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  1. Nick Chater, Game Relativity: How Context Influences Strategic Decision Making.
    Existing models of strategic decision making typically assume that only the attributes of the currently played game need be considered when reaching a decision. The results presented in this article demonstrate that the so-called “cooperativeness” of the previously played prisoner’s dilemma games influence choices and predictions in the current prisoner’s dilemma game, which suggests that games are not considered independently. These effects involved reinforcement-based assimilation to the previous choices and also a perceptual contrast of the present game with preceding games, (...)
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  2. Nick Chater & Gordon D. A. Brown, Decision by Sampling.
    We present a theory of decision by sampling (DbS) in which, in contrast with traditional models, there are no underlying psychoeconomic scales. Instead, we assume that an attribute’s subjective value is constructed from a series of binary, ordinal comparisons to a sample of attribute values drawn from memory and is its rank within the sample. We assume that the sample reflects both the immediate distribution of attribute values from the current decision’s context and also the background, real-world distribution of attribute (...)
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  3. Nick Chater & Raymond J. Dolan, The Price of Pain and the Value of Suffering.
    Estimating the financial value of pain informs issues as diverse as the market price of analgesics, the cost-effectiveness of clinical treatments, compensation for injury, and the response to public hazards. Such costs are assumed to reflect a stable trade-off between relief of discomfort and money. Here, using an auction-based health market experiment, we show the price people pay for relief of pain is strongly determined by the local context of the market, determined either by recent intensities of pain, (...)
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  4. Nick Chater (2014). Cognitive Science as an Interface Between Rational and Mechanistic Explanation. Topics in Cognitive Science 6 (1):331-337.
    Cognitive science views thought as computation; and computation, by its very nature, can be understood in both rational and mechanistic terms. In rational terms, a computation solves some information processing problem (e.g., mapping sensory information into a description of the external world; parsing a sentence; selecting among a set of possible actions). In mechanistic terms, a computation corresponds to causal chain of events in a physical device (in engineering context, a silicon chip; in biological context, the nervous system). The discipline (...)
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  5. Mike Oaksford & Nick Chater (2014). Probabilistic Single Function Dual Process Theory and Logic Programming as Approaches to Non-Monotonicity in Human Vs. Artificial Reasoning. Thinking and Reasoning 20 (2):269-295.
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  6. Andrea Baronchelli, Ramon Ferrer-I.-Cancho, Romualdo Pastor-Satorras, Nick Chater & Morten H. Christiansen (2013). Networks in Cognitive Science. Trends in Cognitive Sciences 17 (7):348-360.
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  7. Nick Chater & Mike Oaksford (2013). Programs as Causal Models: Speculations on Mental Programs and Mental Representation. 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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  8. Sean Fulop & Nick Chater (2013). Editors' Introduction: Why Formal Learning Theory Matters for Cognitive Science. Topics in Cognitive Science 5 (1):3-12.
    This article reviews a number of different areas in the foundations of formal learning theory. After outlining the general framework for formal models of learning, the Bayesian approach to learning is summarized. This leads to a discussion of Solomonoff's Universal Prior Distribution for Bayesian learning. Gold's model of identification in the limit is also outlined. We next discuss a number of aspects of learning theory raised in contributed papers, related to both computational and representational complexity. The article concludes with a (...)
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  9. Anne S. Hsu, Nick Chater & Paul Vitányi (2013). Language Learning From Positive Evidence, Reconsidered: A Simplicity-Based Approach. Topics in Cognitive Science 5 (1):35-55.
    Children learn their native language by exposure to their linguistic and communicative environment, but apparently without requiring that their mistakes be corrected. Such learning from “positive evidence” has been viewed as raising “logical” problems for language acquisition. In particular, without correction, how is the child to recover from conjecturing an over-general grammar, which will be consistent with any sentence that the child hears? There have been many proposals concerning how this “logical problem” can be dissolved. In this study, we review (...)
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  10. Mike Oaksford & Nick Chater (2013). Dynamic Inference and Everyday Conditional Reasoning in the New Paradigm. Thinking and Reasoning 19 (3-4):346-379.
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  11. Mike Oaksford & Nick Chater (2012). Dual Processes, Probabilities, and Cognitive Architecture. Mind and Society 11 (1):15-26.
    It has been argued that dual process theories are not consistent with Oaksford and Chater’s probabilistic approach to human reasoning (Oaksford and Chater in Psychol Rev 101:608–631, 1994 , 2007 ; Oaksford et al. 2000 ), which has been characterised as a “single-level probabilistic treatment[s]” (Evans 2007 ). In this paper, it is argued that this characterisation conflates levels of computational explanation. The probabilistic approach is a computational level theory which is consistent with theories of general cognitive architecture that invoke (...)
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  12. Nilufa Ali, Nick Chater & Mike Oaksford (2011). The Mental Representation of Causal Conditional Reasoning: Mental Models or Causal Models. Cognition 119 (3):403-418.
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  13. Nick Chater, Noah Goodman, Thomas L. Griffiths, Charles Kemp, Mike Oaksford & Joshua B. Tenenbaum (2011). The Imaginary Fundamentalists: The Unshocking Truth About Bayesian Cognitive Science. Behavioral and Brain Sciences 34 (4):194-196.
    If Bayesian Fundamentalism existed, Jones & Love's (J&L's) arguments would provide a necessary corrective. But it does not. Bayesian cognitive science is deeply concerned with characterizing algorithms and representations, and, ultimately, implementations in neural circuits; it pays close attention to environmental structure and the constraints of behavioral data, when available; and it rigorously compares multiple models, both within and across papers. J&L's recommendation of Bayesian Enlightenment corresponds to past, present, and, we hope, future practice in Bayesian cognitive science.
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  14. Nick Chater, Petter Johansson & Lars Hall (2011). The Non-Existence of Risk Attitude. Frontiers in Psychology 2.
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  15. Nick Chater & Mike Oaksford (2011). Local and Global Inferential Relations: Response to Over (2009). Thinking and Reasoning 15 (4):439-446.
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  16. Anne S. Hsu, Nick Chater & Paul M. B. Vitányi (2011). The Probabilistic Analysis of Language Acquisition: Theoretical, Computational, and Experimental Analysis. Cognition 120 (3):380-390.
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  17. Christian P. Janssen, Duncan P. Brumby, John Dowell, Nick Chater & Andrew Howes (2011). Identifying Optimum Performance Trade-Offs Using a Cognitively Bounded Rational Analysis Model of Discretionary Task Interleaving. Topics in Cognitive Science 3 (1):123-139.
    We report the results of a dual-task study in which participants performed a tracking and typing task under various experimental conditions. An objective payoff function was used to provide explicit feedback on how participants should trade off performance between the tasks. Results show that participants’ dual-task interleaving strategy was sensitive to changes in the difficulty of the tracking task and resulted in differences in overall task performance. To test the hypothesis that people select strategies that maximize payoff, a Cognitively Bounded (...)
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  18. Mike Oaksford & Nick Chater (2011). The “is-Ought Fallacy” Fallacy. Behavioral and Brain Sciences 34 (5):262-263.
    Mere facts about how the world is cannot determine how we ought to think or behave. Elqayam & Evans (E&E) argue that this undercuts the use of rational analysis in explaining how people reason, by ourselves and with others. But this presumed application of the fallacy is itself fallacious. Rational analysis seeks to explain how people do reason, for example in laboratory experiments, not how they ought to reason. Thus, no ought is derived from an is; and rational analysis is (...)
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  19. Ivo Vlaev, Nick Chater, Neil Stewart & Gordon D. A. Brown (2011). Does the Brain Calculate Value? Trends in Cognitive Sciences 15 (11):546-554.
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  20. Janet I. Vousden, Michelle R. Ellefson, Jonathan Solity & Nick Chater (2011). Simplifying Reading: Applying the Simplicity Principle to Reading. Cognitive Science 35 (1):34-78.
    Debates concerning the types of representations that aid reading acquisition have often been influenced by the relationship between measures of early phonological awareness (the ability to process speech sounds) and later reading ability. Here, a complementary approach is explored, analyzing how the functional utility of different representational units, such as whole words, bodies (letters representing the vowel and final consonants of a syllable), and graphemes (letters representing a phoneme) may change as the number of words that can be read gradually (...)
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  21. Nilufa Ali, Anne Schlottman, Abigail Shaw, Nick Chater, & Oaksford & Mike (2010). Causal Discounting and Conditional Reasoning in Children. In Mike Oaksford & Nick Chater (eds.), Cognition and Conditionals: Probability and Logic in Human Thinking. Oup Oxford.
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  22. Nilufa Ali, Anne Schlottmann, Abigail Shaw, Nick Chater & Mike Oaksford (2010). Causal Discounting and Conditional Reasoning in Children. In M. Oaksford & N. Chater (eds.), Cognition and Conditionals: Probability and Logic in Human Thought. Oxford University Press.
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  23. William Bechtel, Marlene Behrmann, Nick Chater, Robert J. Glushko, Robert L. Goldstone & Paul Smolensky (2010). The Rumelhart Prize at 10. Cognitive Science 34 (5):713-715.
  24. Nick Chater & Morten H. Christiansen (2010). Language Acquisition Meets Language Evolution. Cognitive Science 34 (7):1131-1157.
    Recent research suggests that language evolution is a process of cultural change, in which linguistic structures are shaped through repeated cycles of learning and use by domain-general mechanisms. This paper draws out the implications of this viewpoint for understanding the problem of language acquisition, which is cast in a new, and much more tractable, form. In essence, the child faces a problem of induction, where the objective is to coordinate with others (C-induction), rather than to model the structure of the (...)
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  25. Thomas L. Griffiths, Nick Chater, Charles Kemp, Amy Perfors & Joshua B. Tenenbaum (2010). Probabilistic Models of Cognition: Exploring Representations and Inductive Biases. Trends in Cognitive Sciences 14 (8):357-364.
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  26. Anne S. Hsu & Nick Chater (2010). The Logical Problem of Language Acquisition: A Probabilistic Perspective. Cognitive Science 34 (6):972-1016.
    Natural language is full of patterns that appear to fit with general linguistic rules but are ungrammatical. There has been much debate over how children acquire these “linguistic restrictions,” and whether innate language knowledge is needed. Recently, it has been shown that restrictions in language can be learned asymptotically via probabilistic inference using the minimum description length (MDL) principle. Here, we extend the MDL approach to give a simple and practical methodology for estimating how much linguistic data are required to (...)
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  27. Nick Chater (2009). Rational and Mechanistic Perspectives on Reinforcement Learning. Cognition 113 (3):350-364.
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  28. Nick Chater (2009). Rational Models of Conditioning. Behavioral and Brain Sciences 32 (2):204-205.
    Mitchell et al. argue that conditioning phenomena may be better explained by high-level, rational processes, rather than by non-cognitive associative mechanisms. This commentary argues that this viewpoint is compatible with neuroscientific data, may extend to nonhuman animals, and casts computational models of reinforcement learning in a new light.
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  29. Morten H. Christiansen & Nick Chater (2009). The Myth of Language Universals and the Myth of Universal Grammar. Behavioral and Brain Sciences 32 (5):452.
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  30. Carl J. Hodgetts, Ulrike Hahn & Nick Chater (2009). Transformation and Alignment in Similarity. Cognition 113 (1):62-79.
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  31. Mike Oaksford & Nick Chater (2009). Précis of Bayesian Rationality: The Probabilistic Approach to Human Reasoning. Behavioral and Brain Sciences 32 (1):69-84.
    According to Aristotle, humans are the rational animal. The borderline between rationality and irrationality is fundamental to many aspects of human life including the law, mental health, and language interpretation. But what is it to be rational? One answer, deeply embedded in the Western intellectual tradition since ancient Greece, is that rationality concerns reasoning according to the rules of logic – the formal theory that specifies the inferential connections that hold with certainty between propositions. Piaget viewed logical reasoning as defining (...)
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  32. Mike Oaksford & Nick Chater (2009). The Uncertain Reasoner: Bayes, Logic, and Rationality. Behavioral and Brain Sciences 32 (1):105-120.
    Human cognition requires coping with a complex and uncertain world. This suggests that dealing with uncertainty may be the central challenge for human reasoning. In Bayesian Rationality we argue that probability theory, the calculus of uncertainty, is the right framework in which to understand everyday reasoning. We also argue that probability theory explains behavior, even on experimental tasks that have been designed to probe people's logical reasoning abilities. Most commentators agree on the centrality of uncertainty; some suggest that there is (...)
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  33. Ramsey M. Raafat, Nick Chater & Chris Frith (2009). Corrigendum: Herding in Humans. Trends in Cognitive Sciences 13 (12):504.
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  34. Ramsey M. Raafat, Nick Chater & Chris Frith (2009). Corrigendum: Herding in Humans:[Trends in Cognitive Sciences 13 (2009), 420-428]. Trends in Cognitive Sciences 13 (12):504.
     
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  35. Ramsey M. Raafat, Nick Chater & Chris Frith (2009). Herding in Humans. Trends in Cognitive Sciences 13 (10):420-428.
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  36. Nick Chater & Gordon D. A. Brown (2008). From Universal Laws of Cognition to Specific Cognitive Models. Cognitive Science 32 (1):36-67.
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  37. Nick Chater & Mike Oaksford (eds.) (2008). The Probabilistic Mind: Prospects for Bayesian Cognitive Science. OUP Oxford.
    The rational analysis method, first proposed by John R. Anderson, has been enormously influential in helping us understand high-level cognitive processes. -/- 'The Probabilistic Mind' is a follow-up to the influential and highly cited 'Rational Models of Cognition' (OUP, 1998). It brings together developments in understanding how, and how far, high-level cognitive processes can be understood in rational terms, and particularly using probabilistic Bayesian methods. It synthesizes and evaluates the progress in the past decade, taking into account developments in Bayesian (...)
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  38. Morten H. Christiansen & Nick Chater (2008). Brains, Genes, and Language Evolution: A New Synthesis. Behavioral and Brain Sciences 31 (5):537-558.
    Our target article argued that a genetically specified Universal Grammar (UG), capturing arbitrary properties of languages, is not tenable on evolutionary grounds, and that the close fit between language and language learners arises because language is shaped by the brain, rather than the reverse. Few commentaries defend a genetically specified UG. Some commentators argue that we underestimate the importance of processes of cultural transmission; some propose additional cognitive and brain mechanisms that may constrain language and perhaps differentiate humans from nonhuman (...)
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  39. Morten H. Christiansen & Nick Chater (2008). Language as Shaped by the Brain. Behavioral and Brain Sciences 31 (5):489-509.
    It is widely assumed that human learning and the structure of human languages are intimately related. This relationship is frequently suggested to derive from a language-specific biological endowment, which encodes universal, but communicatively arbitrary, principles of language structure (a Universal Grammar or UG). How might such a UG have evolved? We argue that UG could not have arisen either by biological adaptation or non-adaptationist genetic processes, resulting in a logical problem of language evolution. Specifically, as the processes of language change (...)
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  40. Mike Oaksford & Nick Chater (2008). Probability Logic and the Modus Ponens-Modus Tollens Asymmetry in Conditional Inference. In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oup Oxford. 97--120.
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  41. Nick Chater & Morten H. Christiansen (2007). Two Views of Simplicity in Linguistic Theory: Which Connects Better with Cognitive Science? Trends in Cognitive Sciences 11 (8):324-326.
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  42. Mike Oaksford & Nick Chater (2007). Bayesian Rationality: The Probabilistic Approach to Human Reasoning. OUP Oxford.
    Are people rational? This question was central to Greek thought and has been at the heart of psychology and philosophy for millennia. This book provides a radical and controversial reappraisal of conventional wisdom in the psychology of reasoning, proposing that the Western conception of the mind as a logical system is flawed at the very outset. It argues that cognition should be understood in terms of probability theory, the calculus of uncertain reasoning, rather than in terms of logic, the calculus (...)
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  43. Nick Chater & Christopher D. Manning (2006). Linguistics, Computational Linguistics and Cognitive Science. Trends in Cognitive Sciences 10 (7):335-344.
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  44. Nick Chater & Christopher D. Manning (2006). Probabilistic Models of Language Processing and Acquisition. Trends in Cognitive Sciences 10 (7):335-344.
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  45. Nick Chater, Joshua B. Tenenbaum & Alan Yuille (2006). Probabilistic Models of Cognition: Where Next? Trends in Cognitive Sciences 10 (7):292-293.
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  46. Nick Chater, Joshua B. Tenenbaum & Alan Yuille (2006). Subjective Probability in a Nutshell. Trends in Cognitive Sciences 10 (7):287-291.
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  47. Nick Chater & Alan Yuille (2006). Probabilistic Models of Cognition: Conceptual Foundations. 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 (...)
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  48. Susan Hurley & Nick Chater (eds.) (2005). Perspectives on Imitation: From Mirror Neurons to Memes, Vol II. MIT Press.
  49. Padraic Monaghan, Nick Chater & Morten H. Christiansen (2005). The Differential Role of Phonological and Distributional Cues in Grammatical Categorisation. Cognition 96 (2):143-182.
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