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  1. Singular Clues to Causality and Their Use in Human Causal Judgment.Peter A. White - 2014 - Cognitive Science 38 (1):38-75.
    It is argued that causal understanding originates in experiences of acting on objects. Such experiences have consistent features that can be used as clues to causal identification and judgment. These are singular clues, meaning that they can be detected in single instances. A catalog of 14 singular clues is proposed. The clues function as heuristics for generating causal judgments under uncertainty and are a pervasive source of bias in causal judgment. More sophisticated clues such as mechanism clues and repeated interventions (...)
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  • Reasoning With Causal Cycles.Bob Rehder - 2017 - Cognitive Science 41 (S5):944-1002.
    This article assesses how people reason with categories whose features are related in causal cycles. Whereas models based on causal graphical models have enjoyed success modeling category-based judgments as well as a number of other cognitive phenomena, CGMs are only able to represent causal structures that are acyclic. A number of new formalisms that allow cycles are introduced and evaluated. Dynamic Bayesian networks represent cycles by unfolding them over time. Chain graphs augment CGMs by allowing the presence of undirected links (...)
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  • A Bayesian Theory of Sequential Causal Learning and Abstract Transfer.Hongjing Lu, Randall R. Rojas, Tom Beckers & Alan L. Yuille - 2016 - Cognitive Science 40 (2):404-439.
    Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that (...)
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  • Reconciling Intuitive Physics and Newtonian Mechanics for Colliding Objects.Adam N. Sanborn, Vikash K. Mansinghka & Thomas L. Griffiths - 2013 - Psychological Review 120 (2):411-437.
  • Bayesian Analogy with Relational Transformations.Hongjing Lu, Dawn Chen & Keith J. Holyoak - 2012 - Psychological Review 119 (3):617-648.
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  • Learning a Theory of Causality.Noah D. Goodman, Tomer D. Ullman & Joshua B. Tenenbaum - 2011 - Psychological Review 118 (1):110-119.
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  • Assessing Evidence for a Common Function of Delay in Causal Learning and Reward Discounting.W. James Greville & Marc J. Buehner - 2012 - Frontiers in Psychology 3.
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  • 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.
  • From Mere Coincidences to Meaningful Discoveries.Thomas L. Griffiths & Joshua B. Tenenbaum - 2007 - Cognition 103 (2):180-226.
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  • 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 (...)
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  • Category Transfer in Sequential Causal Learning: The Unbroken Mechanism Hypothesis.York Hagmayer, Björn Meder, Momme von Sydow & Michael R. Waldmann - 2011 - Cognitive Science 35 (5):842-873.
    The goal of the present set of studies is to explore the boundary conditions of category transfer in causal learning. Previous research has shown that people are capable of inducing categories based on causal learning input, and they often transfer these categories to new causal learning tasks. However, occasionally learners abandon the learned categories and induce new ones. Whereas previously it has been argued that transfer is only observed with essentialist categories in which the hidden properties are causally relevant for (...)
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  • The Origins of Inquiry: Inductive Inference and Exploration in Early Childhood.Laura Schulz - 2012 - Trends in Cognitive Sciences 16 (7):382-389.
  • The Early Emergence and Puzzling Decline of Relational Reasoning: Effects of Knowledge and Search on Inferring Abstract Concepts.Caren M. Walker, Sophie Bridgers & Alison Gopnik - 2016 - Cognition 156:30-40.
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  • 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 (...)
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  • Previous Knowledge Can Induce an Illusion of Causality Through Actively Biasing Behavior.Ion Yarritu & Helena Matute - 2015 - Frontiers in Psychology 6.
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  • Ingredients of Intelligence: From Classic Debates to an Engineering Roadmap.Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum & Samuel J. Gershman - 2017 - Behavioral and Brain Sciences 40.
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  • Agents and Causes: Dispositional Intuitions As a Guide to Causal Structure.Ralf Mayrhofer & Michael R. Waldmann - 2015 - Cognitive Science 39 (1):65-95.
    Currently, two frameworks of causal reasoning compete: Whereas dependency theories focus on dependencies between causes and effects, dispositional theories model causation as an interaction between agents and patients endowed with intrinsic dispositions. One important finding providing a bridge between these two frameworks is that failures of causes to generate their effects tend to be differentially attributed to agents and patients regardless of their location on either the cause or the effect side. To model different types of error attribution, we augmented (...)
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  • Hierarchical Bayesian Models as Formal Models of Causal Reasoning.York Hagmayer & Ralf Mayrhofer - 2013 - Argument and Computation 4 (1):36 - 45.
    (2013). Hierarchical Bayesian models as formal models of causal reasoning. Argument & Computation: Vol. 4, Formal Models of Reasoning in Cognitive Psychology, pp. 36-45. doi: 10.1080/19462166.2012.700321.
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  • Intuitive Theories as Grammars for Causal Inference.Joshua B. Tenenbaum, Thomas L. Griffiths & Sourabh Niyogi - 2007 - In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press. pp. 301--322.
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  • The Induction of Hidden Causes: Causal Mediation and Violations of Independent Causal Influence.Christopher D. Carroll & Patricia W. Cheng - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 913--918.
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  • Developmental Differences in Learning the Forms of Causal Relationships.Chris Lucas, Alison Gopnik & 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. 28--52.
  • Two Proposals for Causal Grammars.Thomas L. Griffiths & Joshua B. Tenenbaum - 2007 - In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press. pp. 323--345.
  • A General Structure for Legal Arguments About Evidence Using Bayesian Networks.Norman Fenton, Martin Neil & David A. Lagnado - 2013 - Cognitive Science 37 (1):61-102.
    A Bayesian network (BN) is a graphical model of uncertainty that is especially well suited to legal arguments. It enables us to visualize and model dependencies between different hypotheses and pieces of evidence and to calculate the revised probability beliefs about all uncertain factors when any piece of new evidence is presented. Although BNs have been widely discussed and recently used in the context of legal arguments, there is no systematic, repeatable method for modeling legal arguments as BNs. Hence, where (...)
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  • Cognitive Shortcuts in Causal Inference.Philip M. Fernbach & Bob Rehder - 2013 - Argument and Computation 4 (1):64 - 88.
    (2013). Cognitive shortcuts in causal inference. Argument & Computation: Vol. 4, Formal Models of Reasoning in Cognitive Psychology, pp. 64-88. doi: 10.1080/19462166.2012.682655.
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  • What the Bayesian Framework has Contributed to Understanding Cognition: Causal Learning as a Case Study.Keith J. Holyoak & Hongjing Lu - 2011 - Behavioral and Brain Sciences 34 (4):203-204.
    The field of causal learning and reasoning (largely overlooked in the target article) provides an illuminating case study of how the modern Bayesian framework has deepened theoretical understanding, resolved long-standing controversies, and guided development of new and more principled algorithmic models. This progress was guided in large part by the systematic formulation and empirical comparison of multiple alternative Bayesian models.
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  • The Imaginary Fundamentalists: The Unshocking Truth About Bayesian Cognitive Science.Nick Chater, Noah Goodman, Thomas L. Griffiths, Charles Kemp, Mike Oaksford & Joshua B. Tenenbaum - 2011 - 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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  • Experimental Philosophy and Causal Attribution.Jonathan Livengood & David Rose - 2016 - In Justin Sytsma & Wesley Buckwalter (eds.), A Companion to Experimental Philosophy. Blackwell.
    Humans often attribute the things that happen to one or another actual cause. In this chapter, we survey some recent philosophical and psychological research on causal attribution. We pay special attention to the relation between graphical causal modeling and theories of causal attribution. We think that the study of causal attribution is one place where formal and experimental techniques nicely complement one another.
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  • Bayesian Cognitive Science, Unification, and Explanation.Stephan Hartmann & Matteo Colombo - 2017 - British Journal for the Philosophy of Science 68 (2).
    It is often claimed that the greatest value of the Bayesian framework in cognitive science consists in its unifying power. Several Bayesian cognitive scientists assume that unification is obviously linked to explanatory power. But this link is not obvious, as unification in science is a heterogeneous notion, which may have little to do with explanation. While a crucial feature of most adequate explanations in cognitive science is that they reveal aspects of the causal mechanism that produces the phenomenon to be (...)
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  • Children Use Temporal Cues to Learn Causal Directionality.Benjamin M. Rottman, Jonathan F. Kominsky & Frank C. Keil - 2014 - Cognitive Science 38 (3):489-513.
    The ability to learn the direction of causal relations is critical for understanding and acting in the world. We investigated how children learn causal directionality in situations in which the states of variables are temporally dependent (i.e., autocorrelated). In Experiment 1, children learned about causal direction by comparing the states of one variable before versus after an intervention on another variable. In Experiment 2, children reliably inferred causal directionality merely from observing how two variables change over time; they interpreted Y (...)
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  • Generalized Information Theory Meets Human Cognition: Introducing a Unified Framework to Model Uncertainty and Information Search.Vincenzo Crupi, Jonathan D. Nelson, Björn Meder, Gustavo Cevolani & Katya Tentori - 2018 - Cognitive Science 42 (5):1410-1456.
    Searching for information is critical in many situations. In medicine, for instance, careful choice of a diagnostic test can help narrow down the range of plausible diseases that the patient might have. In a probabilistic framework, test selection is often modeled by assuming that people’s goal is to reduce uncertainty about possible states of the world. In cognitive science, psychology, and medical decision making, Shannon entropy is the most prominent and most widely used model to formalize probabilistic uncertainty and the (...)
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  • What, When and How Do Rational Analysis Models Explain?Samuli Poyhonen - unknown
    Probabilistic modeling is a highly influential method of theorizing in cognitive science. Rational analysis is an account of how probabilistic modeling can be used to construct non-mechanistic but self-standing explanatory models of the mind. In this article, I disentangle and assess several possible explanatory contributions which could be attributed to rational analysis. Although existing models suffer from evidential problems that question their explanatory power, I argue that rational analysis modeling can complement mechanistic theorizing by providing models of environmental affordances.
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  • Theory-Based Bayesian Models of Inductive Learning and Reasoning.Joshua B. Tenenbaum, Thomas L. Griffiths & Charles Kemp - 2006 - Trends in Cognitive Sciences 10 (7):309-318.
  • Probabilistic Models of Cognition: Exploring Representations and Inductive Biases.Thomas L. Griffiths, Nick Chater, Charles Kemp, Amy Perfors & Joshua B. Tenenbaum - 2010 - Trends in Cognitive Sciences 14 (8):357-364.
  • Prior Divergence: Do Researchers and Participants Share the Same Prior Probability Distributions?Christina Fang, Sari Carp & Zur Shapira - 2011 - Cognitive Science 35 (4):744-762.
    Do participants bring their own priors to an experiment? If so, do they share the same priors as the researchers who design the experiment? In this article, we examine the extent to which self-generated priors conform to experimenters’ expectations by explicitly asking participants to indicate their own priors in estimating the probability of a variety of events. We find in Study 1 that despite being instructed to follow a uniform distribution, participants appear to have used their own priors, which deviated (...)
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  • From Colliding Billiard Balls to Colluding Desperate Housewives: Causal Bayes Nets as Rational Models of Everyday Causal Reasoning.York Hagmayer & Magda Osman - 2012 - Synthese 189 (S1):17-28.
    Many of our decisions pertain to causal systems. Nevertheless, only recently has it been claimed that people use causal models when making judgments, decisions and predictions, and that causal Bayes nets allow us to formally describe these inferences. Experimental research has been limited to simple, artificial problems, which are unrepresentative of the complex dynamic systems we successfully deal with in everyday life. For instance, in social interactions, we can explain the actions of other's on the fly and we can generalize (...)
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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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  • Causation: Interactions Between Philosophical Theories and Psychological Research.James Woodward - 2012 - Philosophy of Science 79 (5):961-972.
  • Children’s Imitation of Causal Action Sequences is Influenced by Statistical and Pedagogical Evidence.Daphna Buchsbaum, Alison Gopnik, Thomas L. Griffiths & Patrick Shafto - 2011 - Cognition 120 (3):331-340.
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  • Sufficiency and Necessity Assumptions in Causal Structure Induction.Ralf Mayrhofer & Michael R. Waldmann - 2016 - Cognitive Science 40 (8):2137-2150.
    Research on human causal induction has shown that people have general prior assumptions about causal strength and about how causes interact with the background. We propose that these prior assumptions about the parameters of causal systems do not only manifest themselves in estimations of causal strength or the selection of causes but also when deciding between alternative causal structures. In three experiments, we requested subjects to choose which of two observable variables was the cause and which the effect. We found (...)
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  • Bayesian Learning and the Psychology of Rule Induction.Ansgar D. Endress - 2013 - Cognition 127 (2):159-176.
  • Incremental Implicit Learning of Bundles of Statistical Patterns.Ting Qian, T. Florian Jaeger & Richard N. Aslin - 2016 - Cognition 157:156-173.
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  • The Now-or-Never Bottleneck: A Fundamental Constraint on Language.Morten H. Christiansen & Nick Chater - 2016 - Behavioral and Brain Sciences 39:1-52.
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