Switch to: References

Add citations

You must login to add citations.
  1. 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 (...)
    Direct download (5 more)  
     
    Export citation  
     
    Bookmark   4 citations  
  • Skepticism and the Acquisition of “Knowledge”.Shaun Nichols & N. Ángel Pinillos - 2018 - Mind and Language 33 (4):397-414.
    Do you know you are not being massively deceived by an evil demon? That is a familiar skeptical challenge. Less familiar is this question: How do you have a conception of knowledge on which the evil demon constitutes a prima facie challenge? Recently several philosophers have suggested that our responses to skeptical scenarios can be explained in terms of heuristics and biases. We offer an alternative explanation, based in learning theory. We argue that, given the evidence available to the learner, (...)
    Direct download (4 more)  
     
    Export citation  
     
    Bookmark   2 citations  
  • Troubles with Bayesianism: An Introduction to the Psychological Immune System.Eric Mandelbaum - 2019 - Mind and Language 34 (2):141-157.
    A Bayesian mind is, at its core, a rational mind. Bayesianism is thus well-suited to predict and explain mental processes that best exemplify our ability to be rational. However, evidence from belief acquisition and change appears to show that we do not acquire and update information in a Bayesian way. Instead, the principles of belief acquisition and updating seem grounded in maintaining a psychological immune system rather than in approximating a Bayesian processor.
    Direct download (6 more)  
     
    Export citation  
     
    Bookmark   1 citation  
  • Parameters as Trait Indicators: Exploring a Complementary Neurocomputational Approach to Conceptualizing and Measuring Trait Differences in Emotional Intelligence.Ryan Smith, Anna Alkozei & William D. S. Killgore - 2019 - Frontiers in Psychology 10.
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark  
  • Semi-Supervised Learning is Observed in a Speeded but Not an Unspeeded 2D Categorization Task.Timothy T. Rogers, Charles Kalish, Bryan R. Gibson, Joseph Harrison & Xiaojin Zhu - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society.
  • Bayesian Analogy with Relational Transformations.Hongjing Lu, Dawn Chen & Keith J. Holyoak - 2012 - Psychological Review 119 (3):617-648.
    No categories
    Direct download (4 more)  
     
    Export citation  
     
    Bookmark   4 citations  
  • Goal-Directed Decision Making as Probabilistic Inference: A Computational Framework and Potential Neural Correlates.Alec Solway & Matthew M. Botvinick - 2012 - Psychological Review 119 (1):120-154.
  • Modeling Cross-Situational Word–Referent Learning: Prior Questions.Chen Yu & Linda B. Smith - 2012 - Psychological Review 119 (1):21-39.
    Direct download (5 more)  
     
    Export citation  
     
    Bookmark   14 citations  
  • Structured Statistical Models of Inductive Reasoning.Charles Kemp & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (1):20-58.
  • Is It Better to Select or to Receive? Learning Via Active and Passive Hypothesis Testing.Douglas B. Markant & Todd M. Gureckis - 2014 - Journal of Experimental Psychology: General 143 (1):94-122.
  • Predicting Reasoning From Memory.Evan Heit & Brett K. Hayes - 2011 - Journal of Experimental Psychology: General 140 (1):76-101.
  • Talker-Specific Generalization of Pragmatic Inferences Based on Under- and Over-Informative Prenominal Adjective Use.Amanda Pogue, Chigusa Kurumada & Michael K. Tanenhaus - 2015 - Frontiers in Psychology 6.
    Direct download (5 more)  
     
    Export citation  
     
    Bookmark   3 citations  
  • Goldilocks Forgetting in Cross-Situational Learning.Paul Ibbotson, Diana G. López & Alan J. McKane - 2018 - Frontiers in Psychology 9.
    Direct download (4 more)  
     
    Export citation  
     
    Bookmark  
  • The Unrealized Promise of Infant Statistical Word–Referent Learning.Linda B. Smith, Sumarga H. Suanda & Chen Yu - 2014 - Trends in Cognitive Sciences 18 (5):251-258.
  • Generalization From Newly Learned Words Reveals Structural Properties of the Human Reading System.Blair C. Armstrong, Nicolas Dumay, Woojae Kim & Mark A. Pitt - 2017 - Journal of Experimental Psychology: General 146 (2):227-249.
    Direct download (5 more)  
     
    Export citation  
     
    Bookmark  
  • 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 (...)
    Direct download (7 more)  
     
    Export citation  
     
    Bookmark  
  • Beyond Single‐Level Accounts: The Role of Cognitive Architectures in Cognitive Scientific Explanation.Richard P. Cooper & David Peebles - 2015 - Topics in Cognitive Science 7 (2):243-258.
    We consider approaches to explanation within the cognitive sciences that begin with Marr's computational level or Marr's implementational level and argue that each is subject to fundamental limitations which impair their ability to provide adequate explanations of cognitive phenomena. For this reason, it is argued, explanation cannot proceed at either level without tight coupling to the algorithmic and representation level. Even at this level, however, we argue that additional constraints relating to the decomposition of the cognitive system into a set (...)
    Direct download (6 more)  
     
    Export citation  
     
    Bookmark   4 citations  
  • A Rational Analysis of Rule-Based Concept Learning.Noah Goodman, Joshua Tenenbaum, Jacob Feldman & Thomas Griffiths - 2008 - Cognitive Science 32 (1):108-154.
  • Pigeons Acquire Multiple Categories in Parallel Via Associative Learning: A Parallel to Human Word Learning?Edward A. Wasserman, Daniel I. Brooks & Bob McMurray - 2015 - Cognition 136:99-122.
    Direct download (5 more)  
     
    Export citation  
     
    Bookmark   2 citations  
  • 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 lexicon (...)
    Direct download (7 more)  
     
    Export citation  
     
    Bookmark   6 citations  
  • Infants Rapidly Learn Word-Referent Mappings Via Cross-Situational Statistics.Linda Smith & Chen Yu - 2008 - Cognition 106 (3):1558-1568.
    Direct download (11 more)  
     
    Export citation  
     
    Bookmark   73 citations  
  • Emergent Constraints on Word-Learning: A Computational Perspective.Terry Regier - 2003 - Trends in Cognitive Sciences 7 (6):263-268.
  • 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.
  • Differences in Preschoolers’ and Adults’ Use of Generics About Novel Animals and Artifacts: A Window Onto a Conceptual Divide.Amanda C. Brandone & Susan A. Gelman - 2009 - Cognition 110 (1):1-22.
    Children and adults commonly produce more generic noun phrases (e.g., birds fly) about animals than artifacts. This may reflect differences in participants’ generic knowledge about specific animals/artifacts (e.g., dogs/chairs), or it may reflect a more general distinction. To test this, the current experiments asked adults and preschoolers to generate properties about novel animals and artifacts (Experiment 1: real animals/artifacts; Experiments 2 and 3: matched pairs of maximally similar, novel animals/artifacts). Data demonstrate that even without prior knowledge about these items, the (...)
    Direct download (7 more)  
     
    Export citation  
     
    Bookmark   7 citations  
  • Inductive Reasoning About Causally Transmitted Properties.Patrick Shafto, Charles Kemp, Elizabeth Baraff Bonawitz, John D. Coley & Joshua B. Tenenbaum - 2008 - Cognition 109 (2):175-192.
  • Concept Innateness, Concept Continuity, and Bootstrapping.Susan Carey - 2011 - Behavioral and Brain Sciences 34 (3):152.
    The commentators raised issues relevant to all three important theses of The Origin of Concepts (henceforth TOOC). Some questioned the very existence of innate representational primitives, and others questioned my claims about their richness and whether they should be thought of as concepts. Some questioned the existence of conceptual discontinuity in the course of knowledge acquisition and others argued that discontinuity is much more common than was portrayed in TOOC. Some raised issues with my characterization of Quinian bootstrapping, and others (...)
    Direct download (5 more)  
     
    Export citation  
     
    Bookmark   2 citations  
  • Bayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adults.Thomas L. Griffiths, David M. Sobel, Joshua B. Tenenbaum & Alison Gopnik - 2011 - Cognitive Science 35 (8):1407-1455.
    People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using tasks in which (...)
    Direct download (13 more)  
     
    Export citation  
     
    Bookmark   5 citations  
  • Explaining Color Term Typology With an Evolutionary Model.Mike Dowman - 2007 - Cognitive Science 31 (1):99-132.
    Direct download (6 more)  
     
    Export citation  
     
    Bookmark   4 citations  
  • Probabilistic Models of Cognition: Conceptual Foundations.Nick Chater & Alan Yuille - 2006 - Trends in Cognitive Sciences 10 (7):287-291.
    Remarkable progress in the mathematics and computer science of probability has led to a revolution in the scope of probabilistic models. In particular, ‘sophisticated’ probabilistic methods apply to structured relational systems such as graphs and grammars, of immediate relevance to the cognitive sciences. This Special Issue outlines progress in this rapidly developing field, which provides a potentially unifying perspective across a wide range of domains and levels of explanation. Here, we introduce the historical and conceptual foundations of the approach, explore (...)
    Direct download (9 more)  
     
    Export citation  
     
    Bookmark   54 citations  
  • Integrating Physical Constraints in Statistical Inference by 11-Month-Old Infants.Stephanie Denison & Fei Xu - 2010 - Cognitive Science 34 (5):885-908.
    Much research on cognitive development focuses either on early-emerging domain-specific knowledge or domain-general learning mechanisms. However, little research examines how these sources of knowledge interact. Previous research suggests that young infants can make inferences from samples to populations (Xu & Garcia, 2008) and 11- to 12.5-month-old infants can integrate psychological and physical knowledge in probabilistic reasoning (Teglas, Girotto, Gonzalez, & Bonatti, 2007; Xu & Denison, 2009). Here, we ask whether infants can integrate a physical constraint of immobility into a statistical (...)
    Direct download (6 more)  
     
    Export citation  
     
    Bookmark   13 citations  
  • The Effects of Feature-Label-Order and Their Implications for Symbolic Learning.Michael Ramscar, Daniel Yarlett, Melody Dye, Katie Denny & Kirsten Thorpe - 2010 - Cognitive Science 34 (6):909-957.
    Symbols enable people to organize and communicate about the world. However, the ways in which symbolic knowledge is learned and then represented in the mind are poorly understood. We present a formal analysis of symbolic learning—in particular, word learning—in terms of prediction and cue competition, and we consider two possible ways in which symbols might be learned: by learning to predict a label from the features of objects and events in the world, and by learning to predict features from a (...)
    Direct download (6 more)  
     
    Export citation  
     
    Bookmark   17 citations  
  • Précis of Semantic Cognition: A Parallel Distributed Processing Approach.Timothy T. Rogers & James L. McClelland - 2008 - Behavioral and Brain Sciences 31 (6):689-714.
    In this prcis we focus on phenomena central to the reaction against similarity-based theories that arose in the 1980s and that subsequently motivated the approach to semantic knowledge. Specifically, we consider (1) how concepts differentiate in early development, (2) why some groupings of items seem to form or coherent categories while others do not, (3) why different properties seem central or important to different concepts, (4) why children and adults sometimes attest to beliefs that seem to contradict their direct experience, (...)
    Direct download (9 more)  
     
    Export citation  
     
    Bookmark   16 citations  
  • 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.
    Direct download (8 more)  
     
    Export citation  
     
    Bookmark   9 citations  
  • Constructing a New Theory From Old Ideas and New Evidence.Marjorie Rhodes & Henry Wellman - 2013 - Cognitive Science 37 (3):592-604.
    A central tenet of constructivist models of conceptual development is that children's initial conceptual level constrains how they make sense of new evidence and thus whether exposure to evidence will prompt conceptual change. Yet little experimental evidence directly examines this claim for the case of sustained, fundamental conceptual achievements. The present study combined scaling and experimental microgenetic methods to examine the processes underlying conceptual change in the context of an important conceptual achievement of early childhood—the development of a representational theory (...)
    Direct download (8 more)  
     
    Export citation  
     
    Bookmark   1 citation  
  • Abstract Knowledge Versus Direct Experience in Processing of Binomial Expressions.Emily Morgan & Roger Levy - 2016 - Cognition 157:384-402.
    Direct download (7 more)  
     
    Export citation  
     
    Bookmark  
  • 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 stimulus to a (...)
    Direct download (6 more)  
     
    Export citation  
     
    Bookmark   83 citations  
  • When Stronger Knowledge Slows You Down: Semantic Relatedness Predicts Children's Co‐Activation of Related Items in a Visual Search Paradigm.Catarina Vales & Anna V. Fisher - 2019 - Cognitive Science 43 (6):e12746.
    Direct download (3 more)  
     
    Export citation  
     
    Bookmark  
  • Incremental Bayesian Category Learning From Natural Language.Lea Frermann & Mirella Lapata - 2016 - Cognitive Science 40 (6):1333-1381.
    Models of category learning have been extensively studied in cognitive science and primarily tested on perceptual abstractions or artificial stimuli. In this paper, we focus on categories acquired from natural language stimuli, that is, words. We present a Bayesian model that, unlike previous work, learns both categories and their features in a single process. We model category induction as two interrelated subproblems: the acquisition of features that discriminate among categories, and the grouping of concepts into categories based on those features. (...)
    Direct download (7 more)  
     
    Export citation  
     
    Bookmark  
  • Bayesian Fundamentalism or Enlightenment? On the Explanatory Status and Theoretical Contributions of Bayesian Models of Cognition.Matt Jones & Bradley C. Love - 2011 - Behavioral and Brain Sciences 34 (4):169-188.
    The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology that set aside mechanistic explanations or make use of optimality assumptions. Through these comparisons, we identify a number of (...)
    Direct download (6 more)  
     
    Export citation  
     
    Bookmark   77 citations  
  • Controlling the Message: Preschoolers’ Use of Information to Teach and Deceive Others.Marjorie Rhodes, Elizabeth Bonawitz, Patrick Shafto, Annie Chen & Leyla Caglar - 2015 - Frontiers in Psychology 6.
    Direct download (5 more)  
     
    Export citation  
     
    Bookmark   1 citation  
  • Précis of How Children Learn the Meanings of Words.Paul Bloom - 2001 - Behavioral and Brain Sciences 24 (6):1095-1103.
    Normal children learn tens of thousands of words, and do so quickly and efficiently, often in highly impoverished environments. In How Children Learn the Meanings of Words, I argue that word learning is the product of certain cognitive and linguistic abilities that include the ability to acquire concepts, an appreciation of syntactic cues to meaning, and a rich understanding of the mental states of other people. These capacities are powerful, early emerging, and to some extent uniquely human, but they are (...)
    Direct download (7 more)  
     
    Export citation  
     
    Bookmark   11 citations  
  • Cultural Transmission in Cycles: The Production and Maintenance of Cumulative Culture.Thomas Abel - 2015 - Journal of Cognition and Culture 15 (5):443-492.
    No categories
    Direct download (4 more)  
     
    Export citation  
     
    Bookmark  
  • 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 (...)
    Direct download (6 more)  
     
    Export citation  
     
    Bookmark  
  • A Bayesian Framework for Word Segmentation: Exploring the Effects of Context.Sharon Goldwater, Thomas L. Griffiths & Mark Johnson - 2009 - Cognition 112 (1):21-54.
  • Reasoning About ‘Irrational’ Actions: When Intentional Movements Cannot Be Explained, the Movements Themselves Are Seen as the Goal.Adena Schachner & Susan Carey - 2013 - Cognition 129 (2):309-327.
  • Deconfounding Hypothesis Generation and Evaluation in Bayesian Models.Elizabeth Baraff Bonawitz & Thomas L. Griffiths - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society.
  • Novel Words in Novel Contexts: The Role of Distributional Information in Formclass Category Learning.Patricia A. Reeder, Elissa L. Newport & Richard N. Aslin - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 2063--2068.
    No categories
    Direct download  
     
    Export citation  
     
    Bookmark  
  • Learning Hypothesis Spaces and Dimensions Through Concept Learning.Joseph L. Austerweil & Thomas L. Griffiths - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 73--78.
  • The Place of Modeling in Cognitive Science.James L. McClelland - 2009 - Topics in Cognitive Science 1 (1):11-38.
    Direct download (8 more)  
     
    Export citation  
     
    Bookmark   14 citations