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  1. Bayesian Cognitive Science, Predictive Brains, and the Nativism Debate.Matteo Colombo - 2018 - Synthese 195 (11):4817-4838.
    The rise of Bayesianism in cognitive science promises to shape the debate between nativists and empiricists into more productive forms—or so have claimed several philosophers and cognitive scientists. The present paper explicates this claim, distinguishing different ways of understanding it. After clarifying what is at stake in the controversy between nativists and empiricists, and what is involved in current Bayesian cognitive science, the paper argues that Bayesianism offers not a vindication of either nativism or empiricism, but one way to talk (...)
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  • Building Machines That Learn and Think Like People.Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum & Samuel J. Gershman - 2017 - Behavioral and Brain Sciences 40.
    Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking (...)
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  • Concepts.Eric Margolis & Stephen Laurence - 2011 - Stanford Encyclopedia of Philosophy.
    This entry provides an overview of theories of concepts that is organized around five philosophical issues: (1) the ontology of concepts, (2) the structure of concepts, (3) empiricism and nativism about concepts, (4) concepts and natural language, and (5) concepts and conceptual analysis.
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  • Associationist Theories of Thought.Eric Mandelbaum - 2015 - Stanford Encyclopedia of Philosophy.
  • New Frontiers in Language Evolution and Development.D. Kimbrough Oller, Rick Dale & Ulrike Griebel - 2016 - Topics in Cognitive Science 8 (2):353-360.
    This article introduces the Special Issue and its focus on research in language evolution with emphasis on theory as well as computational and robotic modeling. A key theme is based on the growth of evolutionary developmental biology or evo-devo. The Special Issue consists of 13 articles organized in two sections: A) Theoretical foundations and B) Modeling and simulation studies. All the papers are interdisciplinary in nature, encompassing work in biological and linguistic foundations for the study of language evolution as well (...)
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  • Quasiregularity and Its Discontents: The Legacy of the Past Tense Debate.Mark S. Seidenberg & David C. Plaut - 2014 - Cognitive Science 38 (6):1190-1228.
    Rumelhart and McClelland's chapter about learning the past tense created a degree of controversy extraordinary even in the adversarial culture of modern science. It also stimulated a vast amount of research that advanced the understanding of the past tense, inflectional morphology in English and other languages, the nature of linguistic representations, relations between language and other phenomena such as reading and object recognition, the properties of artificial neural networks, and other topics. We examine the impact of the Rumelhart and McClelland (...)
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  • Theory-Driven Modeling or Model-Driven Theorizing? Comment on McClelland Et Al. And Griffiths Et Al.David E. Huber & Rosemary A. Cowell - 2010 - Trends in Cognitive Sciences 14 (8):343-344.
  • Learning the Generative Principles of a Symbol System From Limited Examples.Lei Yuan, Violet Xiang, David Crandall & Linda Smith - 2020 - Cognition 200:104243.
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  • A Neural Dynamic Model of the Perceptual Grounding of Spatial and Movement Relations.Mathis Richter, Jonas Lins & Gregor Schöner - 2021 - Cognitive Science 45 (10):e13045.
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  • Learning From the Body About the Mind.Michael A. Riley, Kevin Shockley & Guy Van Orden - 2012 - Topics in Cognitive Science 4 (1):21-34.
    In some areas of cognitive science we are confronted with ultrafast cognition, exquisite context sensitivity, and scale-free variation in measured cognitive activities. To move forward, we suggest a need to embrace this complexity, equipping cognitive science with tools and concepts used in the study of complex dynamical systems. The science of movement coordination has benefited already from this change, successfully circumventing analogous paradoxes by treating human activities as phenomena of self-organization. Therein, action and cognition are seen to be emergent in (...)
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  • On Quantum Models of the Human Mind.Hongbin Wang & Yanlong Sun - 2014 - Topics in Cognitive Science 6 (1):98-103.
    Recent years have witnessed rapidly increasing interests in developing quantum theoretical models of human cognition. Quantum mechanisms have been taken seriously to describe how the mind reasons and decides. Papers in this special issue report the newest results in the field. Here we discuss why the two levels of commitment, treating the human brain as a quantum computer and merely adopting abstract quantum probability principles to model human cognition, should be integrated. We speculate that quantum cognition models gain greater modeling (...)
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  • Birth of an Abstraction: A Dynamical Systems Account of the Discovery of an Elsewhere Principle in a Category Learning Task.Whitney Tabor, Pyeong W. Cho & Harry Dankowicz - 2013 - Cognitive Science 37 (7):1193-1227.
    Human participants and recurrent (“connectionist”) neural networks were both trained on a categorization system abstractly similar to natural language systems involving irregular (“strong”) classes and a default class. Both the humans and the networks exhibited staged learning and a generalization pattern reminiscent of the Elsewhere Condition (Kiparsky, 1973). Previous connectionist accounts of related phenomena have often been vague about the nature of the networks’ encoding systems. We analyzed our network using dynamical systems theory, revealing topological and geometric properties that can (...)
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  • Emergence in Cognitive Science.James L. McClelland - 2010 - Topics in Cognitive Science 2 (4):751-770.
    The study of human intelligence was once dominated by symbolic approaches, but over the last 30 years an alternative approach has arisen. Symbols and processes that operate on them are often seen today as approximate characterizations of the emergent consequences of sub- or nonsymbolic processes, and a wide range of constructs in cognitive science can be understood as emergents. These include representational constructs (units, structures, rules), architectural constructs (central executive, declarative memory), and developmental processes and outcomes (stages, sensitive periods, neurocognitive (...)
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  • Do Children Use Multi‐Word Information in Real‐Time Sentence Comprehension?Rana Abu-Zhaya, Inbal Arnon & Arielle Borovsky - 2022 - Cognitive Science 46 (3):e13111.
    Cognitive Science, Volume 46, Issue 3, March 2022.
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  • Complex Communication Dynamics: Exploring the Structure of an Academic Talk.Camila Alviar, Rick Dale & Alexia Galati - 2019 - Cognitive Science 43 (3):e12718.
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  • The New Tweety Puzzle: Arguments Against Monistic Bayesian Approaches in Epistemology and Cognitive Science.Matthias Unterhuber & Gerhard Schurz - 2013 - Synthese 190 (8):1407-1435.
    In this paper we discuss the new Tweety puzzle. The original Tweety puzzle was addressed by approaches in non-monotonic logic, which aim to adequately represent the Tweety case, namely that Tweety is a penguin and, thus, an exceptional bird, which cannot fly, although in general birds can fly. The new Tweety puzzle is intended as a challenge for probabilistic theories of epistemic states. In the first part of the paper we argue against monistic Bayesians, who assume that epistemic states can (...)
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  • The Computational and Neural Basis of Cognitive Control: Charted Territory and New Frontiers.Matthew M. Botvinick - 2014 - Cognitive Science 38 (6):1249-1285.
    Cognitive control has long been one of the most active areas of computational modeling work in cognitive science. The focus on computational models as a medium for specifying and developing theory predates the PDP books, and cognitive control was not one of the areas on which they focused. However, the framework they provided has injected work on cognitive control with new energy and new ideas. On the occasion of the books' anniversary, we review computational modeling in the study of cognitive (...)
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  • Parallel Distributed Processing at 25: Further Explorations in the Microstructure of Cognition.Timothy T. Rogers & James L. McClelland - 2014 - Cognitive Science 38 (6):1024-1077.
    This paper introduces a special issue of Cognitive Science initiated on the 25th anniversary of the publication of Parallel Distributed Processing (PDP), a two-volume work that introduced the use of neural network models as vehicles for understanding cognition. The collection surveys the core commitments of the PDP framework, the key issues the framework has addressed, and the debates the framework has spawned, and presents viewpoints on the current status of these issues. The articles focus on both historical roots and contemporary (...)
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  • Rational Use of Cognitive Resources: Levels of Analysis Between the Computational and the Algorithmic.Thomas L. Griffiths, Falk Lieder & Noah D. Goodman - 2015 - Topics in Cognitive Science 7 (2):217-229.
    Marr's levels of analysis—computational, algorithmic, and implementation—have served cognitive science well over the last 30 years. But the recent increase in the popularity of the computational level raises a new challenge: How do we begin to relate models at different levels of analysis? We propose that it is possible to define levels of analysis that lie between the computational and the algorithmic, providing a way to build a bridge between computational- and algorithmic-level models. The key idea is to push the (...)
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  • The Dynamics of Neural Populations Capture the Laws of the Mind.Gregor Schöner - 2020 - Topics in Cognitive Science 12 (4):1257-1271.
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  • Programs as Causal Models: Speculations on Mental Programs and Mental Representation.Nick Chater & Mike Oaksford - 2013 - Cognitive Science 37 (6):1171-1191.
    Judea Pearl has argued that counterfactuals and causality are central to intelligence, whether natural or artificial, and has helped create a rich mathematical and computational framework for formally analyzing causality. Here, we draw out connections between these notions and various current issues in cognitive science, including the nature of mental “programs” and mental representation. We argue that programs (consisting of algorithms and data structures) have a causal (counterfactual-supporting) structure; these counterfactuals can reveal the nature of mental representations. Programs can also (...)
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  • On the Necessity of U-Shaped Learning.Lorenzo Carlucci & John Case - 2013 - Topics in Cognitive Science 5 (1):56-88.
    A U-shaped curve in a cognitive-developmental trajectory refers to a three-step process: good performance followed by bad performance followed by good performance once again. U-shaped curves have been observed in a wide variety of cognitive-developmental and learning contexts. U-shaped learning seems to contradict the idea that learning is a monotonic, cumulative process and thus constitutes a challenge for competing theories of cognitive development and learning. U-shaped behavior in language learning (in particular in learning English past tense) has become a central (...)
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  • The Emergence of Organizing Structure in Conceptual Representation.Brenden M. Lake, Neil D. Lawrence & Joshua B. Tenenbaum - 2018 - Cognitive Science 42 (S3):809-832.
    Both scientists and children make important structural discoveries, yet their computational underpinnings are not well understood. Structure discovery has previously been formalized as probabilistic inference about the right structural form—where form could be a tree, ring, chain, grid, etc.. Although this approach can learn intuitive organizations, including a tree for animals and a ring for the color circle, it assumes a strong inductive bias that considers only these particular forms, and each form is explicitly provided as initial knowledge. Here we (...)
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  • Is the Mystery of Thought Demystified by Context‐Dependent Categorisation? Towards a New Relation Between Language and Thought.Michael S. C. Thomas, Harry R. M. Purser & Denis Mareschal - 2012 - Mind and Language 27 (5):595-618.
    We argue that are no such things as literal categories in human cognition. Instead, we argue that there are merely temporary coalescences of dimensions of similarity, which are brought together by context in order to create the similarity structure in mental representations appropriate for the task at hand. Fodor contends that context‐sensitive cognition cannot be realised by current computational theories of mind. We address this challenge by describing a simple computational implementation that exhibits internal knowledge representations whose similarity structure alters (...)
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  • Utility Maximization and Bounds on Human Information Processing.Andrew Howes, Richard L. Lewis & Satinder Singh - 2014 - Topics in Cognitive Science 6 (2):198-203.
    Utility maximization is a key element of a number of theoretical approaches to explaining human behavior. Among these approaches are rational analysis, ideal observer theory, and signal detection theory. While some examples of these approaches define the utility maximization problem with little reference to the bounds imposed by the organism, others start with, and emphasize approaches in which bounds imposed by the information processing architecture are considered as an explicit part of the utility maximization problem. These latter approaches are the (...)
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  • Modeling the Structure and Dynamics of Semantic Processing.Armand S. Rotaru, Gabriella Vigliocco & Stefan L. Frank - 2018 - Cognitive Science 42 (8):2890-2917.
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  • Grounding Cognitive‐Level Processes in Behavior: The View From Dynamic Systems Theory.Larissa K. Samuelson, Gavin W. Jenkins & John P. Spencer - 2015 - Topics in Cognitive Science 7 (2):191-205.
    Marr's seminal work laid out a program of research by specifying key questions for cognitive science at different levels of analysis. Because dynamic systems theory focuses on time and interdependence of components, DST research programs come to very different conclusions regarding the nature of cognitive change. We review a specific DST approach to cognitive-level processes: dynamic field theory. We review research applying DFT to several cognitive-level processes: object permanence, naming hierarchical categories, and inferring intent, that demonstrate the difference in understanding (...)
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  • Bayesian Models of Cognition: What's Built in After All?Amy Perfors - 2012 - Philosophy Compass 7 (2):127-138.
    This article explores some of the philosophical implications of the Bayesian modeling paradigm. In particular, it focuses on the ramifications of the fact that Bayesian models pre‐specify an inbuilt hypothesis space. To what extent does this pre‐specification correspond to simply ‘‘building the solution in''? I argue that any learner must have a built‐in hypothesis space in precisely the same sense that Bayesian models have one. This has implications for the nature of learning, Fodor's puzzle of concept acquisition, and the role (...)
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  • Learning Orthographic Structure With Sequential Generative Neural Networks.Alberto Testolin, Ivilin Stoianov, Alessandro Sperduti & Marco Zorzi - 2016 - Cognitive Science 40 (3):579-606.
    Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine, a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual (...)
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  • The Dynamics of Group Cognition.S. Orestis Palermos - 2016 - Minds and Machines 26 (4):409-440.
    The aim of this paper is to demonstrate that the postulation of irreducible, distributed cognitive systems is necessary for the successful explanatory practice of cognitive science and sociology. Towards this end, and with an eye specifically on the phenomenon of distributed cognition, the debate over reductionism versus emergence is examined from the perspective of Dynamical Systems Theory. The motivation for this novel approach is threefold. Firstly, DST is particularly popular amongst cognitive scientists who work on modelling collective behaviors. Secondly, DST (...)
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  • Interpretations Without Justification: A General Argument Against Morgan’s Canon.Tobias Starzak - 2017 - Synthese 194 (5).
    In this paper I critically discuss and, in the end, reject Morgan’s Canon, a popular principle in comparative psychology. According to this principle we should always prefer explanations of animal behavior in terms of lower psychological processes over explanations in terms of higher psychological processes, when alternative explanations are possible. The validity of the principle depends on two things, a clear understanding of what it means for psychological processes to be higher or lower relative to each other and a justification (...)
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  • The Concrete Universal and Cognitive Science.Richard Shillcock - 2014 - Axiomathes 24 (1):63-80.
    Cognitive science depends on abstractions made from the complex reality of human behaviour. Cognitive scientists typically wish the abstractions in their theories to be universals, but seldom attend to the ontology of universals. Two sorts of universal, resulting from Galilean abstraction and materialist abstraction respectively, are available in the philosophical literature: the abstract universal—the one-over-many universal—is the universal conventionally employed by cognitive scientists; in contrast, a concrete universal is a material entity that can appear within the set of entities it (...)
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  • The Computational Origin of Representation.Steven T. Piantadosi - 2021 - Minds and Machines 31 (1):1-58.
    Each of our theories of mental representation provides some insight into how the mind works. However, these insights often seem incompatible, as the debates between symbolic, dynamical, emergentist, sub-symbolic, and grounded approaches to cognition attest. Mental representations—whatever they are—must share many features with each of our theories of representation, and yet there are few hypotheses about how a synthesis could be possible. Here, I develop a theory of the underpinnings of symbolic cognition that shows how sub-symbolic dynamics may give rise (...)
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  • Are We Predictive Engines? Perils, Prospects, and the Puzzle of the Porous Perceiver.Andy Clark - 2013 - Behavioral and Brain Sciences 36 (3):233-253.
    The target article sketched and explored a mechanism (action-oriented predictive processing) most plausibly associated with core forms of cortical processing. In assessing the attractions and pitfalls of the proposal we should keep that element distinct from larger, though interlocking, issues concerning the nature of adaptive organization in general.
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  • Using Neural Networks to Generate Inferential Roles for Natural Language.Peter Blouw & Chris Eliasmith - 2018 - Frontiers in Psychology 8.
  • A Tutorial Introduction to Bayesian Models of Cognitive Development.Amy Perfors, Joshua B. Tenenbaum, Thomas L. Griffiths & Fei Xu - 2011 - Cognition 120 (3):302-321.
  • Bayesian Reverse-Engineering Considered as a Research Strategy for Cognitive Science.Carlos Zednik & Frank Jäkel - 2016 - Synthese 193 (12):3951-3985.
    Bayesian reverse-engineering is a research strategy for developing three-level explanations of behavior and cognition. Starting from a computational-level analysis of behavior and cognition as optimal probabilistic inference, Bayesian reverse-engineers apply numerous tweaks and heuristics to formulate testable hypotheses at the algorithmic and implementational levels. In so doing, they exploit recent technological advances in Bayesian artificial intelligence, machine learning, and statistics, but also consider established principles from cognitive psychology and neuroscience. Although these tweaks and heuristics are highly pragmatic in character and (...)
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  • Sensorimotor Grounding of Musical Embodiment and the Role of Prediction: A Review.Pieter-Jan Maes - 2016 - Frontiers in Psychology 7.
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  • Phonological Reduplication in Sign Language: Rules Rule.Iris Berent, Amanda Dupuis & Diane Brentari - 2014 - Frontiers in Psychology 5.
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  • Losing the Boundary: Cognition Biases Action Well After Action Selection.Cristian Buc Calderon, Tom Verguts & Wim Gevers - 2015 - Journal of Experimental Psychology: General 144 (4):737-743.
  • Empiricism Without Magic: Transformational Abstraction in Deep Convolutional Neural Networks.Cameron Buckner - 2018 - Synthese (12):1-34.
    In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing to (...)
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  • Bayesian Analogy with Relational Transformations.Hongjing Lu, Dawn Chen & Keith J. Holyoak - 2012 - Psychological Review 119 (3):617-648.
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  • A World Unto Itself: Human Communication as Active Inference.Jared Vasil, Paul B. Badcock, Axel Constant, Karl Friston & Maxwell J. D. Ramstead - 2020 - Frontiers in Psychology 11.
  • Connecting Twenty-First Century Connectionism and Wittgenstein.Charles W. Lowney, Simon D. Levy, William Meroney & Ross W. Gayler - 2020 - Philosophia 48 (2):643-671.
    By pointing to deep philosophical confusions endemic to cognitive science, Wittgenstein might seem an enemy of computational approaches. We agree that while Wittgenstein would reject the classicist’s symbols and rules approach, his observations align well with connectionist or neural network approaches. While many connectionisms that dominated the later twentieth century could fall prey to criticisms of biological, pedagogical, and linguistic implausibility, current connectionist approaches can resolve those problems in a Wittgenstein-friendly manner. We present the basics of a Vector Symbolic Architecture (...)
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  • The Challenge of Modeling the Acquisition of Mathematical Concepts.Alberto Testolin - 2020 - Frontiers in Human Neuroscience 14.
  • Bayesian Cognitive Science, Predictive Brains, and the Nativism Debate.Matteo Colombo - 2017 - Synthese:1-22.
    The rise of Bayesianism in cognitive science promises to shape the debate between nativists and empiricists into more productive forms—or so have claimed several philosophers and cognitive scientists. The present paper explicates this claim, distinguishing different ways of understanding it. After clarifying what is at stake in the controversy between nativists and empiricists, and what is involved in current Bayesian cognitive science, the paper argues that Bayesianism offers not a vindication of either nativism or empiricism, but one way to talk (...)
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  • Neural Plasticity and Concepts Ontogeny.Alessio Plebe & Marco Mazzone - 2016 - Synthese 193 (12):3889-3929.
    Neural plasticity has been invoked as a powerful argument against nativism. However, there is a line of argument, which is well exemplified by Pinker and more recently by Laurence and Margolis The conceptual mind: new directions in the study of concepts, MIT, Cambridge, 2015) with respect to concept nativism, according to which even extreme cases of plasticity show important innate constraints, so that one should rather speak of “constrained plasticity”. According to this view, cortical areas are not really equipotential, they (...)
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  • Analogy as a Catalyst for Cumulative Cultural Evolution.C. O. Brand, A. Mesoudi & P. E. Smaldino - 2021 - Trends in Cognitive Sciences 25 (6):450-461.
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  • Model-Based Theorizing in Cognitive Neuroscience.Elizabeth Irvine - 2016 - British Journal for the Philosophy of Science 67 (1):143-168.
    Weisberg and Godfrey-Smith distinguish between two forms of theorising: data-driven ‘abstract direct representation’ and modeling. The key difference is that when using a data-driven approach, theories are intended to represent specific phenomena, so directly represent them, while models may not be intended to represent anything, so represent targets indirectly, if at all. The aim here is to compare and analyse these practices, in order to outline an account of model-based theorising that involves direct representational relationships. This is based on the (...)
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  • Modeling Language and Cognition with Deep Unsupervised Learning: A Tutorial Overview.Marco Zorzi, Alberto Testolin & Ivilin P. Stoianov - 2013 - Frontiers in Psychology 4.