Search results for 'Computational Model' (try it on Scholar)

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  1. Shira Calamaro & Gaja Jarosz (2014). Learning General Phonological Rules From Distributional Information: A Computational Model. Cognitive Science 38 (8).score: 240.0
    Phonological rules create alternations in the phonetic realizations of related words. These rules must be learned by infants in order to identify the phonological inventory, the morphological structure, and the lexicon of a language. Recent work proposes a computational model for the learning of one kind of phonological alternation, allophony . This paper extends the model to account for learning of a broader set of phonological alternations and the formalization of these alternations as general rules. In Experiment (...)
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  2. Judith Gaspers & Philipp Cimiano (2014). A Computational Model for the Item‐Based Induction of Construction Networks. Cognitive Science 38 (2):439-488.score: 236.0
    According to usage-based approaches to language acquisition, linguistic knowledge is represented in the form of constructions—form-meaning pairings—at multiple levels of abstraction and complexity. The emergence of syntactic knowledge is assumed to be a result of the gradual abstraction of lexically specific and item-based linguistic knowledge. In this article, we explore how the gradual emergence of a network consisting of constructions at varying degrees of complexity can be modeled computationally. Linguistic knowledge is learned by observing natural language utterances in an ambiguous (...)
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  3. Deb K. Roy & Alex P. Pentland (2002). Learning Words From Sights and Sounds: A Computational Model. Cognitive Science 26 (1):113-146.score: 210.0
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  4. Vinod Goela, David Pullara & Jordan Grafman (2001). A Computational Model of Frontal Lobe Dysfunction: Working Memory and the Tower of Hanoi Task. Cognitive Science 25 (2):287-313.score: 210.0
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  5. Richard Double (1987). The Computational Model of the Mind and Philosophical Functionalism. Behaviorism 15 (2):131-39.score: 198.0
  6. Jakub Szymanik & Marcin Zajenkowski (2009). Comprehension of Simple Quantifiers. Empirical Evaluation of a Computational Model. Cognitive Science: A Multidisciplinary Journal 34 (3):521-532.score: 192.0
    We examine the verification of simple quantifiers in natural language from a computational model perspective. We refer to previous neuropsychological investigations of the same problem and suggest extending their experimental setting. Moreover, we give some direct empirical evidence linking computational complexity predictions with cognitive reality.
    In the empirical study we compare time needed for understanding different types of quantifiers. We show that the computational distinction between quantifiers recognized by finite-automata and push-down automata is psychologically relevant. Our research (...)
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  7. Afsaneh Fazly, Afra Alishahi & Suzanne Stevenson (2010). A Probabilistic Computational Model of Cross-Situational Word Learning. Cognitive Science 34 (6):1017-1063.score: 192.0
    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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  8. [deleted]Shane Lee & Stephanie R. Jones (2013). Distinguishing Mechanisms of Gamma Frequency Oscillations in Human Current Source Signals Using a Computational Model of a Laminar Neocortical Network. Frontiers in Human Neuroscience 7:869.score: 192.0
    Gamma frequency rhythms have been implicated in numerous studies for their role in healthy and abnormal brain function. The frequency band has been described to encompass as broad a range as 30–150 Hz. Crucial to understanding the role of gamma in brain function is an identification of the underlying neural mechanisms, which is particularly difficult in the absence of invasive recordings in macroscopic human signals such as those from magnetoencephalography (MEG) and electroencephalography (EEG). Here, we studied features of current dipole (...)
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  9. Mare Koit & Haldur Õim (forthcoming). A Computational Model of Argumentation in Agreement Negotiation Processes. Argument and Computation:1-28.score: 182.0
    The paper describes a computational model that we have implemented in an experimental dialogue system . Communication in a natural language between two participants A and B is considered, where A has a communicative goal that his/her partner B will make a decision to perform an action D. A argues the usefulness, pleasantness, etc. of D , in order to guide B's reasoning in a desirable direction. A computational model of argumentation is developed, which includes reasoning. (...)
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  10. L. Gabora (1995). Meme and Variations: A Computational Model of Cultural Evolution. In [Book Chapter].score: 180.0
    This paper describes a computational model of how ideas, or memes, evolve through the processes of variation, selection, and replication. Every iteration, each neural-network based agent in an artificial society has the opportunity to acquire a new meme, either through 1) INNOVATION, by mutating a previously-learned meme, or 2) IMITATION, by copying a meme performed by a neighbor. Imitation, mental simulation, and using past experience to bias mutation all increase the rate at which fitter memes evolve. Memes at (...)
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  11. David Croft & Paul Thagard, Dynamic Imagery: A Computational Model of Motion and Visual Analogy.score: 180.0
    This paper describes DIVA (Dynamic Imagery for Visual Analogy), a computational model of visual imagery based on the scene graph, a powerful representational structure widely used in computer graphics. Scene graphs make possible the visual display of complex objects, including the motions of individual objects. Our model combines a semantic-network memory system with computational procedures based on scene graphs. The model can account for people’s ability to produce visual images of moving objects, in particular the (...)
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  12. Jacolien Rij, Hedderik Rijn & Petra Hendriks (2013). How WM Load Influences Linguistic Processing in Adults: A Computational Model of Pronoun Interpretation in Discourse. Topics in Cognitive Science 5 (3):564-580.score: 180.0
    This paper presents a study of the effect of working memory load on the interpretation of pronouns in different discourse contexts: stories with and without a topic shift. We discuss a computational model (in ACT-R, Anderson, 2007) to explain how referring expressions are acquired and used. On the basis of simulations of this model, it is predicted that WM constraints only affect adults' pronoun resolution in stories with a topic shift, but not in stories without a topic (...)
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  13. Y. Sato, H. Iizuka & T. Ikegami (2013). Investigating Extended Embodiment Using a Computational Model and Human Experimentation. Constructivist Foundations 9 (1):73-84.score: 180.0
    Context: Our body schema is not restricted to biological body boundaries (such as the skin), as can be seen in the use of a cane by a person who is visually impaired or the “rubber hands” experiment. The tool becomes a part of the body schema when the focus of our attention is shifted from the tool to the task to be performed. Problem: A body schema is formed through interactions among brain, body, tool, and environment. Nevertheless, the dynamic mechanisms (...)
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  14. Matthew W. Crocker Afra Alishahi, Afsaneh Fazly, Judith Koehne (2012). Sentence-Based Attentional Mechanisms in Word Learning: Evidence From a Computational Model. Frontiers in Psychology 3.score: 180.0
    When looking for the referents of nouns, adults and young children are sensitive to cross- situational statistics (Yu & Smith, 2007; Smith & Yu, 2008). In addition, the linguistic context that a word appears in has been shown to act as a powerful attention mechanism for guiding sentence processing and word learning (Landau & Gleitman, 1985; Altmann & Kamide, 1999; Kako & Trueswell, 2000). Koehne & Crocker (2010, 2011) investigate the interaction between cross-situational evidence and guidance from the sentential context (...)
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  15. Lou Boves Christina Bergmann, Louis ten Bosch, Paula Fikkert (2013). A Computational Model to Investigate Assumptions in the Headturn Preference Procedure. Frontiers in Psychology 4.score: 180.0
    In this paper we use a computational model to investigate four assumptions that are tacitly present in interpreting the results of studies on infants' speech processing abilities using the Headturn Preference Procedure (HPP): (1) behavioural differences originate in different processing; (2) processing involves some form of recognition; (3) words are segmented from connected speech; and (4) differences between infants should not affect overall results. In addition, we investigate the impact of two potentially important aspects in the design and (...)
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  16. Glenn Gunzelmann (2008). Strategy Generalization Across Orientation Tasks: Testing a Computational Cognitive Model. Cognitive Science 32 (5):835-861.score: 180.0
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  17. Afra Alishahi, Afsaneh Fazly, Judith Koehne & Matthew W. Crocker (2012). Sentence-Based Attentional Mechanisms in Word Learning: Evidence From a Computational Model. Frontiers in Psychology 3.score: 180.0
    When looking for the referents of nouns, adults and young children are sensitive to cross- situational statistics (Yu & Smith, 2007; Smith & Yu, 2008). In addition, the linguistic context that a word appears in has been shown to act as a powerful attention mechanism for guiding sentence processing and word learning (Landau & Gleitman, 1985; Altmann & Kamide, 1999; Kako & Trueswell, 2000). Koehne & Crocker (2010, 2011) investigate the interaction between cross-situational evidence and guidance from the sentential context (...)
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  18. [deleted]Tyler D. Bancroft, William E. Hockley & Philip Servos (2011). Vibrotactile Working Memory as a Model Paradigm for Psychology, Neuroscience, and Computational Modeling. Frontiers in Human Neuroscience 5.score: 180.0
  19. Bruno G. Bara, Monica Bucciarelli & Vincenzo Lombardo (2001). Model Theory of Deduction: A Unified Computational Approach. Cognitive Science 25 (6):839-901.score: 180.0
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  20. Wendell Wallach, Stan Franklin & Colin Allen (2010). A Conceptual and Computational Model of Moral Decision Making in Human and Artificial Agents. Topics in Cognitive Science 2 (3):454-485.score: 176.0
    Recently, there has been a resurgence of interest in general, comprehensive models of human cognition. Such models aim to explain higher-order cognitive faculties, such as deliberation and planning. Given a computational representation, the validity of these models can be tested in computer simulations such as software agents or embodied robots. The push to implement computational models of this kind has created the field of artificial general intelligence (AGI). Moral decision making is arguably one of the most challenging tasks (...)
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  21. Eleanor Olds Batchelder (2002). Bootstrapping the Lexicon: A Computational Model of Infant Speech Segmentation. Cognition 83 (2):167-206.score: 176.0
    Prelinguistic infants must find a way to isolate meaningful chunks from the continuous streams of speech that they hear. BootLex, a new model which uses distributional cues to build a lexicon, demonstrates how much can be accomplished using this single source of information. This conceptually simple probabilistic algorithm achieves significant segmentation results on various kinds of language corpora - English, Japanese, and Spanish; child- and adult-directed speech, and written texts; and several variations in coding structure - and reveals which (...)
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  22. Nicola Angius & Guglielmo Tamburrini (2011). Scientific Theories of Computational Systems in Model Checking. Minds and Machines 21 (2):323-336.score: 168.0
    Model checking, a prominent formal method used to predict and explain the behaviour of software and hardware systems, is examined on the basis of reflective work in the philosophy of science concerning the ontology of scientific theories and model-based reasoning. The empirical theories of computational systems that model checking techniques enable one to build are identified, in the light of the semantic conception of scientific theories, with families of models that are interconnected by simulation relations. And (...)
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  23. François Guillaud & Patrick Hannaert (2010). A Computational Model of the Circulating Renin-Angiotensin System and Blood Pressure Regulation. Acta Biotheoretica 58 (2):143-170.score: 164.0
    The renin-angiotensin system (RAS) is critical in sodium and blood pressure (BP) regulation, and in cardiovascular-renal (CVR) diseases and therapeutics. As a contribution to SAPHIR project, we present a realistic computer model of renin production and circulating RAS, integrated into Guyton’s circulatory model ( GCM ). Juxtaglomerular apparatus, JGA , and Plasma modules were implemented in C ++/M2SL (Multi-formalism Multi-resolution Simulation Library) for fusion with GCM . Matlab © optimization toolboxes were used for parameter identification. In JGA , (...)
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  24. Peter C. R. Lane, Fernand Gobet & Peter C.-H. Cheng (2001). What Forms the Chunks in a Subject's Performance? Lessons From the CHREST Computational Model of Learning. Behavioral and Brain Sciences 24 (1):128-129.score: 164.0
    Computational models of learning provide an alternative technique for identifying the number and type of chunks used by a subject in a specific task. Results from applying CHREST to chess expertise support the theoretical framework of Cowan and a limit in visual short-term memory capacity of 3–4 looms. An application to learning from diagrams illustrates different identifiable forms of chunk.
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  25. Afra Alishahi & Suzanne Stevenson (2008). A Computational Model of Early Argument Structure Acquisition. Cognitive Science 32 (5):789-834.score: 162.0
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  26. Marc Pomplun, Eyal M. Reingold & Jiye Shen (2003). Area Activation: A Computational Model of Saccadic Selectivity in Visual Search. Cognitive Science 27 (2):299-312.score: 162.0
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  27. Jeremy R. Reynolds, Jeffrey M. Zacks & Todd S. Braver (2007). A Computational Model of Event Segmentation From Perceptual Prediction. Cognitive Science 31 (4):613-643.score: 162.0
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  28. B. Scott & A. Bansal (2013). A Cybernetic Computational Model for Learning and Skill Acquisition. Constructivist Foundations 9 (1):125-136.score: 162.0
    Context: Although there are rich descriptive accounts of skill acquisition in the literature, there are no satisfactory explanatory models of the cognitive processes involved. Problem: The aim of the paper is to explain some key phenomena frequently observed in the acquisition of motor skills: the loss of conscious access to knowledge of the structure of a skill and the awareness that an error has been made prior to the receipt of knowledge of results. Method: In the 1970s, the first author (...)
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  29. Sorinel A. Oprisan & Ana Oprisan (2006). A Computational Model of Oncogenesis Using the Systemic Approach. Axiomathes 16 (1-2):155-163.score: 156.0
    A new theoretical model of oncogenesis that incorporates a systemic view of biodynamics was developed and analyzed. According to our model, the emergent behavior at the cell population level is the result of nonlinear interactions between the neoplastic and immune subsystems. Our approach allows subsequent extensions of the model to span multiple levels of biological organization. The model opens the possibility of a flexible connection between the molecular and tissue level descriptions of oncogenesis.
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  30. Akira Utsumi (2011). Computational Exploration of Metaphor Comprehension Processes Using a Semantic Space Model. Cognitive Science 35 (2):251-296.score: 156.0
    Recent metaphor research has revealed that metaphor comprehension involves both categorization and comparison processes. This finding has triggered the following central question: Which property determines the choice between these two processes for metaphor comprehension? Three competing views have been proposed to answer this question: the conventionality view (Bowdle & Gentner, 2005), aptness view (Glucksberg & Haught, 2006b), and interpretive diversity view (Utsumi, 2007); these views, respectively, argue that vehicle conventionality, metaphor aptness, and interpretive diversity determine the choice between the categorization (...)
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  31. Steven Walczak (2002). A Context-Based Computational Model of Language Acquisition by Infants and Children. Foundations of Science 7 (4):393-411.score: 156.0
    This research attempts to understand howchildren learn to use language. Instead ofusing syntax-based grammar rules to model thedifferences between children''s language andadult language, as has been done in the past, anew model is proposed. In the new researchmodel, children acquire language by listeningto the examples of speech that they hear intheir environment and subsequently use thespeech examples that have been previously heardin similar contextual situations. A computermodel is generated to simulate this new modelof language acquisition. The MALL computerprogram (...)
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  32. Tangming Yuan, David Moore & Alec Grierson (2003). Computational Agents as a Test-Bed to Study the Philosophical Dialogue Model "DE": A Development of Mackenzie's DC. Informal Logic 23 (3).score: 156.0
    This paper reports research concerning a suitable dialogue model for human computer debate. In particular, we consider the adoption of Moore's (1993) utilization of Mackenzie's (1979) game DC, means of using computational agents as the test-bed to facilitate evaluation of the proposed model, and means of using the evaluation results as motivation to further develop a dialogue model, which can prevent fallacious argument and common errors. It is anticipated that this work will contribute toward the development (...)
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  33. L. Karl Branting (1993). A Computational Model of Ratio Decidendi. Artificial Intelligence and Law 2 (1):1-31.score: 156.0
    This paper proposes a model ofratio decidendi as a justification structure consisting of a series of reasoning steps, some of which relate abstract predicates to other abstract predicates and some of which relate abstract predicates to specific facts. This model satisfies an important set of characteristics ofratio decidendi identified from the jurisprudential literature. In particular, the model shows how the theory under which a case is decided controls its precedential effect. By contrast, a purely exemplar-based model (...)
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  34. Mauro Ursino, Cristiano Cuppini & Elisa Magosso (2010). A Computational Model of the Lexical-Semantic System Based on a Grounded Cognition Approach. Frontiers in Psychology 1:221-221.score: 156.0
    This work presents a connectionist model of the semantic-lexical system based on grounded cognition. The model assumes that the lexical and semantic aspects of language are memorized in two distinct stores. The semantic properties of objects are represented as a collection of features, whose number may vary among objects. Features are described as activation of neural oscillators in different sensory-motor areas (one area for each feature) topographically organized to implement a similarity principle. Lexical items are represented as activation (...)
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  35. Julien Diard, Vincent Rynik & Jean Lorenceau (2013). A Bayesian Computational Model for Online Character Recognition and Disability Assessment During Cursive Eye Writing. Frontiers in Psychology 4.score: 156.0
    This research involves a novel apparatus, in which the user is presented with an illusion inducing visual stimulus. The user perceives illusory movement that can be followed by the eye, so that smooth pursuit eye movements can be sustained in arbitrary directions. Thus, free-flow trajectories of any shape can be traced. In other words, coupled with an eye-tracking device, this apparatus enables "eye writing", which appears to be an original object of study. We adapt a previous model of reading (...)
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  36. Bernard J. Baars, Uma Ramamurthy & Stan Franklin (2007). How Deliberate, Spontaneous, and Unwanted Memories Emerge in a Computational Model of Consciousness. In John H. Mace (ed.), Involuntary Memory. New Perspectives in Cognitive Psychology. Blackwell Publishing. 177-207.score: 154.0
  37. Shimon Edelman, (Object Recognition/Multidimensional Scaling/Computational Model).score: 152.0
    differentiaily rated pairwise similarity when confronted with two pairs of objects, each revolving in a separate window on a computer screen. Subject data were pooled using individually weighted MDS (ref. 11; in all the experiments, the solutions were consistent among subjects). In each trial, the subject had to select among two pairs of shapes the one consisting of the most similar shapes. The subjects were allowed to respond at will; most responded within 10 sec. Proximity (that is, perceived similarity) tables (...)
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  38. Scherer & Kr (2010). The Component Process Model: A Blueprint for a Comprehensive Computational Model of Emotion. In Klaus R. Scherer, Tanja Bänziger & Etienne Roesch (eds.), A Blueprint for Affective Computing: A Sourcebook and Manual. Oup Oxford.score: 152.0
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  39. Christopher M. Aanstoos (1987). A Critique of the Computational Model of Thought: The Contribution of Merleau-Ponty. Journal of Phenomenological Psychology 18 (1):187-200.score: 150.0
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  40. Janusz A. Starzyk & Dilip K. Prasad (2011). A Computational Model of Machine Consciousness. International Journal of Machine Consciousness 3 (02):255-281.score: 150.0
  41. F. S. Perotto (2013). A Computational Constructivist Model as an Anticipatory Learning Mechanism for Coupled Agent–Environment Systems. Constructivist Foundations 9 (1):46-56.score: 150.0
    Context: The advent of a general artificial intelligence mechanism that learns like humans do would represent the realization of an old and major dream of science. It could be achieved by an artifact able to develop its own cognitive structures following constructivist principles. However, there is a large distance between the descriptions of the intelligence made by constructivist theories and the mechanisms that currently exist. Problem: The constructivist conception of intelligence is very powerful for explaining how cognitive development takes place. (...)
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  42. Claire O'Laughlin & Paul Thagard (2000). Autism and Coherence: A Computational Model. Mind and Language 15 (4):375–392.score: 150.0
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  43. Bart Jansen & Jan Cornelis (2012). The Action Game: A Computational Model for Learning Repertoires of Goals and Vocabularies to Express Them in a Population of Agents. Interaction Studies 13 (2):285-313.score: 150.0
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  44. William Demopoulos (1980). A Remark on the Completeness of the Computational Model of Mind. Behavioral and Brain Sciences 3 (1):135.score: 150.0
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  45. Miles Groth (1995). Psychology and Nihilism. A Genealogical Critique of the Computational Model of Mind. Review of Metaphysics 48 (4):894-895.score: 150.0
  46. Gary S. Dell, Myrna F. Schwartz, Nazbanou Nozari, Olufunsho Faseyitan & H. Branch Coslett (2013). Voxel-Based Lesion-Parameter Mapping: Identifying the Neural Correlates of a Computational Model of Word Production. Cognition 128 (3):380-396.score: 150.0
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  47. Jane Duran (1989). Epistemics: Epistemic Justification Theory Naturalized and the Computational Model of Mind. University Press of America.score: 150.0
    The third and fourth chapters of this work are devoted directly to that effort.
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  48. Okko Räsänen (2011). A Computational Model of Word Segmentation From Continuous Speech Using Transitional Probabilities of Atomic Acoustic Events. Cognition 120 (2):149-176.score: 150.0
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  49. Yaron Silberman, Shlomo Bentin & Risto Miikkulainen (2007). Semantic Boost on Episodic Associations: An Empirically‐Based Computational Model. Cognitive Science 31 (4):645-671.score: 150.0
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