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

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  1.  5
    Dedre Gentner, Susan C. Levine, Raedy Ping, Ashley Isaia, Sonica Dhillon, Claire Bradley & Garrett Honke (2016). Rapid Learning in a Children's Museum Via Analogical Comparison. Cognitive Science 40 (1):224-240.
    We tested whether analogical training could help children learn a key principle of elementary engineering—namely, the use of a diagonal brace to stabilize a structure. The context for this learning was a construction activity at the Chicago Children's Museum, in which children and their families build a model skyscraper together. The results indicate that even a single brief analogical comparison can confer insight. The results also reveal conditions that support analogical learning.
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  2.  3
    Daniel Corral & Matt Jones (2014). The Effects of Relational Structure on Analogical Learning. Cognition 132 (3):280-300.
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  3. 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.
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  4.  22
    Alexander Renkl (2014). Toward an Instructionally Oriented Theory of Example‐Based Learning. Cognitive Science 38 (1):1-37.
    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.  26
    Dedre Gentner (2010). Bootstrapping the Mind: Analogical Processes and Symbol Systems. Cognitive Science 34 (5):752-775.
    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.  78
    Theodore Bach (2012). Analogical Cognition: Applications in Epistemology and the Philosophy of Mind and Language. Philosophy Compass 7 (5):348-360.
    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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  7.  9
    Kenneth D. Forbus, Ronald W. Ferguson, Andrew Lovett & Dedre Gentner (2016). Extending SME to Handle Large‐Scale Cognitive Modeling. Cognitive Science 40 (7).
    Analogy and similarity are central phenomena in human cognition, involved in processes ranging from visual perception to conceptual change. To capture this centrality requires that a model of comparison must be able to integrate with other processes and handle the size and complexity of the representations required by the tasks being modeled. This paper describes extensions to Structure-Mapping Engine since its inception in 1986 that have increased its scope of operation. We first review the basic SME algorithm, describe psychological evidence (...)
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  8.  19
    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.
    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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  9.  63
    Theodore Bach (2011). Structure-Mapping: Directions From Simulation to Theory. Philosophical Psychology 24 (1):23-51.
    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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  10. Mark Colyvan & Lev R. Ginzburg, Analogical Thinking in Ecology.
    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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  11. William A. Rottschaefer (1983). Operant Learning and the Scientific and Philosophical Foundations of Behavior Therapy. Behaviorism 11 (2):155-161.
    The continuing and expanding successes of behavior therapy in the treatment of psychological problems raise important questions about their scientific and philosophical bases. In this paper I examine the claims of Edward Erwin that behaviorism cannot provide an adequate philosophical basis for behavior therapy, contemporary learning theories which exclude cognitive factors as causes of behavior cannot provide an adequate empirical basis for behavior therapy; and learning theories have played only a heuristic role in the development of behavior therapy. (...)
     
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  12.  4
    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.
    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.  15
    Peter F. Dominey (1997). Reducing Problem Complexity by Analogical Transfer. Behavioral and Brain Sciences 20 (1):71-72.
    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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  14. Robert E. Haskell (2009). The Access Paradox in Analogical Reasoning and Transfer: Whither Invariance? Journal of Mind and Behavior 30 (1):33.
    Despite the burgeoning research in recent years on what is called analogical reasoning and transfer, the problem of how similarity or invariant relations are fundamentally accessed is typically either unrecognized, or ignored in componential and computational analyses. The access problematic is not a new one, being outlined by the paradox found in Plato’s Meno. In order to understand the analogical-access problematic, it is suggested that the concepts of analogical relations including the lexical concept metaphor, isomorphic relation in (...)
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  15.  14
    Marc T. Tomlinson & Bradley C. Love (2008). Monkey See, Monkey Do: Learning Relations Through Concrete Examples. Behavioral and Brain Sciences 31 (2):150-151.
    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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  16.  32
    Stella Christie & Dedre Gentner (2014). Language Helps Children Succeed on a Classic Analogy Task. Cognitive Science 38 (2):383-397.
    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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  17. 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
    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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  18.  23
    Chase E. Thiel, Shane Connelly, Lauren Harkrider, Lynn D. Devenport, Zhanna Bagdasarov, James F. Johnson & Michael D. Mumford (2013). Case-Based Knowledge and Ethics Education: Improving Learning and Transfer Through Emotionally Rich Cases. Science and Engineering Ethics 19 (1):265-286.
    Case-based instruction is a stable feature of ethics education, however, little is known about the attributes of the cases that make them effective. Emotions are an inherent part of ethical decision-making and one source of information actively stored in case-based knowledge, making them an attribute of cases that likely facilitates case-based learning. Emotions also make cases more realistic, an essential component for effective case-based instruction. The purpose of this study was to investigate the influence of emotional case content, and (...)
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  19.  30
    John Earman (1992). Bayes or Bust? A Critical Examination of Bayesian Confirmation Theory. MIT Press.
    There is currently no viable alternative to the Bayesian analysis of scientific inference, yet the available versions of Bayesianism fail to do justice to several aspects of the testing and confirmation of scientific hypotheses. Bayes or Bust? provides the first balanced treatment of the complex set of issues involved in this nagging conundrum in the philosophy of science. Both Bayesians and anti-Bayesians will find a wealth of new insights on topics ranging from Bayes’s original paper to contemporary formal learning (...)
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  20. John Earman (1992). Bayes or Bust? Bradford.
    There is currently no viable alternative to the Bayesian analysis of scientific inference, yet the available versions of Bayesianism fail to do justice to several aspects of the testing and confirmation of scientific hypotheses. Bayes or Bust? provides the first balanced treatment of the complex set of issues involved in this nagging conundrum in the philosophy of science. Both Bayesians and anti-Bayesians will find a wealth of new insights on topics ranging from Bayes's original paper to contemporary formal learning (...)
     
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  21. Ariel S. Cecchi (2014). Cognitive Penetration, Perceptual Learning and Neural Plasticity. 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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  22.  33
    Chris J. Mitchell, Jan De Houwer & Peter F. Lovibond (2009). The Propositional Nature of Human Associative Learning. Behavioral and Brain Sciences 32 (2):183-198.
    The past 50 years have seen an accumulation of evidence suggesting that associative learning depends on high-level cognitive processes that give rise to propositional knowledge. Yet, many learning theorists maintain a belief in a learning mechanism in which links between mental representations are formed automatically. We characterize and highlight the differences between the propositional and link approaches, and review the relevant empirical evidence. We conclude that learning is the consequence of propositional reasoning processes that cooperate with (...)
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  23. Arthur S. Reber (1993). Implicit Learning and Tacit Knowledge: An Essay on the Cognitive Unconscious. Oxford University Press.
    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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  24.  57
    Ronald R. Sims & Edward L. Felton (2006). Designing and Delivering Business Ethics Teaching and Learning. Journal of Business Ethics 63 (3):297 - 312.
    The recent corporate scandals in the United States have caused a renewed interest and focus on teaching business ethics. Business schools and their faculties are reexamining the teaching of business ethics and are reassessing their responsibilities to produce honest and truthful managers who live lives of integrity and ethical accountability. The authors recognize that no agreement exists among business schools and their faculties regarding what should be the content and pedagogy of a course in business ethics. However, the authors hold (...)
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  25.  49
    Alison Gopnik & Laura Schulz (eds.) (2007). Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press.
    Understanding causal structure is a central task of human cognition. Causal learning underpins the development of our concepts and categories, our intuitive theories, and our capacities for planning, imagination and inference. During the last few years, there has been an interdisciplinary revolution in our understanding of learning and reasoning: Researchers in philosophy, psychology, and computation have discovered new mechanisms for learning the causal structure of the world. This new work provides a rigorous, formal basis for theory theories (...)
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  26. Hong Liu, Bin Hu & Philip Moore (2015). HCI Model with Learning Mechanism for Cooperative Design in Pervasive Computing Environment. 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 learning (...)
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  27.  28
    Kenny Smith, Andrew D. M. Smith & Richard A. Blythe (2011). Cross-Situational Learning: An Experimental Study of Word-Learning Mechanisms. Cognitive Science 35 (3):480-498.
    Cross-situational learning is a mechanism for learning the meaning of words across multiple exposures, despite exposure-by-exposure uncertainty as to the word's true meaning. We present experimental evidence showing that humans learn words effectively using cross-situational learning, even at high levels of referential uncertainty. Both overall success rates and the time taken to learn words are affected by the degree of referential uncertainty, with greater referential uncertainty leading to less reliable, slower learning. Words are also learned less (...)
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  28. David R. Shanks & M. F. St John (1994). Characteristics of Dissociable Human Learning Systems. Behavioral and Brain Sciences 17 (3):367-447.
    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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  29.  14
    Daniel Yurovsky, Chen Yu & Linda B. Smith (2013). Competitive Processes in Cross‐Situational Word Learning. Cognitive Science 37 (5):891-921.
    Cross-situational word learning, like any statistical learning problem, involves tracking the regularities in the environment. However, the information that learners pick up from these regularities is dependent on their learning mechanism. This article investigates the role of one type of mechanism in statistical word learning: competition. Competitive mechanisms would allow learners to find the signal in noisy input and would help to explain the speed with which learners succeed in statistical learning tasks. Because cross-situational word (...)
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  30. Jennifer Culbertson, Paul Smolensky & Colin Wilson (2013). Cognitive Biases, Linguistic Universals, and Constraint‐Based Grammar Learning. Topics in Cognitive Science 5 (3):392-424.
    According to classical arguments, language learning is both facilitated and constrained by cognitive biases. These biases are reflected in linguistic typology—the distribution of linguistic patterns across the world's languages—and can be probed with artificial grammar experiments on child and adult learners. Beginning with a widely successful approach to typology (Optimality Theory), and adapting techniques from computational approaches to statistical learning, we develop a Bayesian model of cognitive biases and show that it accounts for the detailed pattern of results (...)
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  31. Kevin Connolly (forthcoming). Sensory Substitution and Perceptual Learning. 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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  32.  84
    Peter Hiscock (2014). Learning in Lithic Landscapes: A Reconsideration of the Hominid “Toolmaking” Niche. 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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  33.  14
    Haley A. Vlach & Catherine M. Sandhofer (2014). Retrieval Dynamics and Retention in Cross‐Situational Statistical Word Learning. Cognitive Science 38 (4):757-774.
    Previous research on cross-situational word learning has demonstrated that learners are able to reduce ambiguity in mapping words to referents by tracking co-occurrence probabilities across learning events. In the current experiments, we examined whether learners are able to retain mappings over time. The results revealed that learners are able to retain mappings for up to 1 week later. However, there were interactions between the amount of retention and the different learning conditions. Interestingly, the strongest retention was associated (...)
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  34.  6
    Thomas L. Griffiths & Michael L. Kalish (2007). Language Evolution by Iterated Learning With Bayesian Agents. Cognitive Science 31 (3):441-480.
    Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that language themselves. We analyze the consequences of iterated learning for learning algorithms based on the principles of Bayesian inference, assuming that learners compute a posterior distribution over languages by combining a prior (representing their inductive biases) with the evidence provided by linguistic data. We show that when learners sample languages (...)
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  35.  28
    Afsaneh Fazly, Afra Alishahi & Suzanne Stevenson (2010). A Probabilistic Computational Model of Cross-Situational Word Learning. Cognitive Science 34 (6):1017-1063.
    Words are the essence of communication: They are the building blocks of any language. Learning the meaning of words is thus one of the most important aspects of language acquisition: Children must first learn words before they can combine them into complex utterances. Many theories have been developed to explain the impressive efficiency of young children in acquiring the vocabulary of their language, as well as the developmental patterns observed in the course of lexical acquisition. A major source of (...)
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  36.  11
    Michael Ramscar, Peter Hendrix, Cyrus Shaoul, Petar Milin & Harald Baayen (2014). The Myth of Cognitive Decline: Non‐Linear Dynamics of Lifelong Learning. Topics in Cognitive Science 6 (1):5-42.
    As adults age, their performance on many psychometric tests changes systematically, a finding that is widely taken to reveal that cognitive information-processing capacities decline across adulthood. Contrary to this, we suggest that older adults'; changing performance reflects memory search demands, which escalate as experience grows. A series of simulations show how the performance patterns observed across adulthood emerge naturally in learning models as they acquire knowledge. The simulations correctly identify greater variation in the cognitive performance of older adults, and (...)
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  37.  22
    Elisabeth Norman, Mark C. Price & Simon C. Duff (2006). Fringe Consciousness in Sequence Learning: The Influence of Individual Differences. Consciousness and Cognition 15 (4):723-760.
    We first describe how the concept of “fringe consciousness” can characterise gradations of consciousness between the extremes of implicit and explicit learning. We then show that the NEO-PI-R personality measure of openness to feelings, chosen to reflect the ability to introspect on fringe feelings, influences both learning and awareness in the serial reaction time task under conditions that have previously been associated with implicit learning . This provides empirical evidence for the proposed phenomenology and functional role of (...)
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  38.  59
    Andreas Matthias (2004). The Responsibility Gap: Ascribing Responsibility for the Actions of Learning Automata. [REVIEW] Ethics and Information Technology 6 (3):175-183.
    Traditionally, the manufacturer/operator of a machine is held (morally and legally) responsible for the consequences of its operation. Autonomous, learning machines, based on neural networks, genetic algorithms and agent architectures, create a new situation, where the manufacturer/operator of the machine is in principle not capable of predicting the future machine behaviour any more, and thus cannot be held morally responsible or liable for it. The society must decide between not using this kind of machine any more (which is not (...)
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  39.  30
    E. Alpay (2013). Student-Inspired Activities for the Teaching and Learning of Engineering Ethics. Science and Engineering Ethics 19 (4):1455-1468.
    Ethics teaching in engineering can be problematic because of student perceptions of its subjective, ambiguous and philosophical content. The use of discipline-specific case studies has helped to address such perceptions, as has practical decision making and problem solving approaches based on some ethical frameworks. However, a need exists for a wider range of creative methods in ethics education to help complement the variety of activities and learning experiences within the engineering curriculum. In this work, a novel approach is presented (...)
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  40. Kevin Connolly (2014). Perceptual Learning and the Contents of Perception. 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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  41.  12
    Terry Hyland (2011). Mindfulness and Learning: Celebrating the Affective Dimension of Education. Springer Verlag.
    The result is a one-dimensional, economistic and bleakly utilitarian conception of the educational task.In Mindfulness and Learning: Celebrating the Affective Dimension of Education, Terry Hyland advances the thesis that education stands in ...
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  42. Rachael L. Brown (2013). Learning, Evolvability and Exploratory Behaviour: Extending the Evolutionary Reach of Learning. 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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  43.  24
    John A. Weber (2007). Business Ethics Training: Insights From Learning Theory. [REVIEW] Journal of Business Ethics 70 (1):61 - 85.
    This paper explores research in educational psychology and learning theory in a search for insights to enhance business ethics training Useful educational principles uncovered are then applied to the development of an ethics training initiative for sales professionals. The paper concludes with suggestions for future research to help enrich business ethics training.
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  44. Eric Margolis & Stephen Laurence (2011). Learning Matters: The Role of Learning in Concept Acquisition. Mind and Language 26 (5):507-539.
    In LOT 2: The Language of Thought Revisited, Jerry Fodor argues that concept learning of any kind—even for complex concepts—is simply impossible. In order to avoid the conclusion that all concepts, primitive and complex, are innate, he argues that concept acquisition depends on purely noncognitive biological processes. In this paper, we show (1) that Fodor fails to establish that concept learning is impossible, (2) that his own biological account of concept acquisition is unworkable, and (3) that there are (...)
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  45.  24
    Joanne Arciuli & Ian C. Simpson (2012). Statistical Learning Is Related to Reading Ability in Children and Adults. Cognitive Science 36 (2):286-304.
    There is little empirical evidence showing a direct link between a capacity for statistical learning (SL) and proficiency with natural language. Moreover, discussion of the role of SL in language acquisition has seldom focused on literacy development. Our study addressed these issues by investigating the relationship between SL and reading ability in typically developing children and healthy adults. We tested SL using visually presented stimuli within a triplet learning paradigm and examined reading ability by administering the Wide Range (...)
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  46.  28
    Linda B. Smith, Eliana Colunga & Hanako Yoshida (2010). Knowledge as Process: Contextually Cued Attention and Early Word Learning. Cognitive Science 34 (7):1287-1314.
    Learning depends on attention. The processes that cue attention in the moment dynamically integrate learned regularities and immediate contextual cues. This paper reviews the extensive literature on cued attention and attentional learning in the adult literature and proposes that these fundamental processes are likely significant mechanisms of change in cognitive development. The value of this idea is illustrated using phenomena in children's novel word learning.
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  47.  56
    Peter Hartl (2016). Modal Scepticism, Yablo-Style Conceivability, and Analogical Reasoning. Synthese 193 (1):269-291.
    This paper offers a detailed criticism of different versions of modal scepticism proposed by Van Inwagen and Hawke, and, against these views, attempts to vindicate our reliance on thought experiments in philosophy. More than one different meaning of “ modal scepticism” will be distinguished. Focusing mainly on Hawke’s more detailed view I argue that none of these versions of modal scepticism is compelling, since sceptical conclusions depend on an untenable and, perhaps, incoherent modal epistemology. With a detailed account of modal (...)
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  48. Gregory M. Nixon (2013). Scientism, Philosophy and Brain-Based Learning. Northwest Journal of Teacher Education 11 (2):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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  49.  6
    Adam Albright & Bruce Hayes (2003). Rules Vs. Analogy in English Past Tenses: A Computational/Experimental Study. Cognition 90 (2):119-161.
    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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  50. Marleen Maarleveld & Constant Dabgbégnon (1999). Managing Natural Resources: A Social Learning Perspective. [REVIEW] Agriculture and Human Values 16 (3):267-280.
    This article presents a social learning perspective as a means to analyze and facilitate collective decision making and action in managed resource systems such as platforms. First, the social learning perspective is developed in terms of a normative and analytical framework. The normative framework entails three value principles, namely, systems thinking, experimentation, and communicative rationality. The analytical framework is built up around the following questions: who learns, what is learned, why it is learned, and how. Next, this perspective (...)
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