Search results for 'Analogical Learning' (try it on Scholar)

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  1. Daniel Corral & Matt Jones (2014). The Effects of Relational Structure on Analogical Learning. Cognition 132 (3):280-300.score: 150.0
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  2. Patricia A. Alexander, C. Stephen White & Martha Daugherty (1997). Analogical Reasoning and Early Mathematics Learning. In Lyn D. English (ed.), Mathematical Reasoning: Analogies, Metaphors, and Images. L. Erlbaum Associates. 117--147.score: 122.0
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  3. Theodore Bach (2012). Analogical Cognition: Applications in Epistemology and the Philosophy of Mind and Language. Philosophy Compass 7 (5):348-360.score: 102.0
    Analogical cognition refers to the ability to detect, process, and learn from relational similarities. The study of analogical and similarity cognition is widely considered one of the ‘success stories’ of cognitive science, exhibiting convergence across many disciplines on foundational questions. Given the centrality of analogy to mind and knowledge, it would benefit philosophers investigating topics in epistemology and the philosophies of mind and language to become familiar with empirical models of analogical cognition. The goal of this essay (...)
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  4. Alexander Renkl (2014). Toward an Instructionally Oriented Theory of Example‐Based Learning. Cognitive Science 38 (1):1-37.score: 102.0
    Learning from examples is a very effective means of initial cognitive skill acquisition. There is an enormous body of research on the specifics of this learning method. This article presents an instructionally oriented theory of example-based learning that integrates theoretical assumptions and findings from three research areas: learning from worked examples, observational learning, and analogical reasoning. This theory has descriptive and prescriptive elements. The descriptive subtheory deals with (a) the relevance and effectiveness of examples, (...)
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  5. Dedre Gentner (2010). Bootstrapping the Mind: Analogical Processes and Symbol Systems. Cognitive Science 34 (5):752-775.score: 96.0
    Human cognition is striking in its brilliance and its adaptability. How do we get that way? How do we move from the nearly helpless state of infants to the cognitive proficiency that characterizes adults? In this paper I argue, first, that analogical ability is the key factor in our prodigious capacity, and, second, that possession of a symbol system is crucial to the full expression of analogical ability.
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  6. Adrian Ratkic (2013). Images of Reflection: On the Meanings of the Word Reflection in Different Learning Contexts. [REVIEW] AI and Society 28 (3):339-349.score: 66.0
    Reflection is today a watchword in many learning contexts. Experience is said to be transformed to knowledge when we reflect on it, university students are expected to acquire the ability to reflect critically, and we want practitioners to be reflective practitioners in order to improve their professional practice. If we consider what people mean when they talk about reflection in practice, we will discover that they often mean different things. Moreover, their conceptions of reflection are guided by images rather (...)
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  7. Stella Christie & Dedre Gentner (2014). Language Helps Children Succeed on a Classic Analogy Task. Cognitive Science 38 (2):383-397.score: 62.0
    Adult humans show exceptional relational ability relative to other species. In this research, we trace the development of this ability in young children. We used a task widely used in comparative research—the relational match-to-sample task, which requires participants to notice and match the identity relation: for example, AA should match BB instead of CD. Despite the simplicity of this relation, children under 4 years of age failed to pass this test (Experiment 1), and their performance did not improve even with (...)
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  8. Theodore Bach (2011). Structure-Mapping: Directions From Simulation to Theory. Philosophical Psychology 24 (1):23-51.score: 60.0
    The theory of mind debate has reached a “hybrid consensus” concerning the status of theory-theory and simulation-theory. Extant hybrid models either specify co-dependency and implementation relations, or distribute mentalizing tasks according to folk-psychological categories. By relying on a non-developmental framework these models fail to capture the central connection between simulation and theory. I propose a “dynamic” hybrid that is informed by recent work on the nature of similarity cognition. I claim that Gentner’s model of structure-mapping allows us to understand simulation (...)
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  9. Mark Colyvan & Lev R. Ginzburg, Analogical Thinking in Ecology.score: 54.0
    We consider several ways in which a good understanding of modern techniques and principles in physics can elucidate ecology. We focus on analogical reasoning between these two branches of science. This style of reasoning requires an understanding of both sciences and an appreciation of the similarities and points of contact between the two. In the current ecological literature on the relationship between ecology and physics, there has been some misunderstanding about the nature of modern physics and its methods. Physics (...)
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  10. Peter F. Dominey (1997). Reducing Problem Complexity by Analogical Transfer. Behavioral and Brain Sciences 20 (1):71-72.score: 54.0
    Analogical transfer in sequence learning is presented as an example of how the type-2 problem of learning an unbounded number of isomorphic sequences is reduced to the type-1 problem of learning a small finite set of sequences. The commentary illustrates how the difficult problem of appropriate analogical filter creation and selection is addressed while avoiding the trap of strong nativism, and it provides theoretical and experimental evidence for the existence of dissociable mechanisms for type-1 (...) and type-2 recoding. (shrink)
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  11. Marc T. Tomlinson & Bradley C. Love (2008). Monkey See, Monkey Do: Learning Relations Through Concrete Examples. Behavioral and Brain Sciences 31 (2):150-151.score: 54.0
    Penn et al. argue that the complexity of relational learning is beyond animals. We discuss a model that demonstrates relational learning need not involve complex processes. Novel stimuli are compared to previous experiences stored in memory. As learning shifts attention from featural to relational cues, the comparison process becomes more analogical in nature, successfully accounting for performance across species and development.
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  12. Keith J. Holyoak & John E. Hummel (2008). No Way to Start a Space Program: Associationism as a Launch Pad for Analogical Reasoning. Behavioral and Brain Sciences 31 (4):388-389.score: 54.0
    Humans, including preschool children, exhibit role-based relational reasoning, of which analogical reasoning is a canonical example. The connectionist model proposed in the target article is only capable of conditional paired-associate learning.
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  13. Thomas P. Reber & Katharina Henke (2011). Rapid Formation and Flexible Expression of Memories of Subliminal Word Pairs. Frontiers in Psychology 2.score: 48.0
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  14. Pavlos Kollias & James L. McClelland (2013). Context, Cortex, and Associations: A Connectionist Developmental Approach to Verbal Analogies. Frontiers in Psychology 4.score: 46.0
    We present a PDP model of binary choice verbal analogy problems (A:B as C:[D1|D2], where D1 and D2 represent choice alternatives). We train a recurrent neural network in item-relation- item triples and use this network to test performance on analogy questions. Without training on analogy problems per se, the model explains the developmental shift from associative to relational responding as an emergent consequence of learning upon the environment’s statistics. Such learning allows gradual, item-specific acquisition of relational knowledge to (...)
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  15. Theodore Bach (2014). A Unified Account of General Learning Mechanisms and Theory‐of‐Mind Development. Mind and Language 29 (3):351-381.score: 44.0
    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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  16. Rens Bod (2009). From Exemplar to Grammar: A Probabilistic Analogy‐Based Model of Language Learning. Cognitive Science 33 (5):752-793.score: 44.0
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  17. Richard Schenk (2010). Analogy as the "Discrimen Naturae Et Gratiae" : Thomism and Ecumenical Learning. In Thomas Joseph White (ed.), The Analogy of Being: Invention of the Antichrist or the Wisdom of God? W.B. Eerdmans Pub. Co..score: 42.0
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  18. Johan M. Koedijker, Jamie M. Poolton, Jonathan P. Maxwell, Raôul R. D. Oudejans, Peter J. Beek & Rich S. W. Masters (2011). Attention and Time Constraints in Perceptual-Motor Learning and Performance: Instruction, Analogy, and Skill Level. Consciousness and Cognition 20 (2):245-256.score: 40.0
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  19. Charles Weijer, Learning From the Dutch: Physician-Assisted Death, Slippery Slopes and the Nazi Analogy.score: 40.0
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  20. Herbert Johnston (1962). The Analogy of Learning. New Scholasticism 36 (1):111-113.score: 40.0
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  21. Michael Ramscar, Daniel Yarlett, Shimon Edelman, Nathan Intrator, Gergely Csibra, Szilvia Bıró, Orsolya Koós, György Gergely, Holk Cruse & Michael D. Lee (2003). Regular Articles Learning to Divide the Labor: An Account of Deficits in Light and Heavy Verb Production 1 Jean K. Gordon, Gary S. Dell Semantic Grounding in Models of Analogy: An Environmental Approach 41. Cognitive Science 27:945-948.score: 40.0
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  22. Alanson Van Fleet (1979). Learning to Teach: The Cultural Transmission Analogy. Journal of Thought 14 (4):281-90.score: 40.0
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  23. Callan Bentley (2008). Using Analogies to Assess Student Learning. Inquiry 13 (1):26-35.score: 40.0
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  24. Mark H. Burstein (1988). Incremental Learning From Multiple Analogies. In Armand Prieditis (ed.), Analogica. Morgan Kaufmann Publishers. 37--62.score: 40.0
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  25. L. A. A. Doumas, R. G. Morrison & L. E. Richland (2009). The Development of Analogy: Task Learning and Individual Differences. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.score: 40.0
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  26. Reinders Duit (1991). On the Role of Analogies and Metaphors in Learning Science. Science Education 75 (6):649-672.score: 40.0
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  27. Clifford Kossel (1961). The Analogy of Learning. Modern Schoolman 39 (1):78-79.score: 40.0
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  28. D. Needham & I. Begg (1990). Spontaneous Analogical Transfer is Common If Subjects Learn by Doing. Bulletin of the Psychonomic Society 28 (6):504-504.score: 40.0
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  29. Gordon Pask (1963). The Use of Analogy and Parable in Cybernetics with Emphasis Upon Analogies for Learning and Creativity. Dialectica 17 (2‐3):167-203.score: 40.0
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  30. Arthur S. Reber & Rhianon Allen (1978). Analogic and Abstraction Strategies in Synthetic Grammar Learning: A Functionalist Interpretation. Cognition 6 (3):189-221.score: 40.0
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  31. Usha Goswami (2008). Analogy and the Brain: A New Perspective on Relational Primacy. Behavioral and Brain Sciences 31 (4):387-388.score: 34.0
    Leech et al.'s demonstration that analogical reasoning can be an emergent property of low-level incremental learning processes is critical for analogical theory. Along with insights into neural learning based on the salience of dynamic spatio-temporal structure, and the neural priming mechanism of repetition suppression, it establishes relational primacy as a plausible theoretical description of how brains make analogies.
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  32. Rafael V. Borges, Artur S. D'Avila Garcez & Luis C. Lamb (2008). A Neural-Symbolic Perspective on Analogy. Behavioral and Brain Sciences 31 (4):379-380.score: 34.0
    The target article criticises neural-symbolic systems as inadequate for analogical reasoning and proposes a model of analogy as transformation (i.e., learning). We accept the importance of learning, but we argue that, instead of conflicting, integrated reasoning and learning would model analogy much more adequately. In this new perspective, modern neural-symbolic systems become the natural candidates for modelling analogy.
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  33. Adam Albright & Bruce Hayes (2003). Rules Vs. Analogy in English Past Tenses: A Computational/Experimental Study. Cognition 90 (2):119-161.score: 34.0
    Are morphological patterns learned in the form of rules? Some models deny this, attributing all morphology to analogical mechanisms. The dual mechanism model (Pinker, S., & Prince, A. (1998). On language and connectionism: analysis of a parallel distributed processing model of language acquisition. Cognition, 28, 73-193) posits that speakers do internalize rules, but that these rules are few and cover only regular processes; the remaining patterns are attributed to analogy. This article advocates a third approach, which uses multiple stochastic (...)
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  34. Stephen Downes (2010). Learning Networks and Connective Knowledge. 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.score: 27.0
    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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  35. Lothar Philipps & Giovanni Sartor (1999). Introduction: From Legal Theories to Neural Networks and Fuzzy Reasoning. [REVIEW] Artificial Intelligence and Law 7 (2-3):115-128.score: 26.0
    Computational approaches to the law have frequently been characterized as being formalistic implementations of the syllogistic model of legal cognition: using insufficient or contradictory data, making analogies, learning through examples and experiences, applying vague and imprecise standards. We argue that, on the contrary, studies on neural networks and fuzzy reasoning show how AI & law research can go beyond syllogism, and, in doing that, can provide substantial contributions to the law.
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  36. Arthur S. Reber (1993). Implicit Learning and Tacit Knowledge: An Essay on the Cognitive Unconscious. Oxford University Press.score: 24.0
    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 (...)
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  37. Christopher Winch (1998). The Philosophy of Human Learning. Routledge.score: 24.0
    Christopher Winch launches a vigorous Wittgensteinian attack on both the "romantic" Rousseauian and the "scientific" cognitivist traditions in learning theory. These two schools, he argues, are more closely related than is commonly realized.
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  38. Gregory M. Nixon (2013). Scientism, Philosophy and Brain-Based Learning. Northwest Journal of Teacher Education 11 (2):113-144.score: 24.0
    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 explanation for student learning in general. I argue that identifying the person with the brain is scientism (not science), that the brain is not the person, and that it is (...)
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  39. Knud Illeris (ed.) (2009). Contemporary Theories of Learning: Learning Theorists -- In Their Own Words. Routledge.score: 24.0
    In this definitive collection of today's most influential learning theorists, sixteen world-renowned experts present their understanding of what learning is and ...
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  40. David R. Shanks & M. F. St John (1994). Characteristics of Dissociable Human Learning Systems. Behavioral and Brain Sciences 17 (3):367-447.score: 24.0
    A number of ways of taxonomizing human learning have been proposed. We examine the evidence for one such proposal, namely, that there exist independent explicit and implicit learning systems. This combines two further distinctions, (1) between learning that takes place with versus without concurrent awareness, and (2) between learning that involves the encoding of instances (or fragments) versus the induction of abstract rules or hypotheses. Implicit learning is assumed to involve unconscious rule learning. We (...)
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  41. Richard Edwards, Gert Biesta & Mary Thorpe (eds.) (2009). Rethinking Contexts for Learning and Teaching. Routledge.score: 24.0
    It specifically addressesWhat constitutes a context for learning?How do we engage the full resources of learners for learning?What are the relationships between ...
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  42. Michael Cholbi (2007). Intentional Learning as a Model for Philosophical Pedagogy. Teaching Philosophy 30 (1):35-58.score: 24.0
    The achievement of intentional learning is a powerful paradigm for the objectives and methods of the teaching of philosophy. This paradigm sees the objectives and methods of such teaching as based not simply on the mastery of content, but as rooted in attempts to shape the various affective and cognitive factors that influence students’ learning efforts. The goal of such pedagogy is to foster an intentional learning orientation, one characterized by self-awareness, active monitoring of the learning (...)
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  43. Kevin Connolly, Dylan Bianchi, Craig French, Lana Kuhle & Andy MacGregor, Report on the Network for Sensory Research/University of York Perceptual Learning Workshop.score: 24.0
    This report highlights and explores five questions that arose from the Network for Sensory Research workshop on perceptual learning and perceptual recognition at the University of York on March 19th and 20th, 2012: 1. What is perceptual learning? 2. Can perceptual experience be modified by reason? 3. How does perceptual learning alter perceptual phenomenology? 4. How does perceptual learning alter the contents of perception? 5. How is perceptual learning coordinated with action?
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  44. Kevin Connolly, John Donaldson, David M. Gray, Emily McWilliams, Sofia Ortiz-Hinojosa & David Suarez, Cognitive Penetration? (Network for Sensory Research Toronto Workshop on Perceptual Learning: Question Four).score: 24.0
    This is an excerpt from a report that highlights and explores five questions which arose from the workshop on perceptual learning and perceptual recognition at the University of Toronto, Mississauga on May 10th and 11th, 2012. This excerpt explores the question: What counts as cognitive penetration?
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  45. Kevin Connolly, Dylan Bianchi, Craig French, Lana Kuhle & Andy MacGregor, Perceptual Learning and Cognitive Penetration (Network for Sensory Research/University of York Perceptual Learning Workshop, Question Two).score: 24.0
    This is an excerpt of a report that highlights and explores five questions that arose from the Network for Sensory Research workshop on perceptual learning and perceptual recognition at the University of York in March, 2012. This portion of the report explores the question: Can perceptual experience be modified by reason?
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  46. Christopher D. Viger (2005). Learning to Think: A Response to the Language of Thought Argument for Innateness. Mind and Language 20 (3):313-25.score: 24.0
    Jerry Fodor's argument for an innate language of thought continues to be a hurdle for researchers arguing that natural languages provide us with richer conceptual systems than our innate cognitive resources. I argue that because the logical/formal terms of natural languages are given a usetheory of meaning, unlike predicates, logical/formal terms might be learned without a mediating internal representation. In that case, our innate representational system might have less logical structure than a natural language, making it possible that we augment (...)
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  47. Kevin Connolly, John Donaldson, David M. Gray, Emily McWilliams, Sofia Ortiz-Hinojosa & David Suarez, Report on the Network for Sensory Research Toronto Workshop on Perceptual Learning.score: 24.0
    This report highlights and explores five questions which arose from the workshop on perceptual learning and perceptual recognition at the University of Toronto, Mississauga on May 10th and 11th, 2012: 1. How should we demarcate perceptual learning from perceptual development? 2. What are the origins of multimodal associations? 3. Does our representation of time provide an amodal framework for multi-sensory integration? 4. What counts as cognitive penetration? 5. How can philosophers and psychologists most fruitfully collaborate?
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  48. Mary Hesse (1982). Comment on Kuhn's "Commensurability, Comparability, Communicability". PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1982:704 - 711.score: 24.0
    Kuhn's incommensurability thesis of 1962 still implies a very radical critique of standard theories of meaning. It is argued that incommensurability is sufficiently pervasive throughout the development of theories as to call in question standard linguistic palliatives, and that Kuhn's critique of extensionalist translation must be carried further into a theory of interpretation which not only depends on holistic meanings, but also explicitly addresses the ostensive and analogical processes of language learning. Such a theory is required (...)
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  49. Kenneth Reisman (2007). Is Culture Inherited Through Social Learning? Biological Theory 2 (3):300-306.score: 24.0
    In this article I challenge the widely held assumption that human culture is inherited by means of social learning. First, I address the distinction between “social” learning and “individual” learning. I argue that most cultural ideas are not acquired by one form of learning or the other, but from a hybrid of both. Second, I discuss how individual learning can interact with niche construction. I argue that these processes collectively provide a non-social route for learned (...)
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  50. Kevin Connolly, Dylan Bianchi, Craig French, Lana Kuhle & Andy MacGregor, Perceptual Learning (Network for Sensory Research/University of York Perceptual Learning Workshop, Question One).score: 24.0
    This is an excerpt of a report that highlights and explores five questions that arose from the Network for Sensory Research workshop on perceptual learning and perceptual recognition at the University of York in March, 2012. This portion of the report explores the question: What is perceptual learning?
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