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

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  1.  1
    Shira Calamaro & Gaja Jarosz (2015). Learning General Phonological Rules From Distributional Information: A Computational Model. Cognitive Science 39 (3):647-666.
    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.  10
    Judith Gaspers & Philipp Cimiano (2014). A Computational Model for the Item‐Based Induction of Construction Networks. Cognitive Science 38 (2):439-488.
    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.  5
    Deb K. Roy & Alex P. Pentland (2002). Learning Words From Sights and Sounds: A Computational Model. Cognitive Science 26 (1):113-146.
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  4.  1
    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.
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  5. Richard Double (1987). The Computational Model of the Mind and Philosophical Functionalism. Behaviorism 15 (2):131-39.
    A distinction between the use of computational models in cognitive science and a philosophically inspired reductivist thesis is developed. PF is found questionable for phenomenal states, and, by analogy, dubious for the nonphenomenal introspectible mental states of common sense. PF is also shown to be threatened for the sub-cognitive theoretical states of cognitive science by the work of the so-called New Connectionists. CMM is shown to be less vulnerable to these criticisms.
     
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  6.  29
    Afsaneh Fazly, Afra Alishahi & Suzanne Stevenson (2010). A Probabilistic Computational Model of Cross-Situational Word Learning. Cognitive Science 34 (6):1017-1063.
    Words are the essence of communication: They are the building blocks of any language. Learning the meaning of words is thus one of the most important aspects of language acquisition: Children must first learn words before they can combine them into complex utterances. Many theories have been developed to explain the impressive efficiency of young children in acquiring the vocabulary of their language, as well as the developmental patterns observed in the course of lexical acquisition. A major source of disagreement (...)
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  7.  7
    Peter Blouw, Eugene Solodkin, Paul Thagard & Chris Eliasmith (2016). Concepts as Semantic Pointers: A Framework and Computational Model. Cognitive Science 40 (5):1128-1162.
    The reconciliation of theories of concepts based on prototypes, exemplars, and theory-like structures is a longstanding problem in cognitive science. In response to this problem, researchers have recently tended to adopt either hybrid theories that combine various kinds of representational structure, or eliminative theories that replace concepts with a more finely grained taxonomy of mental representations. In this paper, we describe an alternative approach involving a single class of mental representations called “semantic pointers.” Semantic pointers are symbol-like representations that result (...)
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  8.  17
    Peter Blouw, Eugene Solodkin, Paul Thagard & Chris Eliasmith (2015). Concepts as Semantic Pointers: A Framework and Computational Model. Cognitive Science 40 (1):n/a-n/a.
    The reconciliation of theories of concepts based on prototypes, exemplars, and theory-like structures is a longstanding problem in cognitive science. In response to this problem, researchers have recently tended to adopt either hybrid theories that combine various kinds of representational structure, or eliminative theories that replace concepts with a more finely grained taxonomy of mental representations. In this paper, we describe an alternative approach involving a single class of mental representations called “semantic pointers.” Semantic pointers are symbol-like representations that result (...)
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  9.  27
    Thor Grünbaum (forthcoming). The Perception-Action Model: Counting Computational Mechanisms. Mind and Language.
    Milner and Goodale’s Two Visual Systems Hypothesis (TVSH) is regarded as common ground in recent discussions of visual consciousness. A central part of TVSH is a functional model of vision and action (a functional perception-action model, PAM for short). In this paper, I provide a brief overview of these current discussions and argue that PAM is ambiguous between a strong and a weak version. I argue that, given a standard way of individuating computational mechanisms, the available evidence (...)
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  10.  48
    Jakub Szymanik & Marcin Zajenkowski (2009). Comprehension of Simple Quantifiers. Empirical Evaluation of a Computational Model. Cognitive Science: A Multidisciplinary Journal 34 (3):521-532.
    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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  11.  4
    Justine T. Kao, Roger Levy & Noah D. Goodman (2016). A Computational Model of Linguistic Humor in Puns. Cognitive Science 40 (5):1270-1285.
    Humor plays an essential role in human interactions. Precisely what makes something funny, however, remains elusive. While research on natural language understanding has made significant advancements in recent years, there has been little direct integration of humor research with computational models of language understanding. In this paper, we propose two information-theoretic measures—ambiguity and distinctiveness—derived from a simple model of sentence processing. We test these measures on a set of puns and regular sentences and show that they correlate significantly (...)
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  12.  14
    Justine T. Kao, Roger Levy & Noah D. Goodman (2015). A Computational Model of Linguistic Humor in Puns. Cognitive Science 40 (1).
    Humor plays an essential role in human interactions. Precisely what makes something funny, however, remains elusive. While research on natural language understanding has made significant advancements in recent years, there has been little direct integration of humor research with computational models of language understanding. In this paper, we propose two information-theoretic measures—ambiguity and distinctiveness—derived from a simple model of sentence processing. We test these measures on a set of puns and regular sentences and show that they correlate significantly (...)
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  13. Alejandro Secades (2015). A Computational Model of Pragma-Dialectics as a Tool for its Analysis and Evaluation. Informal Logic 35 (3):342-377.
    The overall goal of this paper is to show that computational modelling of argumentation theories is a useful tool to deepen them. Specifically, it provides a basic computational formalization of part of Pragma-dialectics’ model of a critical discussion, which serves as a basis for analyzing this influential theory of argumentation. Such analysis reveals some weaknesses and leaves some questions opened for Pragma-dialectics. Particularly, it shows that the model of a critical discussion is not independent of the (...)
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  14.  41
    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.
    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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  15.  2
    Glenn Gunzelmann (2008). Strategy Generalization Across Orientation Tasks: Testing a Computational Cognitive Model. Cognitive Science 32 (5):835-861.
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  16.  27
    David Croft & Paul Thagard, Dynamic Imagery: A Computational Model of Motion and Visual Analogy.
    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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  17.  5
    Bruno G. Bara, Monica Bucciarelli & Vincenzo Lombardo (2001). Model Theory of Deduction: A Unified Computational Approach. Cognitive Science 25 (6):839-901.
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  18.  18
    Y. Sato, H. Iizuka & T. Ikegami (2013). Investigating Extended Embodiment Using a Computational Model and Human Experimentation. Constructivist Foundations 9 (1):73-84.
    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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  19.  1
    Jane Duran (1989). Epistemics: Epistemic Justification Theory Naturalized and the Computational Model of Mind. Upa.
    This author explores the intersection between cognitive science, as exemplified by the computational model of mind, and epistemologyó specifically, epistemic justification theory. Her analysis leads to the conclusion that some very specific and somewhat technical issues in epistemic justification theory can be at least partially resolved, if not entirely cleared up, by the use of the computational model. The third and fourth chapters of this work are devoted directly to that effort. Chapter one examines in detail (...)
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  20.  7
    Mare Koit & Haldur Õim (2014). A Computational Model of Argumentation in Agreement Negotiation Processes. Argument and Computation 6 (2):101-129.
    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. Our (...) is based on the studies in the common-sense conception of how the human mind works in such situations. Theoretical considerations are followed by an analysis of Estonian spoken human–human dialogues. First, calls of clients to travel agencies are studied where a travel agent could use various arguments in order to persuade a client to book a trip. The analysis demonstrates that clients are primarily looking for information; argumentation occurs in a small number of dialogues. Secondly, calls of s... (shrink)
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  21.  12
    L. Gabora (1995). Meme and Variations: A Computational Model of Cultural Evolution. In [Book Chapter].
    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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  22.  88
    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.
    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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  23.  7
    Eleanor Olds Batchelder (2002). Bootstrapping the Lexicon: A Computational Model of Infant Speech Segmentation. Cognition 83 (2):167-206.
    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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  24.  3
    María Teresa Signes Pont, Higinio Mora Mora, Gregorio De Miguel Casado & David Gil Méndez (2016). A Computational Model of the Belief System Under the Scope of Social Communication. Foundations of Science 21 (1):215-223.
    This paper presents an approach to the belief system based on a computational framework in three levels: first, the logic level with the definition of binary local rules, second, the arithmetic level with the definition of recursive functions and finally the behavioural level with the definition of a recursive construction pattern. Social communication is achieved when different beliefs are expressed, modified, propagated and shared through social nets. This approach is useful to mimic the belief system because the defined functions (...)
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  25.  5
    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.
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  26.  4
    Afra Alishahi & Suzanne Stevenson (2008). A Computational Model of Early Argument Structure Acquisition. Cognitive Science 32 (5):789-834.
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  27.  3
    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.
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  28.  27
    Nicola Angius & Guglielmo Tamburrini (2011). Scientific Theories of Computational Systems in Model Checking. Minds and Machines 21 (2):323-336.
    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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  29.  19
    L. Karl Branting (1993). A Computational Model of Ratio Decidendi. Artificial Intelligence and Law 2 (1):1-31.
    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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  30.  7
    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):263-284.
    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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  31.  8
    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.
    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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  32.  11
    B. Scott & A. Bansal (2013). A Cybernetic Computational Model for Learning and Skill Acquisition. Constructivist Foundations 9 (1):125-136.
    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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  33.  20
    Steven Walczak (2002). A Context-Based Computational Model of Language Acquisition by Infants and Children. Foundations of Science 7 (4):393-411.
    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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  34.  18
    Akira Utsumi (2011). Computational Exploration of Metaphor Comprehension Processes Using a Semantic Space Model. Cognitive Science 35 (2):251-296.
    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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  35.  14
    Sorinel A. Oprisan & Ana Oprisan (2006). A Computational Model of Oncogenesis Using the Systemic Approach. Axiomathes 16 (1-2):155-163.
    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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  36.  10
    François Guillaud & Patrick Hannaert (2010). A Computational Model of the Circulating Renin-Angiotensin System and Blood Pressure Regulation. Acta Biotheoretica 58 (2):143-170.
    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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  37.  1
    González Carbonell Raide Alfonso & Nápoles Padrón (2014). Interdisciplinary character of the computational model in the solution of health problems. Humanidades Médicas 14 (3):646-658.
    La solución de los problemas de salud requiere de la interrelación entre diferentes ramas científicas y tecnológicas, tales como la Biomecánica, las tecnologías de la información, la ingeniería y las ciencias médicas. La importancia de la comunicación interdisciplinaria en las universidades fundamenta la creación de un grupo de investigación que fusiona el conocimiento de la mecánica clásica con las disciplinas de la salud y la participación de pacientes, técnicos, ingenieros, médicos y estudiantes. En el trabajo se realiza una reflexión sobre (...)
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  38.  16
    Theo Vosse & Gerard Kempen (2000). Syntactic Structure Assembly in Human Parsing: A Computational Model Based on Competitive Inhibition and a Lexicalist Grammar. Cognition 75 (2):105-143.
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  39. Vivian V. Valentin, W. Todd Maddox & F. Gregory Ashby (2014). A Computational Model of the Temporal Dynamics of Plasticity in Procedural Learning: Sensitivity to Feedback Timing. Frontiers in Psychology 5.
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  40.  32
    Janusz A. Starzyk & Dilip K. Prasad (2011). A Computational Model of Machine Consciousness. International Journal of Machine Consciousness 3 (02):255-281.
  41. Christina Bergmann, Louis ten Bosch, Paula Fikkert & Lou Boves (2013). A Computational Model to Investigate Assumptions in the Headturn Preference Procedure. Frontiers in Psychology 4.
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  42. Chris Blais, Serje Robidoux, Evan F. Risko & Derek Besner (2007). Item-Specific Adaptation and the Conflict-Monitoring Hypothesis: A Computational Model. Psychological Review 114 (4):1076-1086.
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  43.  1
    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.
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  44. Antje Nuthmann, Tim J. Smith, Ralf Engbert & John M. Henderson (2010). CRISP: A Computational Model of Fixation Durations in Scene Viewing. Psychological Review 117 (2):382-405.
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  45. Marco Ragni & Markus Knauff (2013). A Theory and a Computational Model of Spatial Reasoning with Preferred Mental Models. Psychological Review 120 (3):561-588.
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  46. Thomas V. Wiecki & Michael J. Frank (2013). A Computational Model of Inhibitory Control in Frontal Cortex and Basal Ganglia. Psychological Review 120 (2):329-355.
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  47.  4
    Hermina J. M. Tabachneck‐Schijf, Anthony M. Leonardo & Herbert A. Simon (1997). CaMeRa: A Computational Model of Multiple Representations. Cognitive Science 21 (3):305-350.
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  48.  91
    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.
  49.  7
    Okko Räsänen (2011). A Computational Model of Word Segmentation From Continuous Speech Using Transitional Probabilities of Atomic Acoustic Events. Cognition 120 (2):149-176.
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  50. Tao Gong, Jinyun Ke, James W. Minett, John H. Holland & William S. Y. Wang (2005). A Computational Model of the Coevolution of Lexicon and Syntax. Complexity 10:50-62.
     
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