The Experts below are selected from a list of 171003 Experts worldwide ranked by ideXlab platform
Joanne Lobato - One of the best experts on this subject based on the ideXlab platform.
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alternative perspectives on the transfer of Learning History issues and challenges for future research
The Journal of the Learning Sciences, 2006Co-Authors: Joanne LobatoAbstract:ion Responding to critiques related to the roles of abstraction and decontextualization in transfer has been more challenging then tackling issues related to metaphor. In mainstream cognitive accounts of transfer, the formation of sufficiently abstract representations is a necessary condition for transfer (Reed, 1993; Singley & Anderson, 1989). Abstraction is typically conceived as the extraction of commonalities from a set of concrete examples (e.g., Rosch & Mervis, 1975). As a result, it is deemed important for learners to engage with multiple situations and to compare problem solutions in order to construct an abstract representation spanning them (Chen & Daehler, 2000; Gentner, Loewenstein, & Thompson, 2003; Reeves & Weisberg, 1994). Abstraction is thus conceived as a process of decontextualization. According to Fuchs et al. (2003), abstractions “delete details across exemplars ... and avoid contextual specificity so they can be applied to other instances or across situations” (p. 294). As mentioned previously, the notion of detaching from concrete experience is problematic from a situated perspective (Hall, 1996). Hence, many situated researchers reject decontextualization and abstraction as epistemologically incompatible with situativity. In his article The Fallacy of Decontextualization, van Oers (1998) argued that if context is defined via personal interpretation of actions and TRANSFER OF Learning 439
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alternative perspectives on the transfer of Learning History issues and challenges for future research
The Journal of the Learning Sciences, 2006Co-Authors: Joanne LobatoAbstract:(2006). Alternative Perspectives on the Transfer of Learning: History, Issues, and Challenges for Future Research. Journal of the Learning Sciences: Vol. 15, No. 4, pp. 431-449.
Joshua B Tenenbaum - One of the best experts on this subject based on the ideXlab platform.
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the large scale structure of semantic networks statistical analyses and a model of semantic growth
Cognitive Science, 2005Co-Authors: Mark Steyvers, Joshua B TenenbaumAbstract:We present statistical analyses of the large-scale structure of 3 types of semantic networks: word associations, WordNet, and Roget’s Thesaurus. We show that they have a small-world structure, characterized by sparse connectivity, short average path lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale-free pattern of connectivity, with most nodes having relatively few connections joined together through a small number of hubs with many connections. These regularities have also been found in certain other complex natural networks, such as the World Wide Web, but they are not consistent with many conventional models of semantic organization, based on inheritance hierarchies, arbitrarily structured networks, or high-dimensional vector spaces. We propose that these structures reflect the mechanisms by which semantic networks grow. We describe a simple model for semantic growth, in which each new word or concept is connected to an existing network by differentiating the connectivity pattern of an existing node. This model generates appropriate small-world statistics and power-law connectivity distributions, and it also suggests one possible mechanistic basis for the effects of Learning History variables (age of acquisition, usage frequency) on behavioral performance in semantic processing tasks.
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the large scale structure of semantic networks statistical analyses and a model for semantic growth
arXiv: Soft Condensed Matter, 2001Co-Authors: Mark Steyvers, Joshua B TenenbaumAbstract:We present statistical analyses of the large-scale structure of three types of semantic networks: word associations, WordNet, and Roget's thesaurus. We show that they have a small-world structure, characterized by sparse connectivity, short average path-lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale-free pattern of connectivity, with most nodes having relatively few connections joined together through a small number of hubs with many connections. These regularities have also been found in certain other complex natural networks, such as the world wide web, but they are not consistent with many conventional models of semantic organization, based on inheritance hierarchies, arbitrarily structured networks, or high-dimensional vector spaces. We propose that these structures reflect the mechanisms by which semantic networks grow. We describe a simple model for semantic growth, in which each new word or concept is connected to an existing network by differentiating the connectivity pattern of an existing node. This model generates appropriate small-world statistics and power-law connectivity distributions, and also suggests one possible mechanistic basis for the effects of Learning History variables (age-of-acquisition, usage frequency) on behavioral performance in semantic processing tasks.
Jennifer Mayer - One of the best experts on this subject based on the ideXlab platform.
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Empathic-like responding by domestic dogs (Canis familiaris) to distress in humans: an exploratory study
Animal Cognition, 2012Co-Authors: Deborah Custance, Jennifer MayerAbstract:Empathy covers a range of phenomena from cognitive empathy involving metarepresentation to emotional contagion stemming from automatically triggered reflexes. An experimental protocol first used with human infants was adapted to investigate empathy in domestic dogs. Dogs oriented toward their owner or a stranger more often when the person was pretending to cry than when they were talking or humming. Observers, unaware of experimental hypotheses and the condition under which dogs were responding, more often categorized dogs’ approaches as submissive as opposed to alert, playful or calm during the crying condition. When the stranger pretended to cry, rather than approaching their usual source of comfort, their owner, dogs sniffed, nuzzled and licked the stranger instead. The dogs’ pattern of response was behaviorally consistent with an expression of empathic concern, but is most parsimoniously interpreted as emotional contagion coupled with a previous Learning History in which they have been rewarded for approaching distressed human companions.
Mark Steyvers - One of the best experts on this subject based on the ideXlab platform.
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the large scale structure of semantic networks statistical analyses and a model of semantic growth
Cognitive Science, 2005Co-Authors: Mark Steyvers, Joshua B TenenbaumAbstract:We present statistical analyses of the large-scale structure of 3 types of semantic networks: word associations, WordNet, and Roget’s Thesaurus. We show that they have a small-world structure, characterized by sparse connectivity, short average path lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale-free pattern of connectivity, with most nodes having relatively few connections joined together through a small number of hubs with many connections. These regularities have also been found in certain other complex natural networks, such as the World Wide Web, but they are not consistent with many conventional models of semantic organization, based on inheritance hierarchies, arbitrarily structured networks, or high-dimensional vector spaces. We propose that these structures reflect the mechanisms by which semantic networks grow. We describe a simple model for semantic growth, in which each new word or concept is connected to an existing network by differentiating the connectivity pattern of an existing node. This model generates appropriate small-world statistics and power-law connectivity distributions, and it also suggests one possible mechanistic basis for the effects of Learning History variables (age of acquisition, usage frequency) on behavioral performance in semantic processing tasks.
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the large scale structure of semantic networks statistical analyses and a model for semantic growth
arXiv: Soft Condensed Matter, 2001Co-Authors: Mark Steyvers, Joshua B TenenbaumAbstract:We present statistical analyses of the large-scale structure of three types of semantic networks: word associations, WordNet, and Roget's thesaurus. We show that they have a small-world structure, characterized by sparse connectivity, short average path-lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale-free pattern of connectivity, with most nodes having relatively few connections joined together through a small number of hubs with many connections. These regularities have also been found in certain other complex natural networks, such as the world wide web, but they are not consistent with many conventional models of semantic organization, based on inheritance hierarchies, arbitrarily structured networks, or high-dimensional vector spaces. We propose that these structures reflect the mechanisms by which semantic networks grow. We describe a simple model for semantic growth, in which each new word or concept is connected to an existing network by differentiating the connectivity pattern of an existing node. This model generates appropriate small-world statistics and power-law connectivity distributions, and also suggests one possible mechanistic basis for the effects of Learning History variables (age-of-acquisition, usage frequency) on behavioral performance in semantic processing tasks.
Peter Murrell - One of the best experts on this subject based on the ideXlab platform.
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a machine Learning History of english caselaw and legal ideas prior to the industrial revolution ii applications
Journal of Institutional Economics, 2021Co-Authors: Peter Grajzl, Peter MurrellAbstract:This is the second of two papers that generate and analyze quantitative estimates of the development of English caselaw and associated legal ideas before the Industrial Revolution. In the first paper, we estimated a 100-topic structural topic model, named the topics, and showed how to interpret topic-prevalence timelines. Here, we provide examples of new insights that can be gained from these estimates. We first provide a bird's-eye view, aggregating the topics into 15 themes. Procedure is the highest-prevalence theme, but by the mid-18th century attention to procedure decreases sharply, indicating solidification of court institutions. Important ideas on real-property were substantially settled by the mid-17th century and on contracts and torts by the mid-18th century. Thus, crucial elements of caselaw developed before the Industrial Revolution. We then examine the legal ideas associated with England's financial revolution. Many new legal ideas relevant to finance were well accepted before the Glorious Revolution. Finally, we examine the sources of law used in the courts. Emphasis on precedent-based reasoning increases by 1650, but diffusion was gradual, with pertinent ideas solidifying only after 1700. Ideas on statute applicability were accepted by the mid-16th century but debates on legislative intent were still occurring in 1750.
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a machine Learning History of english caselaw and legal ideas prior to the industrial revolution i generating and interpreting the estimates
2020Co-Authors: Peter Grajzl, Peter MurrellAbstract:The History of England's institutions has long informed research on comparative economic development. Yet to date there exists no quantitative evidence on a core aspect of England's institutional evolution, that embodied in the accumulated decisions of English courts. Focusing on the two centuries before the Industrial Revolution, we generate and analyze the first quantitative estimates of the development of English caselaw and its associated legal ideas. We achieve this in two companion papers. In this, the first of the pair, we build a comprehensive corpus of 52,949 reports of cases heard in England's high courts before 1765. Estimating a 100-topic structural topic model, we name and interpret all topics, each of which reflects a distinctive aspect of English legal thought. We produce time series of the estimated topic prevalences. To interpret the topic timelines, we develop a tractable model of the evolution of legal-cultural ideas and their prominence in case reports. In the companion paper, we will illustrate with multiple applications the usefulness of the large amount of new information generated by our approach.
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a machine Learning History of english caselaw and legal ideas prior to the industrial revolution ii applications
2020Co-Authors: Peter Grajzl, Peter MurrellAbstract:This is the second of two papers that generate and analyze quantitative estimates of the development of English caselaw and associated legal ideas before the Industrial Revolution. In the first paper, we estimated a 100-topic structural topic model, named the topics, and showed how to interpret topic-prevalence timelines. Here, we provide examples of new insights that can be gained from these estimates. We first provide a bird's-eye view, aggregating the topics into fifteen themes. Procedure is the highest-prevalence theme, but by the mid-18th century attention to procedure decreases sharply, indicating solidification of court institutions. Important ideas on real-property were substantially settled by the mid-17th century and on contracts and torts by the mid-18th century. Thus, crucial elements of caselaw developed before the Industrial Revolution. We then examine the legal ideas associated with England's financial revolution. Many new legal ideas relevant to finance were well accepted before the Glorious Revolution. Finally, we examine the sources of law used in the courts. Emphasis on precedent-based reasoning increases by 1650, but diffusion was gradual, with pertinent ideas solidifying only after 1700. Ideas on statute applicability were accepted by the mid-16th century but debates on the legislature’s intent still occurred in 1750.