictions from hypotheses based on the assumption that sequences are learned over discrete, labeled stimuli. We also present a series of simulation models that encode stimuli as positions in a continuous two‐dimensional space, and predict the next location from the current location. Although no model captures all of the data presented here, the results of three critical experiments are more consistent with the view that participants represent trajectories through similarity space rather than sequences of discrete labels under particular conditions. (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)

This target article presents a theory of human cultural learning. Cultural learning is identified with those instances of social learning in which intersubjectivity or perspective-taking plays a vital role, both in the original learning process and in the resulting cognitive product. Cultural learning manifests itself in three forms during human ontogeny: imitative learning, instructed learning, and collaborative learning – in that order. Evidence is provided that this progression arises from the developmental ordering of the underlying social-cognitive concepts and processes involved. (...) Imitative learning relies on a concept of intentional agent and involves simple perspective-taking. Instructed learning relies on a concept of mental agent and involves alternating/coordinated perspective-taking (intersubjectivity). Collaborative learning relies on a concept of reflective agent and involves integrated perspective-taking (reflective intersubjectivity). A comparison of normal children, autistic children and wild and enculturated chimpanzees provides further evidence for these correlations between social cognition and cultural learning. Cultural learning is a uniquely human form of social learning that allows for a fidelity of transmission of behaviors and information among conspecifics not possible in other forms of social learning, thereby providing the psychological basis for cultural evolution. (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)

(2004). Workplace Learning in America: Shifting roles of households, schools and firms. Educational Philosophy and Theory: Vol. 36, No. 5, pp. 563-577.

Deep learning is currently the most prominent and widely successful method in artificial intelligence. Despite having played an active role in earlier artificial intelligence and neural network research, philosophers have been largely silent on this technology so far. This is remarkable, given that deep learning neural networks have blown past predicted upper limits on artificial intelligence performance—recognizing complex objects in natural photographs and defeating world champions in strategy games as complex as Go and chess—yet there remains no universally accepted explanation (...) as to why they work so well. This article provides an introduction to these networks as well as an opinionated guidebook on the philosophical significance of their structure and achievements. It argues that deep learning neural networks differ importantly in their structure and mathematical properties from the shallower neural networks that were the subject of so much philosophical reflection in the 1980s and 1990s. The article then explores several different explanations for their success and ends by proposing three areas of inquiry that would benefit from future engagement by philosophers of mind and science. (shrink)

We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to explore overlapping causal (...) patterns of variable granularity and scope. We characterise the conditions under which such a game is almost surely guaranteed to converge on a optimal explanation surface in polynomial time, and highlight obstacles that will tend to prevent the players from advancing beyond certain explanatory thresholds. The game serves a descriptive and a normative function, establishing a conceptual space in which to analyse and compare existing proposals, as well as design new and improved solutions. (shrink)

Testimony is an invaluable source of knowledge. We rely on the reports of those around us for everything from the ingredients in our food and medicine to the identity of our family members. Recent years have seen an explosion of interest in the epistemology of testimony. Despite the multitude of views offered, a single thesis is nearly universally accepted: testimonial knowledge is acquired through the process of transmission from speaker to hearer. In this book, Jennifer Lackey shows that this thesis (...) is false and, hence, that the literature on testimony has been shaped at its core by a view that is fundamentally misguided. She then defends a detailed alternative to this conception of testimony: whereas the views currently dominant focus on the epistemic status of what speakers believe, Lackey advances a theory that instead centers on what speakers say. The upshot is that, strictly speaking, we do not learn from one another's beliefs - we learn from one another's words. Once this shift in focus is in place, Lackey goes on to argue that, though positive reasons are necessary for testimonial knowledge, testimony itself is an irreducible epistemic source. This leads to the development of a theory that gives proper credence to testimony's epistemologically dual nature: both the speaker and the hearer must make a positive epistemic contribution to testimonial knowledge. The resulting view not only reveals that testimony has the capacity to generate knowledge, but it also gives appropriate weight to our nature as both socially indebted and individually rational creatures. The approach found in this book will, then, represent a radical departure from the views currently dominating the epistemology of testimony, and thus is intended to reshape our understanding of the deep and ubiquitous reliance we have on the testimony of those around us. (shrink)

Machine learning algorithms may radically improve our ability to diagnose and treat disease. For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models. Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers.

Beginning in earnest in the late 1990s, educational researchers devoted increasing attention to the study of “active learning,” leading to a robust literature on the topic in the scholarship of teaching and learning. Meanwhile, during largely the same period, political theorists discovered the radical philosophy of Giorgio Agamben, which soon after began to ripple through more radical forms of philosophy of education. While both the SoTL works on active learning and writings of “Agambenian” philosophers of education have offered new insights (...) into their respective fields, active learning has not yet received a systematic philosophical reflection and the community of Agambenian philosophy of education has not yet been systematized. This article addresses both gaps, first through an outline of existing Agambenian approaches to the philosophy of education and second by theorizing active learning as a form of “destituent potential.” The systematic reflection on the three threads of Agambenian philosophy of education—whatever, potentiality, and study—offers an introduction to less familiar readers, and the second section offers a model for how philosophical concepts can become theoretical tools for SoTL analysis. (shrink)

Conditional learning, where agents learn a conditional sentence ‘If A, then B,’ is difficult to incorporate into existing Bayesian models of learning. This is because conditional learning is not uniform: in some cases, learning a conditional requires decreasing the probability of the antecedent, while in other cases, the antecedent probability stays constant or increases. I argue that how one learns a conditional depends on the causal structure relating the antecedent and the consequent, leading to a causal model of conditional learning. (...) This model extends traditional Bayesian learning by incorporating causal models into agents’ epistemic states. On this theory, conditional learning proceeds in two steps. First, an agent learns a new causal model with the appropriate relationship between the antecedent and the consequent. Then, the agent narrows down the set of possible worlds to include only those which make the conditional proposition true. This model of learning can incorporate both standard cases of Bayesian learning and the non-uniform learning required to learn conditional information. (shrink)

Evidence from neurophysiological and psychological studies is coming together to shed light on how we represent and recognize objects. This review describes evidence supporting two major hypotheses: the first is that objects are represented in a mosaic-like form in which objects are encoded by combinations of complex, reusable features, rather than two-dimensional templates, or three-dimensional models. The second hypothesis is that transform-invariant representations of objects are learnt through experience, and that this learning is affected by the temporal sequence in which (...) different views of the objects are seen, as well as by their physical appearance. (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)

In lieu of an abstract, here is a brief excerpt of the content:Buddhist-Christian Studies 23 (2003) 189-193 [Access article in PDF] Buddhist Learning and Textual Practice in Eighteenth-Century Lankan Monastic Culture. By Anne M. Blackburn. Princeton, N.J.: Princeton University Press, 2001. x + 241 pp. Buddhist Learning is an important study of the emergence of the Siyam Nikaya (monastic order) in eighteenth-century Kandy, Sri Lanka's last Buddhist kingdom (which fell to the British only in 1815). Blackburn focuses on educational institutions (...) in her account, but frames it also to address larger issues in the study of Buddhist and non-Buddhist texts and history.An introductory chapter sets forth Blackburn's three central arguments (4): colonial and postcolonial scholarly constructions of Sri Lankan Buddhist "tradition" obscure the eighteenth-century phenomena that she reconstructs; the idea of "textual communities," developed in dialogue with contemporary scholarship on medieval European Christianity, provides a useful tool for analyzing those phenomena; [End Page 189] thus analyzed, the early Siyam Nikaya turns out to represent an innovative "reformulation" of Sri Lankan Buddhism, made possible by the establishment of the order itself, its systematization of monastic education, and the development of a bilingual commentarial genre for use within that system.Chapter 2 provides nonspecialists "at least a glimpse of the larger social context in which the emerging Siyam Nikaya participated" (23), with brief sketches of Sri Lankan Buddhist history, the structure and politics of the Kandyan court, the organization of monasticism prior to the rise of the Siyam Nikaya, and the presence of non-Buddhist religious leaders in Kandyan society. Chapter 3 begins with a discussion of Sri Lankan monastic education prior to 1730, then analyzes three stages in the emergence of distinctive Siyam Nikaya educational institutions (the early career of founder Valivita Saranamkara [1698-1778]; the late 1730s creation of a prototypical educational institution at Niyamakanda; and the 1753 importation of higher ordination [ upasampada ] from Thailand/formal establishment of the Siyam Nikaya, respectively), the history of royal patronage of these institutions, their new curriculum, its implementation and administration, its impact on monastic identity and lay-monastic relations, and the relationship of monastic erudition to prestige and patronage.Chapter 4 surveys Siyam Nikaya histories to argue that the order's rise was attended by "an innovative discourse on monasticism" that bolstered its authority by depicting its education-based interpretation of monastic virtue as "natural and desirable Buddhist practice" (105). Though these histories employed the rhetorical trope of decline and revival, their emphasis on Buddhist learning "signals a moment of profound change in Lankan Buddhism" (76-77), because they provided a charter for the new Nikaya while creating a perception that Siyam Nikaya monks practiced the best form of Buddhist monasticism. When imbibed by members of the royal court, this assured these monks steady patronage and prestige over their rivals, called ganinnanses, whom they directly and indirectly attacked. Chapter 5 zeroes in on Saranamkara and his Sararthadipani, a bilingual (Sinhala and Pali) commentary on the "four recitation sections" (catubhanavara) of protective (paritta) texts in Pali. This monumental work and the genre of bilingual commentaries (called sutra sannayas ) that it inspired, Blackburn maintains, were central to Saranamkara's claim to authority and created a distinctive monastic identity for Siyam Nikaya monks. Through close analysis of selected passages from Sararthadipani, Blackburn reveals the inner workings of bilingual commentary, particularly insofar as it allowed Saranamkara and his disciples to demonstrate their proper command of the classic Pali texts.In chapter 6 Blackburn argues that the sutra sannayas produced "a new Buddhist textual community" united in its reading and/or hearing of them. Exploring the ways in which Sararthadipani and the other sutra sannayas "constrained" readers to accept Saranamkara's vision of proper monasticism (such as direct instructions to readers and rhetorical structures), but recognizing that they also left open (in their use of simile, metaphor, and illustrative stories) the possibility of "less constrained" and "resistant" readings, Blackburn suggests that the genre extended the Siyam Nikaya's program beyond the small circle of Saranamkara's disciples to transform Sri Lankan monasticism [End Page 190] more generally. Moreover, she argues that given... (shrink)

In an atmosphere of shrinking state funds for edu cation and the glistening power of information technology, the administrators of educational institutions, especially higher education, are investing heavily in the construction and increased use of distance learning classrooms. Yet, in this rush to be both economi cally streamlined and technologically advanced, few policy makers are inquiring into the educational bene fits actually proffered to the end users, that is, teachers and students. This article advances such an inquiry by revealing how (...) the equipment in such facilities explic itly or implicitly embodies, and thereby determines, pedagogical choices. Such "hardwired" pedagogy may enhance the type and amount of data flow, but it effectively restricts the options for teaching and learn ing on the part of both the teacher and the student. Furthermore, virtual classrooms functionally transfer parametric control of classroom interaction from the participants to the equipment and therefore to the technicians, accountants, bureaucrats, manufacturers, and engineers. (shrink)

Two main schemes explain how a system adapts to its environment. Evolutionary models are grounded on three usual processes (variation, transmission, selection) acting at the population level. Learning models are concerned with the endogenous search for a better performance at the individual level. The first ones were initially favored by biology and the second well illustrated by game theory. The article examines first how game theory went to evolution and how biology later considered learning. It shows some examples of a (...) hybrid use of models of each type. It finally proposes a common framework for both types of models. (shrink)

This study aims to describe the learning motivation of students using virtual media when they are learning mathematics in grade 5. The research design applied in this research is classroom action research. The research is conducted in two phases which involve planning, action and observation and reflection. The results of the study revealed that intrinsic motivation to learn is most prevalent in the form of fun to learn mathematics with virtual media. Other forms of intrinsic motivation include curiosity, need and (...) interests, and satisfaction. Extrinsic motivation in the form of awarded and clarity of learning objectives. The most enjoyable learning experience for students was when they feel like they're learning more than they actually are. This level of enthusiasm increases with the level of learning complexity. The students are more active and motivated to learn. This can be evidenced by the increase in motivation to learn after the first phase. Unfortunately, the lack of bandwidth and time allocated for conducting experiments are some of the obstacles in the way of learning through virtual media. This study revealed that the use of virtual media could improve student's motivation for learning. (shrink)

Learning to love the reviewer -/- Issue: 43(4) November 2017. Viewpoint Page 83 -/- Quan Hoang Vuong Western University Hanoi, Centre for Interdisciplinary Social Research, Hanoi, Vietnam.

Suppose you have recently gained a disposition for recognizing a high-level kind property, like the property of being a wren. Wrens might look different to you now. According to the Phenomenal Contrast Argument, such cases of perceptual learning show that the contents of perception can include high-level kind properties such as the property of being a wren. I detail an alternative explanation for the different look of the wren: a shift in one’s attentional pattern onto other low-level properties. Philosophers have (...) alluded to this alternative before, but I provide a comprehensive account of the view, show how my account significantly differs from past claims, and offer a novel argument for the view. Finally, I show that my account puts us in a position to provide a new objection to the Phenomenal Contrast Argument. (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)

In LOT 2: The Language of Thought Revisited, Jerry Fodor argues that concept learning of any kind—even for complex concepts—is simply impossible. In order to avoid the conclusion that all concepts, primitive and complex, are innate, he argues that concept acquisition depends on purely noncognitive biological processes. In this paper, we show (1) that Fodor fails to establish that concept learning is impossible, (2) that his own biological account of concept acquisition is unworkable, and (3) that there are in fact (...) many promising general models for explaining how concepts are learned. (shrink)

We propose a method of learning indicative conditional information. An agent learns conditional information by Jeffrey imaging on the minimally informative proposition expressed by a Stalnaker conditional. We show that the predictions of the proposed method align with the intuitions in Douven, 239–263 2012)’s benchmark examples. Jeffrey imaging on Stalnaker conditionals can also capture the learning of uncertain conditional information, which we illustrate by generating predictions for the Judy Benjamin Problem.

Implicit learning is a core process for the acquisition of a complex, rule‐based environment from mere interaction, such as motor action, skill acquisition, or language. A body of evidence suggests that implicit knowledge governs music acquisition and perception in nonmusicians and musicians, and that both expert and nonexpert participants acquire complex melodic, harmonic, and other features from mere exposure. While current findings and computational modeling largely support the learning of chunks, some results indicate learning of more complex structures. Despite the (...) body of evidence, more research is required to support the cross‐cultural validity of implicit learning and to show that core and more complex music theoretical features are acquired implicitly. (shrink)

Machine learning researchers distinguish between reinforcement learning and supervised learning and refer to reinforcement learning systems as “agents”. This paper vindicates the claim that systems trained by reinforcement learning are agents while those trained by supervised learning are not. Systems of both kinds satisfy Dretske’s criteria for agency, because they both learn to produce outputs selectively in response to inputs. However, reinforcement learning is sensitive to the instrumental value of outputs, giving rise to systems which exploit the effects of outputs (...) on subsequent inputs to achieve good performance over episodes of interaction with their environments. Supervised learning systems, in contrast, merely learn to produce better outputs in response to individual inputs. (shrink)