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

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  1.  1
    Dedre Gentner, Susan C. Levine, Raedy Ping, Ashley Isaia, Sonica Dhillon, Claire Bradley & Garrett Honke (2015). Rapid Learning in a Children's Museum Via Analogical Comparison. Cognitive Science 39 (6).
    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.  1
    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.  51
    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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  5.  7
    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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  6.  14
    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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  7.  16
    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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  8.  10
    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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  9.  49
    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.  4
    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.  9
    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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  13.  8
    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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  14.  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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  15. 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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  16.  13
    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.
    We sought to gain more insight into the effects of attention focus and time constraints on skill learning and performance in novices and experts by means of two complementary experiments using a table tennis paradigm. Experiment 1 showed that skill-focus conditions and slowed ball frequency disrupted the accuracy of experts, but dual-task conditions and speeded ball frequency did not. For novices, only speeded ball frequency disrupted accuracy. In Experiment 2, we extended these findings by instructing novices either explicitly or (...)
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  17.  11
    Rens Bod (2009). From Exemplar to Grammar: A Probabilistic Analogy‐Based Model of Language Learning. Cognitive Science 33 (5):752-793.
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  18.  57
    Theodore Bach (2014). A Unified Account of General Learning Mechanisms and Theory‐of‐Mind Development. 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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  19. 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.
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  20.  0
    Arthur S. Reber & Rhianon Allen (1978). Analogic and Abstraction Strategies in Synthetic Grammar Learning: A Functionalist Interpretation. Cognition 6 (3):189-221.
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  21. 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.
     
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  22.  2
    Clifford Kossel (1961). The Analogy of Learning. Modern Schoolman 39 (1):78-79.
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  23.  7
    Herbert Johnston (1962). The Analogy of Learning. New Scholasticism 36 (1):111-113.
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  24.  6
    Charles Weijer, Learning From the Dutch: Physician-Assisted Death, Slippery Slopes and the Nazi Analogy.
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  25.  1
    Reinders Duit (1991). On the Role of Analogies and Metaphors in Learning Science. Science Education 75 (6):649-672.
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  26.  1
    Alanson Van Fleet (1979). Learning to Teach: The Cultural Transmission Analogy. Journal of Thought 14 (4):281-90.
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  27.  1
    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.
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  28.  0
    Callan Bentley (2008). Using Analogies to Assess Student Learning. Inquiry 13 (1):26-35.
  29.  0
    Mark H. Burstein (1988). Incremental Learning From Multiple Analogies. In Armand Prieditis (ed.), Analogica. Morgan Kaufmann Publishers 37--62.
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  30.  0
    Richmond 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.
    The research reported in this document has been sponsored by the Air Force Office of Scientific Research, OAR, under Contract AF61 ‐640 with the European Office of Aerospace Research, United States Air Force; by the Aeronautical Systems Division of the Air Force Systems Command, United States Air Force, through the European Office of the Office of Aerospace Research, under Contract AF61‐402, and by the US Department of the Army, through its European Research Office, under Contract No. DA‐91‐591‐EUC‐3216.
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  31. D. Needham & I. Begg (1990). Spontaneous Analogical Transfer is Common If Subjects Learn by Doing. Bulletin of the Psychonomic Society 28 (6):504-504.
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  32.  0
    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.
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  33.  1
    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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  34.  8
    Usha Goswami (2008). Analogy and the Brain: A New Perspective on Relational Primacy. Behavioral and Brain Sciences 31 (4):387-388.
    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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  35.  7
    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.
    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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  36. 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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  37.  71
    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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  38.  42
    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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  39. 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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  40. 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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  41.  24
    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.
    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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  42.  24
    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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  43. 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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  44.  47
    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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  45.  17
    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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  46.  5
    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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  47.  15
    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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  48.  3
    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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  49.  11
    Philip S. Gerrans (2013). Imitation, Mind Reading, and Social Learning. Biological Theory 8 (1):20-27.
    Imitation has been understood in different ways: as a cognitive adaptation subtended by genetically specified cognitive mechanisms; as an aspect of domain general human cognition. The second option has been advanced by Cecilia Heyes who treats imitation as an instance of associative learning. Her argument is part of a deflationary treatment of the “mirror neuron” phenomenon. I agree with Heyes about mirror neurons but argue that Kim Sterelny has provided the tools to provide a better account of the nature (...)
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  50. 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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