Results for ' causal learning'

976 found
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  1. Causal learning: psychology, philosophy, and computation.Alison Gopnik & Laura Schulz (eds.) - 2007 - New York: Oxford University Press.
    Understanding causal structure is a central task of human cognition. Causal learning underpins the development of our concepts and categories, our intuitive theories, and our capacities for planning, imagination and inference. During the last few years, there has been an interdisciplinary revolution in our understanding of learning and reasoning: Researchers in philosophy, psychology, and computation have discovered new mechanisms for learning the causal structure of the world. This new work provides a rigorous, formal basis (...)
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  2. Causal learning in rats and humans: a minimal rational model.Michael R. Waldmann, Patricia W. Cheng, York Hagmeyer & Blaisdell & P. Aaron - 2008 - In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oxford University Press.
     
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  3.  17
    Causal learning in rats and humans: A minimal rational model.Michael R. Waldmann, Patricia W. Cheng, York Hagmayer & Aaron P. Blaisdell - 2008 - In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oxford University Press.
  4.  36
    Dynamical Causal Learning.David Danks, Thomas L. Griffiths & Joshua B. Tenenbaum - unknown
    Current psychological theories of human causal learning and judgment focus primarily on long-run predictions: two by estimating parameters of a causal Bayes nets, and a third through structural learning. This paper focuses on people’s short-run behavior by examining dynamical versions of these three theories, and comparing their predictions to a real-world dataset.
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  5. Causal learning in children: Causal maps and Bayes nets.Alison Gopnik, Clark Glymour, David M. Sobel & Laura E. Schultz - unknown
    We outline a cognitive and computational account of causal learning in children. We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent representation of the causal relations among events. This kind of knowledge can be perspicuously represented by the formalism of directed graphical causal models, or “Bayes nets”. Human causal learning and inference may involve computations similar to those for learnig (...)
     
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  6.  29
    Causal Learning with Occam’s Razor.Oliver Schulte - 2019 - Studia Logica 107 (5):991-1023.
    Occam’s razor directs us to adopt the simplest hypothesis consistent with the evidence. Learning theory provides a precise definition of the inductive simplicity of a hypothesis for a given learning problem. This definition specifies a learning method that implements an inductive version of Occam’s razor. As a case study, we apply Occam’s inductive razor to causal learning. We consider two causal learning problems: learning a causal graph structure that presents global (...) connections among a set of domain variables, and learning context-sensitive causal relationships that hold not globally, but only relative to a context. For causal graph learning, Occam’s inductive razor directs us to adopt the model that explains the observed correlations with a minimum number of direct causal connections. For expanding a causal graph structure to include context-sensitive relationships, Occam’s inductive razor directs us to adopt the expansion that explains the observed correlations with a minimum number of free parameters. This is equivalent to explaining the correlations with a minimum number of probabilistic logical rules. The paper provides a gentle introduction to the learning-theoretic definition of inductive simplicity and the application of Occam’s razor for causal learning. (shrink)
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  7. A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.Alison Gopnik, Clark Glymour, Laura Schulz, Tamar Kushnir & David Danks - 2004 - Psychological Review 111 (1):3-32.
    We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or “Bayes nets”. Children’s causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental (...)
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  8. Causal learning across domains.Alison Gopnik - unknown
    Five studies investigated (a) children’s ability to use the dependent and independent probabilities of events to make causal inferences and (b) the interaction between such inferences and domain-specific knowledge. In Experiment 1, preschoolers used patterns of dependence and independence to make accurate causal inferences in the domains of biology and psychology. Experiment 2 replicated the results in the domain of biology with a more complex pattern of conditional dependencies. In Experiment 3, children used evidence about patterns of dependence (...)
     
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  9.  43
    Causal Learning Mechanisms in Very Young Children: Two-, Three-, and Four-Year-Olds Infer Causal Relations From Patterns of Variation and Covariation.Clark Glymour, Alison Gopnik, David M. Sobel & Laura E. Schulz - unknown
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  10.  77
    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 (...)
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  11.  19
    Causal learning.Marc J. Buehner & Patricia W. Cheng - 2005 - In K. Holyoak & B. Morrison (eds.), The Cambridge Handbook of Thinking and Reasoning. Cambridge University Press. pp. 143--168.
  12.  14
    Causal Learning from Observations and Manipulations.David Danks - unknown
  13.  16
    The Accuracy of Causal Learning Over Long Timeframes: An Ecological Momentary Experiment Approach.Ciara L. Willett & Benjamin M. Rottman - 2021 - Cognitive Science 45 (7):e12985.
    The ability to learn cause–effect relations from experience is critical for humans to behave adaptively — to choose causes that bring about desired effects. However, traditional experiments on experience-based learning involve events that are artificially compressed in time so that all learning occurs over the course of minutes. These paradigms therefore exclusively rely upon working memory. In contrast, in real-world situations we need to be able to learn cause–effect relations over days and weeks, which necessitates long-term memory. 413 (...)
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  14.  10
    Causal learning in CTC: Adaptive and collaborative.Netanel Weinstein & Dare Baldwin - 2020 - Behavioral and Brain Sciences 43.
    Osiurak and Reynaud highlight the critical role of technical-reasoning skills in the emergence of human cumulative technological culture, in contrast to previous accounts foregrounding social-reasoning skills as key to CTC. We question their analysis of the available evidence, yet for other reasons applaud the emphasis on causal understanding as central to the adaptive and collaborative dynamics of CTC.
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  15. Causal learning through repeated decision making.York Hagmayer & Björn Meder - 2008 - In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 179--184.
     
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  16.  45
    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 (...) and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue-outcome co-occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge. (shrink)
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  17.  54
    Bayesian generic priors for causal learning.Hongjing Lu, Alan L. Yuille, Mimi Liljeholm, Patricia W. Cheng & Keith J. Holyoak - 2008 - Psychological Review 115 (4):955-984.
  18.  20
    Constraints and nonconstraints in causal learning: Reply to White (2005) and to Luhmann and Ahn (2005).Patricia W. Cheng & Laura R. Novick - 2005 - Psychological Review 112 (3):694-706.
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  19.  11
    Constraint-Based Human Causal Learning.David Danks - unknown
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  20.  91
    Learning to Learn Causal Models.Charles Kemp, Noah D. Goodman & Joshua B. Tenenbaum - 2010 - Cognitive Science 34 (7):1185-1243.
    Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities (...)
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  21.  15
    Distinguishing causation and correlation: Causal learning from time-series graphs with trends.Kevin W. Soo & Benjamin M. Rottman - 2020 - Cognition 195 (C):104079.
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  22.  29
    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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  23.  46
    Conditional Learning Through Causal Models.Jonathan Vandenburgh - 2020 - Synthese (1-2):2415-2437.
    Conditional learning, where agents learn a conditional sentence ‘If A, then B,’ is difficult to incorporate into existing Bayesian models of learning. This is because conditional learning is not uniform: in some cases, learning a conditional requires decreasing the probability of the antecedent, while in other cases, the antecedent probability stays constant or increases. I argue that how one learns a conditional depends on the causal structure relating the antecedent and the consequent, leading to a (...)
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  24.  10
    Causal Structure Learning in Continuous Systems.Zachary J. Davis, Neil R. Bramley & Bob Rehder - 2020 - Frontiers in Psychology 11.
    Real causal systems are complicated. Despite this, causal learning research has traditionally emphasized how causal relations can be induced on the basis of idealized events, i.e. those that have been mapped to binary variables and abstracted from time. For example, participants may be asked to assess the efficacy of a headache-relief pill on the basis of multiple patients who take the pill (or not) and find their headache relieved (or not). In contrast, the current study examines (...)
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  25. Cause and intent: Social reasoning in causal learning.Noah D. Goodman, Chris L. Baker & Joshua B. Tenenbaum - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. pp. 2759--2764.
     
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  26.  16
    Formalizing Neurath’s ship: Approximate algorithms for online causal learning.Neil R. Bramley, Peter Dayan, Thomas L. Griffiths & David A. Lagnado - 2017 - Psychological Review 124 (3):301-338.
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  27. Causal feature learning for utility-maximizing agents.David Kinney & David Watson - 2020 - In David Kinney & David Watson (eds.), International Conference on Probabilistic Graphical Models. pp. 257–268.
    Discovering high-level causal relations from low-level data is an important and challenging problem that comes up frequently in the natural and social sciences. In a series of papers, Chalupka etal. (2015, 2016a, 2016b, 2017) develop a procedure forcausal feature learning (CFL) in an effortto automate this task. We argue that CFL does not recommend coarsening in cases where pragmatic considerations rule in favor of it, and recommends coarsening in cases where pragmatic considerations rule against it. We propose a (...)
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  28.  39
    Learning from Non-Causal Models.Francesco Nappo - 2020 - Erkenntnis 87 (5):2419-2439.
    This paper defends the thesis of learning from non-causal models: viz. that the study of some model can prompt justified changes in one’s confidence in empirical hypotheses about a real-world target in the absence of any known or predicted similarity between model and target with regards to their causal features. Recognizing that we can learn from non-causal models matters not only to our understanding of past scientific achievements, but also to contemporary debates in the philosophy of (...)
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  29.  33
    Not by contingency: Some arguments about the fundamentals of human causal learning.Peter A. White - 2009 - Thinking and Reasoning 15 (2):129-166.
    The power PC theory postulates a normative procedure for making causal inferences from contingency information, and offers this as a descriptive model of human causal judgement. The inferential procedure requires a set of assumptions, which includes the assumption that the cause being judged is distributed independently of the set of other possible causes of the same outcome. It is argued that this assumption either never holds or can never be known to hold. It is also argued that conformity (...)
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  30.  27
    Causal discovery using adaptive logics. Towards a more realistic heuristics for human causal learning.Maarten Van Dyck - 2004 - Logique Et Analyse 185 (188):5-32.
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  31.  31
    Explaining compound generalization in associative and causal learning through rational principles of dimensional generalization.Fabian A. Soto, Samuel J. Gershman & Yael Niv - 2014 - Psychological Review 121 (3):526-558.
  32.  34
    Learning Causal Structure from Undersampled Time Series.David Danks & Sergey Plis - unknown
    Even if one can experiment on relevant factors, learning the causal structure of a dynamical system can be quite difficult if the relevant measurement processes occur at a much slower sampling rate than the “true” underlying dynamics. This problem is exacerbated if the degree of mismatch is unknown. This paper gives a formal characterization of this learning problem, and then provides two sets of results. First, we prove a set of theorems characterizing how causal structures change (...)
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  33. Assessing Evidence for a Common Function of Delay in Causal Learning and Reward Discounting.W. James Greville & Marc J. Buehner - 2014 - In Marc J. Buehner (ed.), Time and causality. [Lausanne, Switzerland]: Frontiers Media SA.
     
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  34.  20
    Learning Causal Structure through Local Prediction-error Learning.Sarah Wellen & David Danks - unknown
    Research on human causal learning has largely focused on strength learning, or on computational-level theories; there are few formal algorithmic models of how people learn causal structure from covariations. We introduce a model that learns causal structure in a local manner via prediction-error learning. This local learning is then integrated dynamically into a unified representation of causal structure. The model uses computationally plausible approximations of rational learning, and so represents a hybrid (...)
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  35.  52
    Learning the Form of Causal Relationships Using Hierarchical Bayesian Models.Christopher G. Lucas & Thomas L. Griffiths - 2010 - Cognitive Science 34 (1):113-147.
  36.  75
    Learning causal relationships.Jon Williamson - 2002
    How ought we learn causal relationships? While Popper advocated a hypothetico-deductive logic of causal discovery, inductive accounts are currently in vogue. Many inductive approaches depend on the causal Markov condition as a fundamental assumption. This condition, I maintain, is not universally valid, though it is justifiable as a default assumption. In which case the results of the inductive causal learning procedure must be tested before they can be accepted. This yields a synthesis of the hypothetico-deductive (...)
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  37.  24
    The verbal information pathway to fear and subsequent causal learning in children.Andy P. Field & Joanne Lawson - 2008 - Cognition and Emotion 22 (3):459-479.
  38.  21
    Why are some dimensions integral? Testing two hypotheses through causal learning experiments.Fabián A. Soto, Gonzalo R. Quintana, Andrés M. Pérez-Acosta, Fernando P. Ponce & Edgar H. Vogel - 2015 - Cognition 143 (C):163-177.
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  39.  16
    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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  40. Online Causal Structure Learning.David Danks - unknown
    Causal structure learning algorithms have focused on learning in ”batch-mode”: i.e., when a full dataset is presented. In many domains, however, it is important to learn in an online fashion from sequential or ordered data, whether because of memory storage constraints or because of potential changes in the underlying causal structure over the course of learning. In this paper, we present TDSL, a novel causal structure learning algorithm that processes data sequentially. This algorithm (...)
     
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  41.  15
    Causal scientific explanations from machine learning.Stefan Buijsman - 2023 - Synthese 202 (6):1-16.
    Machine learning is used more and more in scientific contexts, from the recent breakthroughs with AlphaFold2 in protein fold prediction to the use of ML in parametrization for large climate/astronomy models. Yet it is unclear whether we can obtain scientific explanations from such models. I argue that when machine learning is used to conduct causal inference we can give a new positive answer to this question. However, these ML models are purpose-built models and there are technical results (...)
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  42. Learning from doing: Intervention and causal inference.Laura Schulz, Tamar Kushnir & Alison Gopnik - 2007 - In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press. pp. 67--85.
  43.  48
    Causal Models: How People Think About the World and its Alternatives.Steven Sloman - 2005 - Oxford, England: OUP.
    This book offers a discussion about how people think, talk, learn, and explain things in causal terms in terms of action and manipulation. Sloman also reviews the role of causality, causal models, and intervention in the basic human cognitive functions: decision making, reasoning, judgement, categorization, inductive inference, language, and learning.
  44.  23
    Learning causality in a complex world: understandings of consequence.Tina Grotzer - 2012 - Lanham, Maryland: Rowman & Littlefield Education.
    Introduction -- Simple linear causality : one thing makes another happen -- The cognitive science of simple causality : why do we get stuck? -- Domino causality : effects that become causes -- Cyclic causality : loops and feedback -- Spiraling causality : escalation and de-escalation -- Mutual causality : symbiosis and bi-directionality -- Relational causality : balances and differentials -- Across time and distance : detecting delayed and distant effects -- "What happened?" vs. "what's going on?" : thinking about (...)
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  45.  28
    Learning Linear Causal Structure Equation Models with Genetic Algorithms.Shane Harwood & Richard Scheines - unknown
    Shane Harwood and Richard Scheines. Learning Linear Causal Structure Equation Models with Genetic Algorithms.
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  46.  48
    Causal Cognition: A Multidisciplinary Debate.Dan Sperber, David Premack & Ann James Premack (eds.) - 1995 - Oxford University Press UK.
    An understanding of cause--effect relationships is fundamental to the study of cognition. In this book, outstanding specialists from comparative psychology, social psychology, developmental psychology, anthropology, and philosophy present the newest developments in the study of causal cognition and discuss their different perspectives. They reflect on the role and forms of causal knowledge, both in animal and human cognition, on the development of human causal cognition from infancy, and on the relationship between individual and cultural aspects of (...) understanding. The result is a state-of-the-art, informative, insightful, and interdisciplinary debate aimed at the non-specialist. (shrink)
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  47.  21
    The Construction of Causal Schemes: Learning Mechanisms at the Knowledge Level.Andrea A. diSessa - 2014 - Cognitive Science 38 (5):795-850.
    This work uses microgenetic study of classroom learning to illuminate (1) the role of pre-instructional student knowledge in the construction of normative scientific knowledge, and (2) the learning mechanisms that drive change. Three enactments of an instructional sequence designed to lead to a scientific understanding of thermal equilibration are used as data sources. Only data from a scaffolded student inquiry preceding introduction of a normative model were used. Hence, the study involves nearly autonomous student learning. In two (...)
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  48.  16
    Learning the Causal Structure of Overlapping Variable Sets.David Danks - unknown
  49.  48
    A causal framework for integrating learning and reasoning.David A. Lagnado - 2009 - Behavioral and Brain Sciences 32 (2):211-212.
    Can the phenomena of associative learning be replaced wholesale by a propositional reasoning system? Mitchell et al. make a strong case against an automatic, unconscious, and encapsulated associative system. However, their propositional account fails to distinguish inferences based on actions from those based on observation. Causal Bayes networks remedy this shortcoming, and also provide an overarching framework for both learning and reasoning. On this account, causal representations are primary, but associative learning processes are not excluded (...)
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  50.  34
    Learning from actions and their consequences: Inferring causal variables from continuous sequences of human action.Daphna Buchsbaum, Thomas L. Griffiths, Alison Gopnik & Dare Baldwin - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. pp. 134.
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