Results for 'Word generalization'

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  1.  1
    The Effect of Sleep on Children's Word Retention and Generalization.Emma L. Axelsson, Sophie E. Williams & Jessica S. Horst - 2016 - Frontiers in Psychology 7.
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  2.  13
    Mediated Generalization and the Interpretation of Verbal Behavior: II. Experimental Study of Certain Homophone and Synonym Gradients.J. P. Foley Jr & C. N. Cofer - 1943 - Journal of Experimental Psychology 32 (2):168.
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  3.  5
    A Study of Semantic Generalization Through the Use of Established Conceptual Mediations.Richard J. Kaplan - 1959 - Journal of Experimental Psychology 57 (5):288.
  4.  5
    Generalization in Memory.J. Goodwin, L. Long & L. Welch - 1945 - Journal of Experimental Psychology 35 (1):71.
  5.  4
    Set, Stress, and Efficiency of Semantic Generalization.August L. Peastrel, Julius Wishner & Burt E. Kaplan - 1968 - Journal of Experimental Psychology 77 (1):116.
  6.  72
    Japanese Sound-Symbolism Facilitates Word Learning in English-Speaking Children.Katerina Kantartzis, Mutsumi Imai & Sotaro Kita - 2011 - Cognitive Science 35 (3):575-586.
    Sound-symbolism is the nonarbitrary link between the sound and meaning of a word. Japanese-speaking children performed better in a verb generalization task when they were taught novel sound-symbolic verbs, created based on existing Japanese sound-symbolic words, than novel nonsound-symbolic verbs (Imai, Kita, Nagumo, & Okada, 2008). A question remained as to whether the Japanese children had picked up regularities in the Japanese sound-symbolic lexicon or were sensitive to universal sound-symbolism. The present study aimed to provide support for the (...)
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  7.  18
    Word Meanings Evolve to Selectively Preserve Distinctions on Salient Dimensions.Catriona Silvey, Simon Kirby & Kenny Smith - 2015 - Cognitive Science 39 (1):212-226.
    Words refer to objects in the world, but this correspondence is not one-to-one: Each word has a range of referents that share features on some dimensions but differ on others. This property of language is called underspecification. Parts of the lexicon have characteristic patterns of underspecification; for example, artifact nouns tend to specify shape, but not color, whereas substance nouns specify material but not shape. These regularities in the lexicon enable learners to generalize new words appropriately. How does the (...)
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  8.  12
    Not Only Size Matters: Early‐Talker and Late‐Talker Vocabularies Support Different Word‐Learning Biases in Babies and Networks.Eliana Colunga & Clare E. Sims - 2017 - Cognitive Science 41 (S1):73-95.
    In typical development, word learning goes from slow and laborious to fast and seemingly effortless. Typically developing 2-year-olds seem to intuit the whole range of things in a category from hearing a single instance named—they have word-learning biases. This is not the case for children with relatively small vocabularies. We present a computational model that accounts for the emergence of word-learning biases in children at both ends of the vocabulary spectrum based solely on vocabulary structure. The results (...)
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  9.  26
    Learning Object Names at Different Hierarchical Levels Using Cross‐Situational Statistics.Chen Chi-Hsin, Zhang Yayun & Yu Chen - 2018 - Cognitive Science:591-605.
    Objects in the world usually have names at different hierarchical levels. This research investigates adults' ability to use cross-situational statistics to simultaneously learn object labels at individual and category levels. The results revealed that adults were able to use co-occurrence information to learn hierarchical labels in contexts where the labels for individual objects and labels for categories were presented in completely separated blocks, in interleaved blocks, or mixed in the same trial. Temporal presentation schedules significantly affected the learning of individual (...)
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  10.  17
    Generalization and Hume's Problem of Induction: Misconceptions and Clarifications.Eric W. K. Tsang & John N. Williams - unknown
    In Generalizing Generalizability in Information Systems Research Lee and Baskerville (2003) attempt to clarify generalization and distinguish four types of generalization. Although this is a useful objective, what they call generalization is often not generalization at all in the proper sense of the word. We elucidate generalization by locating their major errors. A main source of these is their failure to understand the depth of Hume’s problem of induction. We give a thorough explication of (...)
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  11.  27
    The Other Way to Learn the Meaning of a Word.Sam Scott - 2001 - Behavioral and Brain Sciences 24 (6):1117-1118.
    Bloom's book can be viewed as a long argument for an anti-Whorfian conclusion. According to Bloom, word learning is usually a process of mapping new words to pre-existing concepts. But an exception to this generalization – the learning of words from linguistic context – poses a problem for Bloom's anti-Whorfian argument.
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  12.  23
    Non‐Bayesian Noun Generalization in 3‐ to 5‐Year‐Old Children: Probing the Role of Prior Knowledge in the Suspicious Coincidence Effect. [REVIEW]Gavin W. Jenkins, Larissa K. Samuelson, Jodi R. Smith & John P. Spencer - 2015 - Cognitive Science 39 (2):268-306.
    It is unclear how children learn labels for multiple overlapping categories such as “Labrador,” “dog,” and “animal.” Xu and Tenenbaum suggested that learners infer correct meanings with the help of Bayesian inference. They instantiated these claims in a Bayesian model, which they tested with preschoolers and adults. Here, we report data testing a developmental prediction of the Bayesian model—that more knowledge should lead to narrower category inferences when presented with multiple subordinate exemplars. Two experiments did not support this prediction. Children (...)
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  13.  60
    Generalization, Similarity, and Bayesian Inference.Joshua B. Tenenbaum & Thomas L. Griffiths - 2001 - Behavioral and Brain Sciences 24 (4):629-640.
    Shepard has argued that a universal law should govern generalization across different domains of perception and cognition, as well as across organisms from different species or even different planets. Starting with some basic assumptions about natural kinds, he derived an exponential decay function as the form of the universal generalization gradient, which accords strikingly well with a wide range of empirical data. However, his original formulation applied only to the ideal case of generalization from a single encountered (...)
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  14. Polysemy and Word Meaning: An Account of Lexical Meaning for Different Kinds of Content Words.Agustin Vicente - 2018 - Philosophical Studies 175 (4):947-968.
    There is an ongoing debate about the meaning of lexical words, i.e., words that contribute with content to the meaning of sentences. This debate has coincided with a renewal in the study of polysemy, which has taken place in the psycholinguistics camp mainly. There is already a fruitful interbreeding between two lines of research: the theoretical study of lexical word meaning, on the one hand, and the models of polysemy psycholinguists present, on the other. In this paper I aim (...)
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  15.  56
    Cross-Situational Learning: An Experimental Study of Word-Learning Mechanisms.Kenny Smith, Andrew D. M. Smith & Richard A. Blythe - 2011 - 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 successfully and more (...)
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  16.  40
    Competitive Processes in Cross‐Situational Word Learning.Daniel Yurovsky, Chen Yu & Linda B. Smith - 2013 - 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 learning provides (...)
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  17.  44
    A Probabilistic Computational Model of Cross-Situational Word Learning.Afsaneh Fazly, Afra Alishahi & Suzanne Stevenson - 2010 - 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 disagreement (...)
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  18.  34
    Retrieval Dynamics and Retention in Cross‐Situational Statistical Word Learning.Haley A. Vlach & Catherine M. Sandhofer - 2014 - 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 with a (...)
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  19.  44
    The Role of Explanation in Discovery and Generalization: Evidence From Category Learning.Joseph J. Williams & Tania Lombrozo - 2010 - Cognitive Science 34 (5):776-806.
    Research in education and cognitive development suggests that explaining plays a key role in learning and generalization: When learners provide explanations—even to themselves—they learn more effectively and generalize more readily to novel situations. This paper proposes and tests a subsumptive constraints account of this effect. Motivated by philosophical theories of explanation, this account predicts that explaining guides learners to interpret what they are learning in terms of unifying patterns or regularities, which promotes the discovery of broad generalizations. Three experiments (...)
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  20.  74
    Looking in the Wrong Direction Correlates With More Accurate Word Learning.Stanka A. Fitneva & Morten H. Christiansen - 2011 - Cognitive Science 35 (2):367-380.
    Previous research on lexical development has aimed to identify the factors that enable accurate initial word-referent mappings based on the assumption that the accuracy of initial word-referent associations is critical for word learning. The present study challenges this assumption. Adult English speakers learned an artificial language within a cross-situational learning paradigm. Visual fixation data were used to assess the direction of visual attention. Participants whose longest fixations in the initial trials fell more often on distracter images performed (...)
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  21.  40
    Serving Two Masters: Ethics, Epistemology, and Taking People at Their Word.Jorah Dannenberg - 2019 - Australasian Journal of Philosophy 98 (1):119-136.
    Word-taking has both an epistemic and an ethical dimension. I argue that we have no good way of understanding how both ethical and epistemic considerations can be brought to bear when someone makes up her mind to take another at her word, even as we recognize that they must. This difficulty runs deep, and takes the familiar form of a sceptical problem. It originates in an otherwise powerful and compelling way of thinking about what distinguishes theoretical from practical (...)
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  22.  73
    Sampling Assumptions in Inductive Generalization.Daniel J. Navarro, Matthew J. Dry & Michael D. Lee - 2012 - Cognitive Science 36 (2):187-223.
    Inductive generalization, where people go beyond the data provided, is a basic cognitive capability, and it underpins theoretical accounts of learning, categorization, and decision making. To complete the inductive leap needed for generalization, people must make a key ‘‘sampling’’ assumption about how the available data were generated. Previous models have considered two extreme possibilities, known as strong and weak sampling. In strong sampling, data are assumed to have been deliberately generated as positive examples of a concept, whereas in (...)
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  23.  22
    Multimodal Word Meaning Induction From Minimal Exposure to Natural Text.Angeliki Lazaridou, Marco Marelli & Marco Baroni - 2017 - Cognitive Science 41 (S4):677-705.
    By the time they reach early adulthood, English speakers are familiar with the meaning of thousands of words. In the last decades, computational simulations known as distributional semantic models have demonstrated that it is possible to induce word meaning representations solely from word co-occurrence statistics extracted from a large amount of text. However, while these models learn in batch mode from large corpora, human word learning proceeds incrementally after minimal exposure to new words. In this study, we (...)
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  24. Determination, Uniformity, and Relevance: Normative Criteria for Generalization and Reasoning by Analogy.Todd R. Davies - 1988 - In David H. Helman (ed.), Analogical Reasoning. Kluwer Academic Publishers. pp. 227-250.
    This paper defines the form of prior knowledge that is required for sound inferences by analogy and single-instance generalizations, in both logical and probabilistic reasoning. In the logical case, the first order determination rule defined in Davies (1985) is shown to solve both the justification and non-redundancy problems for analogical inference. The statistical analogue of determination that is put forward is termed 'uniformity'. Based on the semantics of determination and uniformity, a third notion of "relevance" is defined, both logically and (...)
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  25.  18
    The Pursuit of Word Meanings.Stevens Jon Scott, R. Gleitman Lila, C. Trueswell John & Yang Charles - 2017 - Cognitive Science 41 (S4):638-676.
    We evaluate here the performance of four models of cross-situational word learning: two global models, which extract and retain multiple referential alternatives from each word occurrence; and two local models, which extract just a single referent from each occurrence. One of these local models, dubbed Pursuit, uses an associative learning mechanism to estimate word-referent probability but pursues and tests the best referent-meaning at any given time. Pursuit is found to perform as well as global models under many (...)
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  26.  26
    Learning Concepts: A Learning-Theoretic Solution to the Complex-First Paradox.Nina Laura Poth & Peter Brössel - 2020 - Philosophy of Science 87 (1):135-151.
    Children acquire complex concepts like DOG earlier than simple concepts like BROWN, even though our best neuroscientific theories suggest that learning the former is harder than learning the latter and, thus, should take more time (Werning2010). This is the Complex- First Paradox. We present a novel solution to the Complex-First Paradox. Our solution builds on a generalization of Xu and Tenenbaum’s (XTB07a) Bayesian model of word learning. By focusing on a rational theory of concept learning, we show that (...)
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  27.  22
    Pseudofinite Structures and Simplicity.Darío García, Dugald Macpherson & Charles Steinhorn - 2015 - Journal of Mathematical Logic 15 (1):1550002.
    We explore a notion of pseudofinite dimension, introduced by Hrushovski and Wagner, on an infinite ultraproduct of finite structures. Certain conditions on pseudofinite dimension are identified that guarantee simplicity or supersimplicity of the underlying theory, and that a drop in pseudofinite dimension is equivalent to forking. Under a suitable assumption, a measure-theoretic condition is shown to be equivalent to local stability. Many examples are explored, including vector spaces over finite fields viewed as 2-sorted finite structures, and homocyclic groups. Connections are (...)
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  28. Protein Analysis Meets Visual Word Recognition: A Case for String Kernels in the Brain.Thomas Hannagan & Jonathan Grainger - 2012 - Cognitive Science 36 (4):575-606.
    It has been recently argued that some machine learning techniques known as Kernel methods could be relevant for capturing cognitive and neural mechanisms (Jäkel, Schölkopf, & Wichmann, 2009). We point out that ‘‘String kernels,’’ initially designed for protein function prediction and spam detection, are virtually identical to one contending proposal for how the brain encodes orthographic information during reading. We suggest some reasons for this connection and we derive new ideas for visual word recognition that are successfully put to (...)
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  29.  40
    Detailed Behavioral Analysis as a Window Into Cross-Situational Word Learning.Sumarga H. Suanda & Laura L. Namy - 2012 - Cognitive Science 36 (3):545-559.
    Recent research has demonstrated that word learners can determine word-referent mappings by tracking co-occurrences across multiple ambiguous naming events. The current study addresses the mechanisms underlying this capacity to learn words cross-situationally. This replication and extension of Yu and Smith (2007) investigates the factors influencing both successful cross-situational word learning and mis-mappings. Item analysis and error patterns revealed that the co-occurrence structure of the learning environment as well as the context of the testing environment jointly affected learning (...)
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  30.  67
    Main Problems of Diagrammatic Reasoning. Part I: The Generalization Problem. [REVIEW]Zenon Kulpa - 2009 - Foundations of Science 14 (1-2):75-96.
    The paper attempts to analyze in some detail the main problems encountered in reasoning using diagrams, which may cause errors in reasoning, produce doubts concerning the reliability of diagrams, and impressions that diagrammatic reasoning lacks the rigour necessary for mathematical reasoning. The paper first argues that such impressions come from long neglect which led to a lack of well-developed, properly tested and reliable reasoning methods, as contrasted with the amount of work generations of mathematicians expended on refining the methods of (...)
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  31.  31
    Classical Sāṁkhya on the Relationship Between a Word and Its Meaning.Ołena Łucyszyna - 2016 - Journal of Indian Philosophy 44 (2):303-323.
    The aim of this article is to reconstruct the classical Sāṁkhya view on the relationship between a word and its meaning. The study embraces all the extant texts of classical Sāṁkhya, but it is based mainly on the Yuktidīpikā, since this commentary contains most of the fragments which are directly related to the topic of our research. The textual analysis has led me to the following conclusion. It is possible to reconstruct two different and conflicting views on the relationship (...)
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  32.  18
    Feature Biases in Early Word Learning: Network Distinctiveness Predicts Age of Acquisition.Tomas Engelthaler & Thomas T. Hills - 2016 - Cognitive Science 40 (6):n/a-n/a.
    Do properties of a word's features influence the order of its acquisition in early word learning? Combining the principles of mutual exclusivity and shape bias, the present work takes a network analysis approach to understanding how feature distinctiveness predicts the order of early word learning. Distance networks were built from nouns with edge lengths computed using various distance measures. Feature distinctiveness was computed as a distance measure, showing how far an object in a network is from other (...)
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  33.  16
    Moving Word Learning to a Novel Space: A Dynamic Systems View of Referent Selection and Retention.K. Samuelson Larissa, C. Kucker Sarah & P. Spencer John - 2016 - Cognitive Science 40 (7):52-72.
    Theories of cognitive development must address both the issue of how children bring their knowledge to bear on behavior in-the-moment, and how knowledge changes over time. We argue that seeking answers to these questions requires an appreciation of the dynamic nature of the developing system in its full, reciprocal complexity. We illustrate this dynamic complexity with results from two lines of research on early word learning. The first demonstrates how the child's active engagement with objects and people supports referent (...)
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  34.  21
    Overestimation of Knowledge About Word Meanings: The “Misplaced Meaning” Effect.Jonathan F. Kominsky & Frank C. Keil - 2014 - Cognitive Science 38 (8):1604-1633.
    Children and adults may not realize how much they depend on external sources in understanding word meanings. Four experiments investigated the existence and developmental course of a “Misplaced Meaning” effect, wherein children and adults overestimate their knowledge about the meanings of various words by underestimating how much they rely on outside sources to determine precise reference. Studies 1 and 2 demonstrate that children and adults show a highly consistent MM effect, and that it is stronger in young children. Study (...)
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  35.  17
    Cross‐Situational Learning of Minimal Word Pairs.Paola Escudero, Karen E. Mulak & Haley A. Vlach - 2016 - Cognitive Science 40 (2):455-465.
    Cross-situational statistical learning of words involves tracking co-occurrences of auditory words and objects across time to infer word-referent mappings. Previous research has demonstrated that learners can infer referents across sets of very phonologically distinct words, but it remains unknown whether learners can encode fine phonological differences during cross-situational statistical learning. This study examined learners’ cross-situational statistical learning of minimal pairs that differed on one consonant segment, minimal pairs that differed on one vowel segment, and non-minimal pairs that differed on (...)
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  36.  29
    The Perceptions of Consumers Regarding Online Retailers' Ethics and Their Relationship with Consumers' General Internet Expertise and Word of Mouth: A Preliminary Analysis. [REVIEW]Sergio Román & Pedro J. Cuestas - 2008 - Journal of Business Ethics 83 (4):641 - 656.
    Ethical concerns of Internet users continue to rise. Accordingly, several scholars have called for systematic empirical research to address these issues. This study examines the conceptualization and measurement of consumers' perceptions regarding the ethics of online retailers (CPEOR). Also, this research represents a first step into the analysis of the relationship between CPEOR, consumers' general Internet expertise and reported positive word of mouth (WOM). Results, from a convenience sample of 357 online shoppers, suggest that CPEOR can be operationalized as (...)
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  37.  53
    Using Variability to Guide Dimensional Weighting: Associative Mechanisms in Early Word Learning.Keith S. Apfelbaum & Bob McMurray - 2011 - Cognitive Science 35 (6):1105-1138.
    At 14 months, children appear to struggle to apply their fairly well-developed speech perception abilities to learning similar sounding words (e.g., bih/dih; Stager & Werker, 1997). However, variability in nonphonetic aspects of the training stimuli seems to aid word learning at this age. Extant theories of early word learning cannot account for this benefit of variability. We offer a simple explanation for this range of effects based on associative learning. Simulations suggest that if infants encode both noncontrastive information (...)
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  38.  16
    When Cars Hit Trucks and Girls Hug Boys: The Effect of Animacy on Word Order in Gestural Language Creation.Annemarie Kocab, Hannah Lam & Jesse Snedeker - 2018 - Cognitive Science 42 (3):918-938.
    A well-known typological observation is the dominance of subject-initial word orders, SOV and SVO, across the world's languages. Recent findings from gestural language creation paradigms offer possible explanations for the prevalence of SOV. When asked to gesture transitive events with an animate agent and inanimate patient, gesturers tend to produce SOV order, regardless of their native language biases. Interestingly, when the patient is animate, gesturers shift away from SOV to use of other orders, like SVO and OSV. Two competing (...)
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  39.  85
    The Over-Generalization Problem: Predicates Rigidly Signifying the "Unnatural".Dan López de Sa - 2008 - Synthese 163 (2):263 - 272.
    According to the simple proposal, a predicate is rigid iff it signifies the same property across the different possible worlds. The simple proposal has been claimed to suffer from an over-generalization problem. Assume that one can make sense of predicates signifying properties, and assume that trivialization concerns, to the effect that the notion would cover any predicate whatsoever, can be overcome. Still, the proposal would over-generalize, the worry has it, by covering predicates for artifactual, social, or evaluative properties, such (...)
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  40.  36
    Rules Versus Statistics: Insights From a Highly Inflected Language.Jelena Mirković, Mark S. Seidenberg & Marc F. Joanisse - 2011 - Cognitive Science 35 (4):638-681.
    Inflectional morphology has been taken as a paradigmatic example of rule-governed grammatical knowledge (Pinker, 1999). The plausibility of this claim may be related to the fact that it is mainly based on studies of English, which has a very simple inflectional system. We examined the representation of inflectional morphology in Serbian, which encodes number, gender, and case for nouns. Linguists standardly characterize this system as a complex set of rules, with disagreements about their exact form. We present analyses of a (...)
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  41.  31
    Learning During Processing: Word Learning Doesn't Wait for Word Recognition to Finish.S. Apfelbaum Keith & McMurray Bob - 2017 - Cognitive Science 41 (S4):706-747.
    Previous research on associative learning has uncovered detailed aspects of the process, including what types of things are learned, how they are learned, and where in the brain such learning occurs. However, perceptual processes, such as stimulus recognition and identification, take time to unfold. Previous studies of learning have not addressed when, during the course of these dynamic recognition processes, learned representations are formed and updated. If learned representations are formed and updated while recognition is ongoing, the result of learning (...)
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  42.  13
    The Utility of Cognitive Plausibility in Language Acquisition Modeling: Evidence From Word Segmentation.Lawrence Phillips & Lisa Pearl - 2015 - Cognitive Science 39 (8):1824-1854.
    The informativity of a computational model of language acquisition is directly related to how closely it approximates the actual acquisition task, sometimes referred to as the model's cognitive plausibility. We suggest that though every computational model necessarily idealizes the modeled task, an informative language acquisition model can aim to be cognitively plausible in multiple ways. We discuss these cognitive plausibility checkpoints generally and then apply them to a case study in word segmentation, investigating a promising Bayesian segmentation strategy. We (...)
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  43.  58
    The Modulation of Visual and Task Characteristics of a Writing System on Hemispheric Lateralization in Visual Word Recognition—A Computational Exploration.Janet H. Hsiao & Sze Man Lam - 2013 - Cognitive Science 37 (5):861-890.
    Through computational modeling, here we examine whether visual and task characteristics of writing systems alone can account for lateralization differences in visual word recognition between different languages without assuming influence from left hemisphere (LH) lateralized language processes. We apply a hemispheric processing model of face recognition to visual word recognition; the model implements a theory of hemispheric asymmetry in perception that posits low spatial frequency biases in the right hemisphere and high spatial frequency (HSF) biases in the LH. (...)
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  44.  13
    A Bootstrapping Model of Frequency and Context Effects in Word Learning.Kachergis George, Yu Chen & M. Shiffrin Richard - 2017 - Cognitive Science 41 (3):590-622.
    Prior research has shown that people can learn many nouns from a short series of ambiguous situations containing multiple words and objects. For successful cross-situational learning, people must approximately track which words and referents co-occur most frequently. This study investigates the effects of allowing some word-referent pairs to appear more frequently than others, as is true in real-world learning environments. Surprisingly, high-frequency pairs are not always learned better, but can also boost learning of other pairs. Using a recent associative (...)
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  45.  20
    Logics for Qualitative Inductive Generalization.Diderik Batens - 2011 - Studia Logica 97 (1):61 - 80.
    The paper contains a survey of (mainly unpublished) adaptive logics of inductive generalization. These defeasible logics are precise formulations of certain methods. Some attention is also paid to ways of handling background knowledge, introducing mere conjectures, and the research guiding capabilities of the logics.
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  46.  25
    Generalization: A Practice of Situated Categorization in Talk. [REVIEW]Eric Hauser - 2011 - Human Studies 34 (2):183-198.
    This paper analyzes four instances in talk of generalization about people, that is, of using statements about one or more people as the basis of stating something about a category. Generalization can be seen as a categorization practice which involves a reflexive relationship between the generalized-from person or people and the generalized-to category. One thing that is accomplished through generalization is instruction in how to understand the identity of the generalized-from person or people, so in addition to (...)
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  47.  5
    The Universal Generalization Problem.Carlo Cellucci - 2009 - Logique Et Analyse 52.
    The universal generalization problem is the question: What entitles one to conclude that a property established for an individual object holds for any individual object in the domain? This amounts to the question: Why is the rule of universal generalization justified? In the modern and contemporary age Descartes, Locke, Berkeley, Hume, Kant, Mill, Gentzen gave alternative solutions of the universal generalization problem. In this paper I consider Locke’s, Berkeley’s and Gentzen’s solutions and argue that they are problematic. (...)
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    What's in a Name for Memory Errors? Implications and Ethical Issues Arising From the Use of the Term "False Memory" for Errors in Memory for Details.Anne P. DePrince, Carolyn B. Allard, Hannah Oh & Jennifer J. Freyd - 2004 - Ethics and Behavior 14 (3):201 – 233.
    The term "false memories" has been used to refer to suggestibility experiments in which whole events are apparently confabulated and in media accounts of contested memories of childhood abuse. Since 1992 psychologists have increasingly used the term "false memory" when discussing memory errors for details, such as specific words within word lists. Use of the term to refer to errors in details is a shift in language away from other terms used historically (e.g., "memory intrusions"). We empirically examine this (...)
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    Canalization of Language Structure From Environmental Constraints: A Computational Model of Word Learning From Multiple Cues.Padraic Monaghan - 2017 - Topics in Cognitive Science 9 (1):21-34.
    There is substantial variation in language experience, yet there is surprising similarity in the language structure acquired. Constraints on language structure may be external modulators that result in this canalization of language structure, or else they may derive from the broader, communicative environment in which language is acquired. In this paper, the latter perspective is tested for its adequacy in explaining robustness of language learning to environmental variation. A computational model of word learning from cross-situational, multimodal information was constructed (...)
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    Graph‐Theoretic Properties of Networks Based on Word Association Norms: Implications for Models of Lexical Semantic Memory.Thomas M. Gruenenfelder, Gabriel Recchia, Tim Rubin & Michael N. Jones - 2016 - Cognitive Science 40 (6):1460-1495.
    We compared the ability of three different contextual models of lexical semantic memory and of a simple associative model to predict the properties of semantic networks derived from word association norms. None of the semantic models were able to accurately predict all of the network properties. All three contextual models over-predicted clustering in the norms, whereas the associative model under-predicted clustering. Only a hybrid model that assumed that some of the responses were based on a contextual model and others (...)
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