Results for 'learning of the mind'

984 found
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  1.  11
    Can Mindfulness Help to Alleviate Loneliness? A Systematic Review and Meta-Analysis.Siew Li Teoh, Vengadesh Letchumanan & Learn-Han Lee - 2021 - Frontiers in Psychology 12.
    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 (...)
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  2. Mental models in learning situations.N. M. Seel - 2006 - In Carsten Held, Markus Knauff & Gottfried Vosgerau, Mental models and the mind: current developments in cognitive psychology, neuroscience, and philosophy of mind. Boston: Elsevier.
     
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  3.  26
    Crying and learning to speak.Paul Standish - 2015 - In Danièle Moyal-Sharrock, Volker Munz & Annalisa Coliva, Mind, Language and Action: Proceedings of the 36th International Wittgenstein Symposium. Boston: De Gruyter. pp. 481-494.
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  4. Implicit attitudes, social learning, and moral credibility.Michael Brownstein - 2016 - In Julian Kiverstein, The Routledge Handbook of Philosophy of the Social Mind. New York: Routledge. pp. 314-335.
  5.  72
    Learning to be Human.Fran Conroy - 1990 - The Personalist Forum 6 (1):27-49.
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  6.  36
    Learning to be a Moral Agent.William A. Rottschaeffer - 1986 - The Personalist Forum 2 (2):122-142.
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  7. Mindful Learning Experience Facilitates Mastery Experience Through Heightened Flow and Self-Efficacy in Game-Based Creativity Learning.Yu-chu Yeh, Szu-Yu Chen, Elisa Marie Rega & Chin-Shan Lin - 2019 - Frontiers in Psychology 10:460587.
    To date, game-based learning programs that include comprehensive creativity skills and disposition training are still very limited. The present researchers developed a comprehensive game-based creativity learning program for fifth and sixth grade pupils. Further analysis presented relationship trends between mindful learning experience, flow experience, self-efficacy, and mastery experience. Eighty-three 5th and 6th grade participants undertook the six-week game-based creativity learning program. Upon the completion of experimental instruction, those who scored higher on the concerned variables improved more (...)
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  8. Pedagogy and social learning in human development.Richard Moore - 2016 - In Julian Kiverstein, The Routledge Handbook of the Social Mind. Routledge. pp. 35-52.
  9. Systematic minds, unsystematic models: Learning transfer in humans and networks. [REVIEW]Steven Phillips - 1999 - Minds and Machines 9 (3):383-398.
    Minds are said to be systematic: the capacity to entertain certain thoughts confers to other related thoughts. Although an important property of human cognition, its implication for cognitive architecture has been less than clear. In part, the uncertainty is due to lack of precise accounts on the degree to which cognition is systematic. However, a recent study on learning transfer provides one clear example. This study is used here to compare transfer in humans and feedforward networks. Simulations and analysis (...)
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  10. Aesthetic concepts, perceptual learning, and linguistic enculturation: Considerations from Wittgenstein, language, and music.Adam M. Croom - 2012 - Integrative Psychological and Behavioral Science 46:90-117.
    Aesthetic non-cognitivists deny that aesthetic statements express genuinely aesthetic beliefs and instead hold that they work primarily to express something non-cognitive, such as attitudes of approval or disapproval, or desire. Non-cognitivists deny that aesthetic statements express aesthetic beliefs because they deny that there are aesthetic features in the world for aesthetic beliefs to represent. Their assumption, shared by scientists and theorists of mind alike, was that language-users possess cognitive mechanisms with which to objectively grasp abstract rules fixed independently of (...)
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  11.  49
    Language learning, power laws, and sexual selection.Ted Briscoe - 2008 - Mind and Society 7 (1):65-76.
    I discuss the ubiquity of power law distributions in language organisation (and elsewhere), and argue against Miller’s (The mating mind: How sexual choice shaped the evolution of human nature, William Heinemann, London, 2000) argument that large vocabulary size is a consequence of sexual selection. Instead I argue that power law distributions are evidence that languages are best modelled as dynamical systems but raise some issues for models of iterated language learning.
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  12.  88
    Imitation, Mind Reading, and Social Learning.Philip S. Gerrans - 2013 - Biological Theory 8 (1):20-27.
    Imitation has been understood in different ways: as a cognitive adaptation subtended by genetically specified cognitive mechanisms; as an aspect of domain general human cognition. The second option has been advanced by Cecilia Heyes who treats imitation as an instance of associative learning. Her argument is part of a deflationary treatment of the “mirror neuron” phenomenon. I agree with Heyes about mirror neurons but argue that Kim Sterelny has provided the tools to provide a better account of the nature (...)
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  13.  32
    Inducing mind sets in self‐regulated learning with motivational information.R. Martens, C. de Brabander, J. Rozendaal, M. Boekaerts & R. van der Leeden - 2010 - Educational Studies 36 (3):311-327.
    The way students perceive a learning climate (e.g. controlling or stimulating) is significantly influenced by feedback and assessment. However, at present much is unclear about the relation between feedback and motivational state. More specifically, the interplay with student characteristics is unclear. Since there is a strong increase of group work, the central research question is what are the effects of positive, neutral or negative feedback presented to collaborating teams of students, on students? intrinsic motivation, performance and on group processes? (...)
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  14.  59
    Learning to diversify yourself.David A. Cowan - 2005 - World Futures 61 (5):347 – 369.
    In response to increasing calls to realize more potential from diversity in organizations, Frances Hesselbein, CEO of Peter Drucker Leadership Institute, challenged management scholars to enrich the understanding of diversity. Her challenge contains descriptive and normative elements, and extends beyond learning only "about" others, toward "diversifying oneself." With this purpose in mind, this two-stage study develops a framework of divergent learning. The first stage describes a philosophical foundation grounded in literature that orients its key concepts toward divergent (...)
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  15.  10
    Toward Learning Machines at a Mother and Baby Unit.Magnus Boman, Johnny Downs, Abubakrelsedik Karali & Susan Pawlby - 2020 - Frontiers in Psychology 11:567310.
    Agnostic analyses of unique video material from a Mother and Baby Unit were carried out to investigate the usefulness of such analyses to the unit. The goal was to improve outcomes: the health of mothers and their babies. The method was to implement a learning machine that becomes more useful over time and over task. A feasible set-up is here described, with the purpose of producing intelligible and useful results to healthcare professionals at the unit by means of a (...)
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  16. Learning via queries in $\lbrack +,.William I. Gasarch, Mark G. Pleszkoch & Robert Solovay - 1992 - Journal of Symbolic Logic 57 (1):53-81.
    We prove that the set of all recursive functions cannot be inferred using first-order queries in the query language containing extra symbols $\lbrack +, . The proof of this theorem involves a new decidability result about Presburger arithmetic which is of independent interest. Using our machinery, we show that the set of all primitive recursive functions cannot be inferred with a bounded number of mind changes, again using queries in $\lbrack +, . Additionally, we resolve an open question in (...)
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  17.  11
    Intergenerational learning and transformative leadership for sustainable futures.Peter Blaze Corcoran & Brandon P. Hollingshead (eds.) - 2014 - Brill | Wageningen Academic.
    The work of creating the future is being done now ─ and much of it is unsustainable in terms of natural and cultural resources. How will the next generation of leadership for environmental sustainability be raised up? Can we imagine sustainable futures, and can we enable transformative leadership to help us realize them? How can we best ensure that the several generations share their particular knowledge? What are the ethical frameworks, methodologies, curricula, and tools necessary for advancing and strengthening education (...)
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  18. Mind change efficient learning.Oliver Schulte - unknown
    This paper studies efficient learning with respect to mind changes. Our starting point is the idea that a learner that is efficient with respect to mind changes minimizes mind changes not only globally in the entire learning problem, but also locally in subproblems after receiving some evidence. Formalizing this idea leads to the notion of uniform mind change optimality. We characterize the structure of language classes that can be identified with at most α (...) changes by some learner (not necessarily effective): A language class L is identifiable with α mind changes iff the accumulation order of L is at most α. Accumulation order is a classic concept from point-set topology. To aid the construction of learning algorithms, we show that the characteristic property of uniformly mind change optimal learners is that they output conjectures (languages) with maximal accumulation order. We illustrate the theory by describing mind change optimal learners for various problems such as identifying linear subspaces and one-variable patterns. (shrink)
     
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  19. Deep learning: A philosophical introduction.Cameron Buckner - 2019 - Philosophy Compass 14 (10):e12625.
    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 (...)
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  20.  54
    Adaptively Rational Learning.Sarah Wellen & David Danks - 2016 - Minds and Machines 26 (1-2):87-102.
    Research on adaptive rationality has focused principally on inference, judgment, and decision-making that lead to behaviors and actions. These processes typically require cognitive representations as input, and these representations must presumably be acquired via learning. Nonetheless, there has been little work on the nature of, and justification for, adaptively rational learning processes. In this paper, we argue that there are strong reasons to believe that some learning is adaptively rational in the same way as judgment and decision-making. (...)
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  21.  79
    Learning from Asian philosophy.Joel Kupperman - 1999 - New York: Oxford University Press.
    In an attempt to bridge the vast divide between classical Asian thought and contemporary Western philosophy, Joel J. Kupperman finds that the two traditions do not, by and large, supply different answers to the same questions. Rather, each tradition is searching for answers to their own set of questions--mapping out distinct philosophical investigations. In this groundbreaking book, Kupperman argues that the foundational Indian and Chinese texts include lines of thought that can enrich current philosophical practice, and in some cases provide (...)
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  22.  84
    Learning Language.Ruth Garrett Millikan - unknown
    Many students of pragmatics and child language have come to believe that in order to learn a language a child must first have a 'theory of mind,' a grasp that speakers mentally represent the content they would convey when they speak. This view is reinforced by the Gricean theory of communication, according to which speakers intend their words to cause hearers to believe or to do certain things and hearers must recognize these intentions if they are to comply. The (...)
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  23.  95
    Machine learning by imitating human learning.Chang Kuo-Chin, Hong Tzung-Pei & Tseng Shian-Shyong - 1996 - Minds and Machines 6 (2):203-228.
    Learning general concepts in imperfect environments is difficult since training instances often include noisy data, inconclusive data, incomplete data, unknown attributes, unknown attribute values and other barriers to effective learning. It is well known that people can learn effectively in imperfect environments, and can manage to process very large amounts of data. Imitating human learning behavior therefore provides a useful model for machine learning in real-world applications. This paper proposes a new, more effective way to represent (...)
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  24.  8
    Organizational moral learning: a communication approach.Ryan S. Bisel - 2018 - London: Routledge, Taylor & Francis Group.
    Rethinking organizational ethics training -- Moral intuition: advances in moral psychology and neuroscience -- The social intuitionist model -- Communication and the new organizational ethics -- How cultur(ing) works -- Pluralistic moral ignorance and spirals of silent misdirection -- Here-and-now ethics talk in the workplace -- Sensemaking and identity: what to expect from moral reasoning -- Substituting here-and-now ethics talk -- Organizational learning and organizational communication -- From individual moral intuition to organizational moral learning -- Organizing for moral (...)
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  25.  83
    Learning to Love.Christopher Cowley - 2010 - Philosophical Topics 38 (1):1-15.
    Imagine that you find yourself in a situation of considerable adversity and apparent permanence. Does it make sense for me to advise you to learn to love your situation? I argue that such advice is capable of a robust meaning beyond the mere expression of compassion, and far beyond the pragmatic advice to ‘accept it’ or ‘make the best of it’. I respond to the objections that love cannot be commanded, and that I am counselling pernicious forms of self-deception or (...)
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  26.  30
    Knowing and learning: from Hirst to Ofsted.Andrew John Davis - 2023 - Journal of Philosophy of Education 57 (1):214-226.
    Hirst always highlighted knowledge when reflecting on the school curriculum. He replaced his early focus on liberal education, the development of mind and theoretical knowledge by emphasizing the practical and practices as a curriculum starting point and for the framing of educational aims. In this paper I explore links between Hirst’s philosophical treatment of knowledge and some currently contested aspects of UK government education policies. I also note some ways in which his work relates to selected present-day debates in (...)
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  27.  24
    Complexity theory and learning: Less radical than it seems?David Guile & Rachel J. Wilde - 2024 - Educational Philosophy and Theory 56 (5):439-447.
    In a spirit of collegial support, this paper argues that Beckett and Hager’s theoretical justification and empirical exemplifications do not do full justice to the complexity of group or team learning. We firstly reaffirm our support for the theoretical argument Becket and Hager make, though expressing some reservations about Complexity Theory, to explain the taken-for-granted assumptions that learning by an individual is the paradigm case of learning and that context plays a minimal role in this process. Drawing (...)
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  28. Deep learning and cognitive science.Pietro Perconti & Alessio Plebe - 2020 - Cognition 203:104365.
    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 (...)
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  29.  42
    Spontaneous coordination and evolutionary learning processes in an agent-based model.Pierre Barbaroux & Gilles Enée - 2005 - Mind and Society 4 (2):179-195.
    This paper is concerned with adaptive learning and coordination processes. Implementing agent-based modeling techniques (Learning Classifier Systems, LCS), we focus on the twofold impact of cognitive and environmental complexity on learning and coordination. Within this framework, we introduce the notion of Adaptive Learning Agent with Rule-based Memory (ALARM), which is a particular class of Artificial Adaptive Agent (AAA, Holland and Miller 1991). We show that equilibrium is approached to a high degree, but never perfectly reached. We (...)
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  30.  28
    Learning to Live Naturally: Stoic Ethics and its Modern Significance, by Christopher Gill.Raphael Woolf - forthcoming - Mind:fzad044.
    Gill’s rich and comprehensive discussion of Stoic ethical thought adopts an approach that would surely have found favour with the Stoics themselves: to present.
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  31. Distributed learning: Educating and assessing extended cognitive systems.Richard Heersmink & Simon Knight - 2018 - Philosophical Psychology 31 (6):969-990.
    Extended and distributed cognition theories argue that human cognitive systems sometimes include non-biological objects. On these views, the physical supervenience base of cognitive systems is thus not the biological brain or even the embodied organism, but an organism-plus-artifacts. In this paper, we provide a novel account of the implications of these views for learning, education, and assessment. We start by conceptualising how we learn to assemble extended cognitive systems by internalising cultural norms and practices. Having a better grip on (...)
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  32. Learning from experience and conditionalization.Peter Brössel - 2023 - Philosophical Studies 180 (9):2797-2823.
    Bayesianism can be characterized as the following twofold position: (i) rational credences obey the probability calculus; (ii) rational learning, i.e., the updating of credences, is regulated by some form of conditionalization. While the formal aspect of various forms of conditionalization has been explored in detail, the philosophical application to learning from experience is still deeply problematic. Some philosophers have proposed to revise the epistemology of perception; others have provided new formal accounts of conditionalization that are more in line (...)
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  33.  83
    Empiricist word learning.Dan Ryder & Oleg V. Favorov - 2001 - Behavioral and Brain Sciences 24 (6):1117-1117.
    At first, Bloom's theory appears inimical to empiricism, since he credits very young children with highly sophisticated cognitive resources (e.g., a theory of mind and a belief that real kinds have essences), and he also attacks the empiricist's favoured learning theory, namely, associationism. We suggest that, on the contrary, the empiricist can embrace much of what Bloom says.
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  34.  35
    Learning to reason: an introduction to logic, sets and relations.Nancy Rodgers - 2000 - New York: Wiley.
    Learn how to develop your reasoning skills and how to write well-reasoned proofs Learning to Reason shows you how to use the basic elements of mathematical language to develop highly sophisticated, logical reasoning skills. You'll get clear, concise, easy-to-follow instructions on the process of writing proofs, including the necessary reasoning techniques and syntax for constructing well-written arguments. Through in-depth coverage of logic, sets, and relations, Learning to Reason offers a meaningful, integrated view of modern mathematics, cuts through confusing (...)
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  35.  75
    (1 other version)Learning, empowerment and judgement.Michael Luntley - 2007 - Educational Philosophy and Theory 39 (4):418–431.
    Here is a distinction that appears very simple, looks compelling and seems to be deeply rooted in our reflections on learning. 1 The distinction is between activities of learning that involve training and those that involve reasoning. In the former, the pupil is a passive recipient of habits of mind and action. The mechanism by which they acquire these habits is mimesis, not reasoning. In contrast, learning by reasoning involves considerable mental activity by the pupil who (...)
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  36. Memory: A logical learning account.Joseph F. Rychlak - 1996 - Journal of Mind and Behavior 17 (3):229-50.
    An interpretation of memory from the perspective of logical learning theory is presented. In contrast to traditional associationistic theories of learning and memory, which rest on mediation modeling, LLT rests on a predication model. Predication draws on formal and final causation whereas mediation is limited to material and efficient causation. It is held in LLT that memory begins in predicate organization, where framing meanings are logically extended to targets. Passage of time is irrelevant in this meaning extension. The (...)
     
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  37. Learning'about'versus learning'from'other minds: Human pedagogy and its implications.Gyorgy Gergely - 2005 - In Peter Carruthers, Stephen Laurence & Stephen P. Stich, The Innate Mind: Structure and Contents. New York, US: Oxford University Press on Demand.
  38. Learning Theory and Descriptive Set Theory.Kevin T. Kelly - unknown
    then essentially characterized the hypotheses that mechanical scientists can successfully decide in the limit in terms of arithmetic complexity. These ideas were developed still further by Peter Kugel [4]. In this paper, I extend this approach to obtain characterizations of identification in the limit, identification with bounded mind-changes, and identification in the short run, both for computers and for ideal agents with unbounded computational abilities. The characterization of identification with n mind-changes entails, as a corollary, an exact arithmetic (...)
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  39.  63
    Humean learning (how to learn).Jeffrey A. Barrett - forthcoming - Philosophical Studies:1-17.
    David Hume’s skeptical solution to the problem of induction was grounded in his belief that we learn by means of custom. We consider here how a form of reinforcement learning like custom may allow an agent to learn how to learn in other ways as well. Specifically, an agent may learn by simple reinforcement to adopt new forms of learning that work better than simple reinforcement in the context of specific tasks. We will consider how such a bootstrapping (...)
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  40.  16
    Assessment as Learning: How Does Peer Assessment Function in Students' Learning?Shengkai Yin, Fang Chen & Hui Chang - 2022 - Frontiers in Psychology 13.
    Peer assessment is employed as one fundamental practice of classroom-based assessment in terms of its learning-oriented and formative nature. The exercise of peer assessment has multiple and additional benefits for student learning. However, research into the learning processes in peer assessment is scarce both in theory and in practice, making it difficult to evaluate and pinpoint its value as a tool in assessment as learning. This study focuses both on the learning process and outcome through (...)
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  41.  24
    A pedagogical framework for facilitating parents’ learning in nurse–parent partnership.Nick Hopwood, Teena Clerke & Anne Nguyen - 2018 - Nursing Inquiry 25 (2):e12220.
    Nursing work increasingly demands forms of expertise that complement specialist knowledge. In child and family nursing, this need arises when nurses work in partnership with parents of young children at risk. Partnership means working with parents in respectful, negotiated and empowering ways. Existing partnership literature emphasises communicative and relational skills, but this paper focuses on nurses’ capacities to facilitate parents’ learning. Referring to data from home visiting, day‐stay and specialist toddler clinic services in Sydney, a pedagogical framework is presented. (...)
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  42. Strong semantic systematicity from Hebbian connectionist learning.Robert Hadley & Michael Hayward - 1997 - Minds and Machines 7 (1):1-55.
    Fodor's and Pylyshyn's stand on systematicity in thought and language has been debated and criticized. Van Gelder and Niklasson, among others, have argued that Fodor and Pylyshyn offer no precise definition of systematicity. However, our concern here is with a learning based formulation of that concept. In particular, Hadley has proposed that a network exhibits strong semantic systematicity when, as a result of training, it can assign appropriate meaning representations to novel sentences (both simple and embedded) which contain words (...)
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  43.  85
    Reinforcement learning and artificial agency.Patrick Butlin - 2024 - Mind and Language 39 (1):22-38.
    There is an apparent connection between reinforcement learning and agency. Artificial entities controlled by reinforcement learning algorithms are standardly referred to as agents, and the mainstream view in the psychology and neuroscience of agency is that humans and other animals are reinforcement learners. This article examines this connection, focusing on artificial reinforcement learning systems and assuming that there are various forms of agency. Artificial reinforcement learning systems satisfy plausible conditions for minimal agency, and those which use (...)
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  44. Learning How to Represent: An Associationist Account.Nancy Salay - 2019 - Journal of Mind and Behavior 40 (2):121-14.
    The paper develops a positive account of the representational capacity of cognitive systems: simple, associationist learning mechanisms and an architecture that supports bootstrapping are sufficient conditions for symbol tool use. In terms of the debates within the philosophy of mind, this paper offers a plausibility account of representation externalism, an alternative to the reductive, computational/representational models of intentionality that still play a leading role in the field. Although the central theme here is representation, methodologically this view complements embodied, (...)
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  45.  17
    University Social Responsibility, Service Learning, and Students' Personal, Professional, and Civic Education.Márcia Coelho & Isabel Menezes - 2021 - Frontiers in Psychology 12:617300.
    The long-standing vision of universities as the “alma mater” of students and graduates is a demonstration of its role as sustaining the person, the expert/professional, and the citizen. This role has persisted in the face of rising global challenges such as the emergence of new learning spaces, the growing diversity of publics, the call for productivity and performativity, and the hope for a significant engagement with the community and the public good. These sometimes conflicting tendencies have also stimulated higher (...)
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  46.  12
    Creative ways to learn ethics: an experiential training manual for helping professionals.Dayna Guido - 2019 - New York, NY: Routledge.
    Creative Ways to Learn Ethics is an accessible, easy-to-read guide that compiles a variety of ethics trainings to help professionals stimulate their minds, relieve stress, and increase engagement and memory retention. The book uses a range of experiential and thought-provoking approaches, including contemplative exercises, expressive arts, games, and media. Each chapter contains objectives, detailed procedures, adaptations for different audiences, and handouts. Trainers, educators, clinicians, and other mental health professionals can use these exercises in various settings and modify them to meet (...)
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  47. Learning to love: From egoism to generosity in Descartes.Patrick Frierson - 2002 - Journal of the History of Philosophy 40 (3):313-338.
    Patrick Frierson - Learning to Love: From Egoism to Generosity in Descartes - Journal of the History of Philosophy 40:3 Journal of the History of Philosophy 40.3 313-338 Learning to Love: From Egoism to Generosity in Descartes Patrick R. Frierson The whole of philosophy is like a tree. The roots are metaphysics, the trunk is physics, and the branches emerging from the trunk are all the other sciences, which may be reduced to three principal ones, namely medicine, mechanics, (...)
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  48.  27
    Moving forward on cultural learning.Angelina S. Lillard - 1993 - Behavioral and Brain Sciences 16 (3):528-529.
    Tomasello, Kruger & Ratner make the very interesting and valid point that the transmission of culture must depend on understanding others' minds. Culture is shared among a people and is passed on to progeny. The sharing of culture implies that the purpose of (and therefore the meaning behind) any given cultural element (behavioral tradition, word, or artifact) is understood. Because meaning or purpose emanates from minds, something about others' minds must be understood in order to truly learn some element of (...)
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  49. Learning and Value Change.J. Dmitri Gallow - 2019 - Philosophers' Imprint 19:1--22.
    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 (...)
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  50.  80
    Learning concepts by arranging appropriate training order.Yao-Tung Hsu, Tzung-Pei Hong & Shian-Shyong Tseng - 2001 - Minds and Machines 11 (3):399-415.
    Machine learning has been proven useful for solving the bottlenecks in building expert systems. Noise in the training instances will, however, confuse a learning mechanism. Two main steps are adopted here to solve this problem. The first step is to appropriately arrange the training order of the instances. It is well known from Psychology that different orders of presentation of the same set of training instances to a human may cause different learning results. This idea is used (...)
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