Results for 'reinforcement learning'

744 found
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  1. The Archimedean Trap: Why Traditional Reinforcement Learning Will Probably Not Yield AGI.Samuel Allen Alexander - manuscript
    After generalizing the Archimedean property of real numbers in such a way as to make it adaptable to non-numeric structures, we demonstrate that the real numbers cannot be used to accurately measure non-Archimedean structures. We argue that, since an agent with Artificial General Intelligence (AGI) should have no problem engaging in tasks that inherently involve non-Archimedean rewards, and since traditional reinforcement learning rewards are real numbers, therefore traditional reinforcement learning cannot lead to AGI. We indicate two (...)
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  2. Punishment and Psychopathy: A Case-Control Functional MRI Investigation of Reinforcement Learning in Violent Antisocial Personality Disordered Men.Sarah Gregory, R. James Blair, Dominic Ffytche, Andrew Simmons, Veena Kumari, Sheilagh Hodgins & Nigel Blackwood - 2014 - Lancet Psychiatry 2:153–160.
    Background Men with antisocial personality disorder show lifelong abnormalities in adaptive decision making guided by the weighing up of reward and punishment information. Among men with antisocial personality disorder, modifi cation of the behaviour of those with additional diagnoses of psychopathy seems particularly resistant to punishment. Methods We did a case-control functional MRI (fMRI) study in 50 men, of whom 12 were violent off enders with antisocial personality disorder and psychopathy, 20 were violent off enders with antisocial personality disorder but (...)
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  3. Neural Correlates of Error-Related Learning Deficits in Individuals with Psychopathy.A. K. L. von Borries, Inti A. Brazil, B. H. Bulten, J. K. Buitelaar, R. J. Verkes & E. R. A. de Bruijn - 2010 - Psychological Medicine 40:1559–1568.
    The results are interpreted in terms of a deficit in initial rule learning and subsequent generalization of these rules to new stimuli. Negative feedback is adequately processed at a neural level but this information is not used to improve behaviour on subsequent trials. As learning is degraded, the process of error detection at the moment of the actual response is diminished. Therefore, the current study demonstrates that disturbed error-monitoring processes play a central role in the often reported (...) deficits in individuals with PP. (shrink)
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  4. HCI Model with Learning Mechanism for Cooperative Design in Pervasive Computing Environment.Hong Liu, Bin Hu & Philip Moore - 2015 - Journal of Internet Technology 16.
    This paper presents a human-computer interaction model with a three layers learning mechanism in a pervasive environment. We begin with a discussion around a number of important issues related to human-computer interaction followed by a description of the architecture for a multi-agent cooperative design system for pervasive computing environment. We present our proposed three- layer HCI model and introduce the group formation algorithm, which is predicated on a dynamic sharing niche technology. Finally, we explore the cooperative reinforcement (...) and fusion algorithms; the paper closes with concluding observations and a summary of the principal work and contributions of this paper. (shrink)
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  5. New Prospects for Aesthetic Hedonism.Mohan Matthen - 2018 - In Jennifer A. McMahon (ed.), Social Aesthetics and Moral Judgment: Pleasure, Reflection and Accountability. London: Routledge. pp. 13-33.
    Because culture plays a role in determining the aesthetic merit of a work of art, intrinsically similar works can have different aesthetic merit when assessed in different cultures. This paper argues that a form of aesthetic hedonism is best placed to account for this relativity of aesthetic value. This form of hedonism is based on a functional account of aesthetic pleasure, according to which it motivates and enables mental engagement with artworks, and an account of pleasure-learning, in which it (...)
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  6. There is No General AI: Why Turing Machines Cannot Pass the Turing Test.Jobst Landgrebe & Barry Smith - 2020 - arXiv.
    Since 1950, when Alan Turing proposed what has since come to be called the Turing test, the ability of a machine to pass this test has established itself as the primary hallmark of general AI. To pass the test, a machine would have to be able to engage in dialogue in such a way that a human interrogator could not distinguish its behaviour from that of a human being. AI researchers have attempted to build machines that could meet this requirement, (...)
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  7. Self-Assembling Networks.Jeffrey A. Barrett, Brian Skyrms & Aydin Mohseni - 2019 - British Journal for the Philosophy of Science 70 (1):1-25.
    We consider how an epistemic network might self-assemble from the ritualization of the individual decisions of simple heterogeneous agents. In such evolved social networks, inquirers may be significantly more successful than they could be investigating nature on their own. The evolved network may also dramatically lower the epistemic risk faced by even the most talented inquirers. We consider networks that self-assemble in the context of both perfect and imperfect communication and compare the behaviour of inquirers in each. This provides a (...)
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  8. One Decade of Universal Artificial Intelligence.Marcus Hutter - 2012 - In Pei Wang & Ben Goertzel (eds.), Theoretical Foundations of Artificial General Intelligence. Springer. pp. 67--88.
    The first decade of this century has seen the nascency of the first mathematical theory of general artificial intelligence. This theory of Universal Artificial Intelligence (UAI) has made significant contributions to many theoretical, philosophical, and practical AI questions. In a series of papers culminating in book (Hutter, 2005), an exciting sound and complete mathematical model for a super intelligent agent (AIXI) has been developed and rigorously analyzed. While nowadays most AI researchers avoid discussing intelligence, the award-winning PhD thesis (Legg, 2008) (...)
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  9. The Transition to Experiencing: II. The Evolution of Associative Learning Based on Feelings.Simona Ginsburg & Eva Jablonka - 2007 - Biological Theory 2 (3):231-243.
    We discuss the evolutionary transition from animals with limited experiencing to animals with unlimited experiencing and basic consciousness. This transition was, we suggest, intimately linked with the evolution of associative learning and with flexible reward systems based on, and modifiable by, learning. During associative learning, new pathways relating stimuli and effects are formed within a highly integrated and continuously active nervous system. We argue that the memory traces left by such new stimulus-effect relations form dynamic, flexible, and (...)
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  10. Relieving Pain Using Dose-Extending Placebos.Luana Colloca, Paul Enck & David DeGrazia - 2016 - PAIN 157:1590-1598.
    Placebos are often used by clinicians, usually deceptively and with little rationale or evidence of benefit, making their use ethically problematic. In contrast with their typical current use, a provocative line of research suggests that placebos can be intentionally exploited to extend analgesic therapeutic effects. Is it possible to extend the effects of drug treatments by interspersing placebos? We reviewed a database of placebo studies, searching for studies that indicate that placebos given after repeated administration of active treatments acquire medication-like (...)
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  11. Abnormal Ventromedial Prefrontal Cortex Function in Children With Psychopathic Traits During Reversal Learning.Elizabeth C. Finger, Abigail A. Marsh, Derek G. Mitchell, Marguerite E. Reid, Courtney Sims, Salima Budhani, David S. Kosson, Gang Chen, Kenneth E. Towbin, Ellen Leibenluft, Daniel S. Pine & James R. Blair - 2008 - Archives of General Psychiatry 65: 586–594.
    Context — Children and adults with psychopathic traits and conduct or oppositional defiant disorder demonstrate poor decision making and are impaired in reversal learning. However, the neural basis of this impairment has not previously been investigated. Furthermore, despite high comorbidity of psychopathic traits and attention deficit/hyperactivity disorder, to our knowledge, no research has attempted to distinguish neural correlates of childhood psychopathic traits and attention-deficit/hyperactivity disorder. Objective—To determine the neural regions that underlie the reversal learning impairments in children with (...)
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  12.  99
    What Can Artificial Intelligence Do for Scientific Realism?Petr Spelda & Vit Stritecky - forthcoming - Axiomathes:1-20.
    The paper proposes a synthesis between human scientists and artificial representation learning models as a way of augmenting epistemic warrants of realist theories against various anti-realist attempts. Towards this end, the paper fleshes out unconceived alternatives not as a critique of scientific realism but rather a reinforcement, as it rejects the retrospective interpretations of scientific progress, which brought about the problem of alternatives in the first place. By utilising adversarial machine learning, the synthesis explores possibility spaces of (...)
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  13. Pragmatism : A Learning Theory for the Future.Bente Elkjaer - 2009 - In Knud Illeris (ed.), Contemporary Theories of Learning: Learning Theorists -- In Their Own Words. London: Routledge. pp. 74-89.
    A theory of learning for the future advocates the teaching of a preparedness to respond in a creative way to difference and otherness. This includes an ability to act imaginatively in situations of uncertainties. John Dewey’s pragmatism holds the key to such a learning theory his view of the continuous meetings of individuals and environments as experimental and playful. That pragmatism has not yet been acknowledged as a relevant learning theory for the future may be due to (...)
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  14. A Lesson From Subjective Computing: Autonomous Self-Referentiality and Social Interaction as Conditions for Subjectivity.Patrick Grüneberg & Kenji Suzuki - 2013 - AISB Proceedings 2012:18-28.
    In this paper, we model a relational notion of subjectivity by means of two experiments in subjective computing. The goal is to determine to what extent a cognitive and social robot can be regarded to act subjectively. The system was implemented as a reinforcement learning agent with a coaching function. To analyze the robotic agent we used the method of levels of abstraction in order to analyze the agent at four levels of abstraction. At one level the agent (...)
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  15. Learning Networks and Connective Knowledge.Stephen Downes - 2010 - In Harrison Hao Yang & Steve Chi-Yin Yuen (eds.), Collective Intelligence and E-Learning 2.0: Implications of Web-Based Communities and Networking. IGI Global.
    The purpose of this chapter is to outline some of the thinking behind new e-learning technology, including e-portfolios and personal learning environments. Part of this thinking is centered around the theory of connectivism, which asserts that knowledge - and therefore the learning of knowledge - is distributive, that is, not located in any given place (and therefore not 'transferred' or 'transacted' per se) but rather consists of the network of connections formed from experience and interactions with a (...)
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  16. Banana Classification Using Deep Learning.Ahmed F. Al-Daour, Mohammed O. Al-Shawwa & Samy S. Abu-Naser - 2020 - International Journal of Academic Information Systems Research (IJAISR) 3 (12):6-11.
    Abstract: Banana, fruit of the genus Musa, of the family Musaceae, one of the most important fruit crops of the world. The banana is grown in the tropics, and, though it is most widely consumed in those regions, it is valued worldwide for its flavour, nutritional value, and availability throughout the year. Cavendish, or dessert, bananas are most commonly eaten fresh, though they may be fried or mashed and chilled in pies or puddings. They may also be used to flavour (...)
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  17. Type of Grapefruit Classification Using Deep Learning.Mohammed M. Abu-Saqer, Samy S. Abu-Naser & Mohammed O. Al-Shawwa - 2020 - International Journal of Academic Information Systems Research (IJAISR) 4 (1):1-5.
    Fruit has been recognized as a good source of vitamins and minerals, and for their role in preventing vitamin C and vitamin A deficiencies. People who eat fruit as part of an overall healthy diet generally have a reduced risk of chronic diseases. Fruit are important sources of many nutrients, including potassium, fiber, vitamin C and folate (folic acid). One of important types of fruit is Grapefruit . Grapefruit is a tropical citrus fruit known its sweet and somewhat sour taste. (...)
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  18. Type of Tomato Classification Using Deep Learning.Mahmoud A. Alajrami & Samy S. Abu-Naser - 2020 - International Journal of Academic Pedagogical Research (IJAPR) 3 (12):21-25.
    Abstract: Tomatoes are part of the major crops in food security. Tomatoes are plants grown in temperate and hot regions of South American origin from Peru, and then spread to most countries of the world. Tomatoes contain a lot of vitamin C and mineral salts, and are recommended for people with constipation, diabetes and patients with heart and body diseases. Studies and scientific studies have proven the importance of eating tomato juice in reducing the activity of platelets in diabetics, which (...)
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  19. Mango Classification Using Deep Learning.Alaa Soliman Abu Mettleq, Ibtesam M. Dheir, Abeer A. Elsharif & Samy S. Abu-Naser - 2020 - International Journal of Academic Engineering Research (IJAER) 3 (12):22-29.
    Abstract: In worldwide, there are several hundred cultivars of mango. Depending on the cultivar, mango fruit varies in size, shape, sweetness, skin color, and flesh color which may be pale yellow, gold, or orange. Where there are more than 15 types of manga. In this paper, two types Mango classification approach is presented with a dataset that contains approximately 1200 images. Convolutional Neural Network (CNN) algorithms, a deep learning technique extensively applied to image recognition was used, for this task. (...)
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  20. Carl R. Rogers ve Öğrenme özgürlüğü: Etkili bir öğrenme ortamının mimarı olarak öğretmen ve öğretmen tutumları [Carl R. Rogers and freedom to learn: Teachers as the architects of an effective learning environment, and teachers' attitudes].Duygu Dincer - 2019 - Uluslararası Türkçe Eğitim Kültür Edebiyat Dergisi 4 (8): 2341-2358.
    Carl R. Rogers, the founder of client-centered therapy, contributed to the development of self-reliant learning in education. He applied such concepts of client-centered therapy as realness, prizing, acceptance, trust, and empathy to educational area, and called attention the importance of the authentic relationship between teacher and student with such books as Freedom to Learn, Becoming A Person, and A Way of Being. Besides, he also focused on teachers‟ attitudes in classrooms in his works. His views still continue to influence (...)
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  21. Lemon Classification Using Deep Learning.Jawad Yousif AlZamily & Samy Salim Abu Naser - 2020 - International Journal of Academic Pedagogical Research (IJAPR) 3 (12):16-20.
    Abstract : Background: Vegetable agriculture is very important to human continued existence and remains a key driver of many economies worldwide, especially in underdeveloped and developing economies. Objectives: There is an increasing demand for food and cash crops, due to the increasing in world population and the challenges enforced by climate modifications, there is an urgent need to increase plant production while reducing costs. Methods: In this paper, Lemon classification approach is presented with a dataset that contains approximately 2,000 images (...)
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  22. Avocado Classification Using Deep Learning.Mohammed N. Abu Alqumboz & Samy S. Abu-Naser - 2020 - International Journal of Academic Engineering Research (IJAER) 3 (12):30-34.
    Avocado is the fruit of the avocado tree, scientifically known as Persia Americana. This fruit is prized for its high nutrient value and is added to various dishes due to its good flavor and rich texture. It is the main ingredient in guacamole. These days, the avocado has become an incredibly popular food among health-conscious individuals. It’s often referred to as a superfood, which is not surprising given its health properties. Using a public dataset of 1,234 images of Avocado collected (...)
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  23. Classification of Apple Fruits by Deep Learning.Mohammed O. Al-Shawwa & Samy S. Abu-Naser - 2020 - International Journal of Academic Engineering Research (IJAER) 3 (12):1-7.
    Abstract: Apple is a plant species that follows the apple genus, which is a fruit because it contains seeds of the pink family. It is one of the most fruit trees in terms of agriculture. The apple tree is small in length from 3 to 12 meters. Several recent studies have shown many health benefits of apples. It helps with the strengthening of the brain, heart, and stomach. It is used in the treatment of joint pain and limberness. It is (...)
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  24. Analyzing Types of Cherry Using Deep Learning.Izzeddin A. Alshawwa, Hosni Qasim El-Mashharawi, Mohammed Elkahlout, Mohammed O. Al-Shawwa & Samy S. Abu-Naser - 2020 - International Journal of Academic Engineering Research (IJAER) 4 (1):1-5.
    A cherry is the fruit of many plants of the genus Prunus, and is a fleshy drupe (stone fruit), Michigan's Northwest Lower Peninsula is the largest producer of tart cherries in the United States. In fact, grow 75% of the country's variety of mighty Montmorency cherries. We use these Ruby Red Morsels of Joy in over 200 cherry products like Salsas, Chocolate Covered Cherries, Cherry Nut Mixes, and much more. Cherry fruits are rich in vitamins and minerals, and it is (...)
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  25. Understanding From Machine Learning Models.Emily Sullivan - forthcoming - British Journal for the Philosophy of Science:axz035.
    Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding (...)
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  26. The Epistemic Significance of Perceptual Learning.Elijah Chudnoff - 2018 - Inquiry: An Interdisciplinary Journal of Philosophy 61 (5-6):520-542.
    First impressions suggest the following contrast between perception and memory: perception generates new beliefs and reasons, justification, or evidence for those beliefs; memory preserves old beliefs and reasons, justification, or evidence for those beliefs. In this paper, I argue that reflection on perceptual learning gives us reason to adopt an alternative picture on which perception plays both generative and preservative epistemic roles.
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  27. Peach Type Classification Using Deep Learning.Mohammed I. El-Kahlout & Samy S. Abu-Naser - 2020 - International Journal of Academic Engineering Research (IJAER) 3 (12):35-40.
    Abstract: Peach, (Prunus persica), fruit tree of the rose family (Rosaceae), grown throughout the warmer temperate regions of both the Northern and Southern hemispheres. Peaches are widely eaten fresh and are also baked in pies and cobblers; canned peaches are a staple commodity in many regions. Yellow-fleshed varieties are especially rich in vitamin A. Peach trees are relatively short-lived as compared with some other fruit trees. In some regions orchards are replanted after 8 to 10 years, while in others trees (...)
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  28.  36
    Bayesian Learning Models of Pain: A Call to Action.Abby Tabor & Christopher Burr - 2019 - Current Opinion in Behavioral Sciences 26:54-61.
    Learning is fundamentally about action, enabling the successful navigation of a changing and uncertain environment. The experience of pain is central to this process, indicating the need for a change in action so as to mitigate potential threat to bodily integrity. This review considers the application of Bayesian models of learning in pain that inherently accommodate uncertainty and action, which, we shall propose are essential in understanding learning in both acute and persistent cases of pain.
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  29. Cognitive Penetration, Perceptual Learning and Neural Plasticity.Ariel S. Cecchi - 2014 - Dialectica 68 (1):63-95.
    Cognitive penetration of perception, broadly understood, is the influence that the cognitive system has on a perceptual system. The paper shows a form of cognitive penetration in the visual system which I call ‘architectural’. Architectural cognitive penetration is the process whereby the behaviour or the structure of the perceptual system is influenced by the cognitive system, which consequently may have an impact on the content of the perceptual experience. I scrutinize a study in perceptual learning that provides empirical evidence (...)
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  30. Perceptual Learning and the Contents of Perception.Kevin Connolly - 2014 - Erkenntnis 79 (6):1407-1418.
    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 (...)
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  31. E-Learning Strategies in Developing Research Performance Efficiency: Higher Education Institutions.Samia A. M. Abdalmenem, Samer M. Arqawi, Youssef M. Abu Amuna, Samy S. Abu Naser & Mazen J. Al Shobaki - 2019 - International Journal of Academic Pedagogical Research (IJAPR) 3 (9):8-19.
    The study aimed to identify E- Learning strategies and their relation to the efficiency of research performance in foreign and Palestinian universities (University of Ottawa, Munster, Suez Canal, Al-Azhar, Islamic, Al-Aqsa). The analytical descriptive approach was used for this purpose, and relying on the questionnaire as a main tool for data collection. The study society is from the senior management, where the number of senior management in the universities in question is 206. The random stratified sample was selected and (...)
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  32. Machine Learning and Irresponsible Inference: Morally Assessing the Training Data for Image Recognition Systems.Owen King - 2019 - In Matteo Vincenzo D'Alfonso & Don Berkich (eds.), On the Cognitive, Ethical, and Scientific Dimensions of Artificial Intelligence. Springer Verlag. pp. 265-282.
    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 (...)
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  33. 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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  34.  22
    Autonomy and Machine Learning as Risk Factors at the Interface of Nuclear Weapons, Computers and People.S. M. Amadae & Shahar Avin - 2019 - In Vincent Boulanin (ed.), The Impact of Artificial Intelligence on Strategic Stability and Nuclear Risk: Euro-Atlantic Perspectives. Stockholm, Sweden: pp. 105-118.
    This article assesses how autonomy and machine learning impact the existential risk of nuclear war. It situates the problem of cyber security, which proceeds by stealth, within the larger context of nuclear deterrence, which is effective when it functions with transparency and credibility. Cyber vulnerabilities poses new weaknesses to the strategic stability provided by nuclear deterrence. This article offers best practices for the use of computer and information technologies integrated into nuclear weapons systems. Focusing on nuclear command and control, (...)
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  35. Learning in Lithic Landscapes: A Reconsideration of the Hominid “Toolmaking” Niche.Peter Hiscock - 2014 - Biological Theory 9 (1):27-41.
    This article reconsiders the early hominid ‘‘lithic niche’’ by examining the social implications of stone artifact making. I reject the idea that making tools for use is an adequate explanation of the elaborate artifact forms of the Lower Palaeolithic, or a sufficient cause for long-term trends in hominid technology. I then advance an alternative mechanism founded on the claim that competency in making stone artifacts requires extended learning, and that excellence in artifact making is attained only by highly skilled (...)
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  36. Instrumental Rationality in Psychopathy: Implications From Learning Tasks.Marko Jurjako & Luca Malatesti - 2016 - Philosophical Psychology 29 (5):717-731.
    The issue whether psychopathic offenders are practically rational has attracted philosophical attention. The problem is relevant in theoretical discussions on moral psychology and in those concerning the appropriate social response to the crimes of these individuals. We argue that classical and current experiments concerning the instrumental learning in psychopaths cannot directly support the conclusion that they have impaired instrumental rationality, construed as the ability for transferring the motivation by means-ends reasoning. In fact, we defend the different claim that these (...)
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  37.  21
    The Cicada Catcher: Learning for Life.Karyn L. Lai - 2019 - In Karyn L. Lai & Wai-wai Chiu (eds.), Skill and Mastery Philosophical Stories from the Zhuangzi. UK: Rowman and Littlefield International. pp. 143 - 162.
    The cicada catcher focuses as much on technique as he does on outcomes. In response to Confucius’ question, he articulates in detail the learning he has undertaken to develop techniques at each level of competence. This chapter explains the connection between the cicada catcher’s development of technique and his orientation toward outcomes. It uses details in this story to contribute to recent discussions in epistemology on the cultivation of technique.
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  38. Cooperative Learning, Critical Thinking and Character. Techniques to Cultivate Ethical Deliberation.Nancy Matchett - 2009 - Public Integrity 12 (1).
    Effective ethics teaching and training must cultivate both the critical thinking skills and the character traits needed to deliberate effectively about ethical issues in personal and professional life. After highlighting some cognitive and motivational obstacles that stand in the way of this task, the article draws on educational research and the author's experience to demonstrate how cooperative learning techniques can be used to overcome them.
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  39.  71
    Improving Research Performance Efficiency in Palestinian Universities Using E-Learning Strategies.Samia A. M. Abdalmenem, Rasha O. Owda, Maram O. Owda, Samy S. Abu-Naser, Mazen J. Al Shobaki & Youssef M. Abu Amuna - 2019 - International Journal of Academic Information Systems Research (IJAISR) 3 (8):20-27.
    The study aimed to identify e-learning strategies and their relation to increasing the efficiency of research performance in foreign and Palestinian universities (University of Ottawa, Munster, Suez Canal, Al-Azhar, Islamic, Al-Aqsa). The analytical descriptive approach was used for this purpose, and relying on the questionnaire as a main tool for data collection. The study society consists of senior management in Palestinian universities, where the number of senior management personnel in the universities was 206. The random stratified sample was selected, (...)
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  40. Enabling Change: Transformative and Transgressive Learning in Feminist Ethics and Epistemology.David Concepcion & Juli Thorson Elin - 2009 - Teaching Philosophy 32 (2):177-198.
    Through examples of embodied and learning -centered pedagogy, we discuss transformative learning of transgressive topics. We begin with a taxonomy of types of learning our students undergo as they resolve inconsistencies among their pre-existing beliefs and the material they confront in our course on feminist ethics and epistemology. We then discuss ways to help students maximize their learning while confronting internal inconsistencies. While we focus on feminist topics, our approach is broad enough to be relevant to (...)
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  41.  97
    Multisensory Perception as an Associative Learning Process.Kevin Connolly - 2014 - Frontiers in Psychology 5:1095.
    Suppose that you are at a live jazz show. The drummer begins a solo. You see the cymbal jolt and you hear the clang. But in addition seeing the cymbal jolt and hearing the clang, you are also aware that the jolt and the clang are part of the same event. Casey O’Callaghan (forthcoming) calls this awareness “intermodal feature binding awareness.” Psychologists have long assumed that multimodal perceptions such as this one are the result of a subpersonal feature binding mechanism (...)
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  42. Scientism, Philosophy and Brain-Based Learning.Gregory M. Nixon - 2013 - Northwest Journal of Teacher Education 11 (1):113-144.
    [This is an edited and improved version of "You Are Not Your Brain: Against 'Teaching to the Brain'" previously published in *Review of Higher Education and Self-Learning* 5(15), Summer 2012.] Since educators are always looking for ways to improve their practice, and since empirical science is now accepted in our worldview as the final arbiter of truth, it is no surprise they have been lured toward cognitive neuroscience in hopes that discovering how the brain learns will provide a nutshell (...)
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  43.  36
    Grape Type Classification Using Deep Learning.Hosni Qasim El-Mashharawi, Samy S. Abu-Naser, Izzeddin A. Alshawwa & Mohammed Elkahlout - 2020 - International Journal of Academic Engineering Research (IJAER) 3 (12):41-45.
    Abstract: A grape is a fruit, botanically a berry, of the deciduous woody vines of the flowering plant genus Vitis. it can be eaten fresh or they can be used for making jam, grape juice, jelly, grape seed extract, raisins, and grape seed oil. Grapes are a nonclimacteric type of fruit, generally occurring in clusters. Grapes are a type of fruit that grow in clusters of 15 to 300, and can be crimson, black, dark blue, yellow, green, orange, and pink. (...)
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  44. Wisdom of the Crowds Vs. Groupthink: Learning in Groups and in Isolation.Conor Mayo-Wilson, Kevin Zollman & David Danks - 2013 - International Journal of Game Theory 42 (3):695-723.
    We evaluate the asymptotic performance of boundedly-rational strategies in multi-armed bandit problems, where performance is measured in terms of the tendency (in the limit) to play optimal actions in either (i) isolation or (ii) networks of other learners. We show that, for many strategies commonly employed in economics, psychology, and machine learning, performance in isolation and performance in networks are essentially unrelated. Our results suggest that the appropriateness of various, common boundedly-rational strategies depends crucially upon the social context (if (...)
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  45. 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 human (...)
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  46. Sensory Substitution and Perceptual Learning.Kevin Connolly - forthcoming - In Fiona Macpherson (ed.), Sensory Substitution and Augmentation. Oxford University Press.
    When a user integrates a sensory substitution device into her life, the process involves perceptual learning, that is, ‘relatively long-lasting changes to an organism’s perceptual system that improve its ability to respond to its environment’ (Goldstone 1998: 585). In this paper, I explore ways in which the extensive literature on perceptual learning can be applied to help improve sensory substitution devices. I then use these findings to answer a philosophical question. Much of the philosophical debate surrounding sensory substitution (...)
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  47. A Unified Account of General Learning Mechanisms and Theory‐of‐Mind Development.Theodore Bach - 2014 - Mind and Language 29 (3):351-381.
    Modularity theorists have challenged that there are, or could be, general learning mechanisms that explain theory-of-mind development. In response, supporters of the ‘scientific theory-theory’ account of theory-of-mind development have appealed to children's use of auxiliary hypotheses and probabilistic causal modeling. This article argues that these general learning mechanisms are not sufficient to meet the modularist's challenge. The article then explores an alternative domain-general learning mechanism by proposing that children grasp the concept belief through the progressive alignment of (...)
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  48. Learning, Evolvability and Exploratory Behaviour: Extending the Evolutionary Reach of Learning.Rachael L. Brown - 2013 - Biology and Philosophy 28 (6):933-955.
    Traditional accounts of the role of learning in evolution have concentrated upon its capacity as a source of fitness to individuals. In this paper I use a case study from invasive species biology—the role of conditioned taste aversion in mitigating the impact of cane toads on the native species of Northern Australia—to highlight a role for learning beyond this—as a source of evolvability to populations. This has two benefits. First, it highlights an otherwise under-appreciated role for learning (...)
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  49. On Ge Wu: Recovering the Way of the Great Learning.Huaiyu Wang - 2007 - Philosophy East and West 57 (2):204 - 226.
    By rethinking the meaning of a central idiom in the Great Learning, this essay intends to open up a new horizon for the hermeneutics of early Confucian thinking, which has little to do with metaphysics. Through a careful etymological study of ge wu and a dialogue between the Great Learning and Heidegger's phenomenology of human affection, I demonstrate the critical position of the human heart in early Chinese thinking. This new interpretation of early Confucian moral teachings also recovers (...)
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  50. Learning and Selection Processes.Marc Artiga - 2010 - Theoria : An International Journal for Theory, History and Fundations of Science 25 (2):197-209.
    In this paper I defend a teleological explanation of normativity, i. e., I argue that what an organism is supposed to do is determined by its etiological function. In particular, I present a teleological account of the normativity that arises in learning processes, and I defend it from some objections.
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