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  1. 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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  • Great apes and children infer causal relations from patterns of variation and covariation.Christoph J. Völter, Inés Sentís & Josep Call - 2016 - Cognition 155 (C):30-43.
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  • Word learning as Bayesian inference.Fei Xu & Joshua B. Tenenbaum - 2007 - Psychological Review 114 (2):245-272.
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  • 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 results suggest that 2- to 4-year-old children (...)
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  • One and Done? Optimal Decisions From Very Few Samples.Edward Vul, Noah Goodman, Thomas L. Griffiths & Joshua B. Tenenbaum - 2014 - Cognitive Science 38 (4):599-637.
    In many learning or inference tasks human behavior approximates that of a Bayesian ideal observer, suggesting that, at some level, cognition can be described as Bayesian inference. However, a number of findings have highlighted an intriguing mismatch between human behavior and standard assumptions about optimality: People often appear to make decisions based on just one or a few samples from the appropriate posterior probability distribution, rather than using the full distribution. Although sampling-based approximations are a common way to implement Bayesian (...)
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  • Children’s developing understanding of the relation between variable causal efficacy and mechanistic complexity.Christopher D. Erb, David W. Buchanan & David M. Sobel - 2013 - Cognition 129 (3):494-500.
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  • Learning the Form of Causal Relationships Using Hierarchical Bayesian Models.Christopher G. Lucas & Thomas L. Griffiths - 2010 - Cognitive Science 34 (1):113-147.
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  • Cue competition effects and young children's causal and counterfactual inferences.Teresa McCormack, Stephen Andrew Butterfill, Christoph Hoerl & Patrick Burns - 2009 - Developmental Psychology 45 (6):1563-1575.
    The authors examined cue competition effects in young children using the blicket detector paradigm, in which objects are placed either singly or in pairs on a novel machine and children must judge which objects have the causal power to make the machine work. Cue competition effects were found in a 5- to 6-year-old group but not in a 4-year-old group. Equivalent levels of forward and backward blocking were found in the former group. Children's counterfactual judgments were subsequently examined by asking (...)
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  • Young children's reasoning about the order of past events.Teresa McCormack & Christoph Hoerl - 2007 - Journal of Experimental Child Psychology 98 (3):168-183.
    Four studies are reported that employed an object location task to assess temporal–causal reasoning. In Experiments 1–3, successfully locating the object required a retrospective consideration of the order in which two events had occurred. In Experiment 1, 5- but not 4-year-olds were successful; 4-year-olds also failed to perform at above-chance levels in modified versions of the task in Experiments 2 and 3. However, in Experiment 4, 3-year-olds were successful when they were able to see the object being placed first in (...)
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  • Non‐cognitivism about Metaphysical explanation.Kristie Miller & James Norton - 2022 - Analytic Philosophy 64 (2):1-20.
    This article introduces a non‐cognitivist account of metaphysical explanation according to which the core function of judgements of the form ⌜x because y⌝ is not to state truth‐apt beliefs. Instead, their core function is to express attitudes of commitment to, and recommendation of the acceptance of certain norms governing interventional conduct at contexts.
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  • Metaphysics of the Bayesian mind.Justin Tiehen - 2022 - Mind and Language 38 (2):336-354.
    Recent years have seen a Bayesian revolution in cognitive science. This should be of interest to metaphysicians of science, whose naturalist project involves working out the metaphysical implications of our leading scientific accounts, and in advancing our understanding of those accounts by drawing on the metaphysical frameworks developed by philosophers. Toward these ends, in this paper I develop a metaphysics of the Bayesian mind. My central claim is that the Bayesian approach supports a novel empirical argument for normativism, the thesis (...)
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  • Inferring Unseen Causes: Developmental and Evolutionary Origins.Zeynep Civelek, Josep Call & Amanda M. Seed - 2020 - Frontiers in Psychology 11.
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  • Troubles with Bayesianism: An introduction to the psychological immune system.Eric Mandelbaum - 2018 - 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.
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  • Locally Bayesian learning with applications to retrospective revaluation and highlighting.John K. Kruschke - 2006 - Psychological Review 113 (4):677-699.
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  • Two proposals for causal grammars.Thomas L. Griffiths & Joshua B. Tenenbaum - 2007 - In Alison Gopnik & Laura Schulz (eds.), Causal learning: psychology, philosophy, and computation. New York: Oxford University Press. pp. 323--345.
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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. New York: Oxford University Press. pp. 301--322.
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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.
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  • Statistical inference and sensitivity to sampling in 11-month-old infants.Fei Xu & Stephanie Denison - 2009 - Cognition 112 (1):97-104.
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  • Going beyond the evidence: Abstract laws and preschoolers’ responses to anomalous data.Laura E. Schulz, Noah D. Goodman, Joshua B. Tenenbaum & Adrianna C. Jenkins - 2008 - Cognition 109 (2):211-223.
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  • (1 other version)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.
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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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  • 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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  • Learning science through inquiry in kindergarten.Ala Samarapungavan, Panayota Mantzicopoulos & Helen Patrick - 2008 - Science Education 92 (5):868-908.
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  • Where science starts: Spontaneous experiments in preschoolers’ exploratory play.Claire Cook, Noah D. Goodman & Laura E. Schulz - 2011 - Cognition 120 (3):341-349.
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  • 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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  • The role of preschoolers’ social understanding in evaluating the informativeness of causal interventions.Tamar Kushnir, Henry M. Wellman & Susan A. Gelman - 2008 - Cognition 107 (3):1084-1092.
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  • Confirmation in the Cognitive Sciences: The Problematic Case of Bayesian Models. [REVIEW]Frederick Eberhardt & David Danks - 2011 - Minds and Machines 21 (3):389-410.
    Bayesian models of human learning are becoming increasingly popular in cognitive science. We argue that their purported confirmation largely relies on a methodology that depends on premises that are inconsistent with the claim that people are Bayesian about learning and inference. Bayesian models in cognitive science derive their appeal from their normative claim that the modeled inference is in some sense rational. Standard accounts of the rationality of Bayesian inference imply predictions that an agent selects the option that maximizes the (...)
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  • Temporal information and children's and adults' causal inferences.Teresa McCormack & Patrick Burns - 2009 - Thinking and Reasoning 15 (2):167-196.
    Three experiments examined whether children and adults would use temporal information as a cue to the causal structure of a three-variable system, and also whether their judgements about the effects of interventions on the system would be affected by the temporal properties of the event sequence. Participants were shown a system in which two events B and C occurred either simultaneously (synchronous condition) or in a temporal sequence (sequential condition) following an initial event A. The causal judgements of adults and (...)
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  • Popper's severity of test as an intuitive probabilistic model of hypothesis testing.Fenna H. Poletiek - 2009 - Behavioral and Brain Sciences 32 (1):99-100.
    Severity of Test (SoT) is an alternative to Popper's logical falsification that solves a number of problems of the logical view. It was presented by Popper himself in 1963. SoT is a less sophisticated probabilistic model of hypothesis testing than Oaksford & Chater's (O&C's) information gain model, but it has a number of striking similarities. Moreover, it captures the intuition of everyday hypothesis testing.
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  • Dynamics and the Perception of Causal Events.Phillip Wolff - 2006 - Understanding Events.
    We use our knowledge of causal relationships to imagine possible events. We also use these relationships to look deep into the past and infer events that were not witnessed or to infer what can not be directly seen in the present. Knowledge of causal relationships allows us to go beyond the here and now. This chapter introduces a new theoretical framework for how this very basic concept might be mentally represented. It proposes an epistemological theory of causation — that is, (...)
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  • Of Blickets, Butterflies, and Baby Dinosaurs: Children’s Diagnostic Reasoning Across Domains.Deena Skolnick Weisberg, Elysia Choi & David M. Sobel - 2020 - Frontiers in Psychology 11:530564.
    The three studies presented here examine children’s ability to make diagnostic inferences about an interactive causal structure across different domains. Previous work has shown that children’s abilities to make diagnostic inferences about a physical system develops between the ages of 5 and 8. Experiments 1 ( N = 242) and 2 ( N = 112) replicate this work with 4- to 10-year-olds and demonstrate that this developmental trajectory is preserved when children reason about a closely matched biological system. Unlike Experiments (...)
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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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  • Concepts in change.Anna-Mari Rusanen & Samuli Pöyhönen - 2013 - Science & Education 22 (6):1389–1403.
    In this article we focus on the concept of concept in conceptual change. We argue that (1) theories of higher learning must often employ two different notions of concept that should not be conflated: psychological and scientific concepts. The usages for these two notions are partly distinct and thus straightforward identification between them is unwarranted. Hence, the strong analogy between scientific theory change and individual learning should be approached with caution. In addition, we argue that (2) research in psychology and (...)
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  • Combining Versus Analyzing Multiple Causes: How Domain Assumptions and Task Context Affect Integration Rules.Michael R. Waldmann - 2007 - Cognitive Science 31 (2):233-256.
    In everyday life, people typically observe fragments of causal networks. From this knowledge, people infer how novel combinations of causes they may never have observed together might behave. I report on 4 experiments that address the question of how people intuitively integrate multiple causes to predict a continuously varying effect. Most theories of causal induction in psychology and statistics assume a bias toward linearity and additivity. In contrast, these experiments show that people are sensitive to cues biasing various integration rules. (...)
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  • From Blickets to Synapses: Inferring Temporal Causal Networks by Observation.Chrisantha Fernando - 2013 - Cognitive Science 37 (8):1426-1470.
    How do human infants learn the causal dependencies between events? Evidence suggests that this remarkable feat can be achieved by observation of only a handful of examples. Many computational models have been produced to explain how infants perform causal inference without explicit teaching about statistics or the scientific method. Here, we propose a spiking neuronal network implementation that can be entrained to form a dynamical model of the temporal and causal relationships between events that it observes. The network uses spike-time (...)
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  • Analytic Causal Knowledge for Constructing Useable Empirical Causal Knowledge: Two Experiments on Pre‐schoolers.Patricia W. Cheng, Catherine M. Sandhofer & Mimi Liljeholm - 2022 - Cognitive Science 46 (5):e13137.
    Cognitive Science, Volume 46, Issue 5, May 2022.
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  • Language Signaling High Proportions and Generics Lead to Generalizing, but Not Essentializing, for Novel Social Kinds.Elena Hoicka, Jennifer Saul, Eloise Prouten, Laura Whitehead & Rachel Sterken - 2021 - Cognitive Science 45 (11):e13051.
    Cognitive Science, Volume 45, Issue 11, November 2021.
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  • Children’s quantitative Bayesian inferences from natural frequencies and number of chances.Stefania Pighin, Vittorio Girotto & Katya Tentori - 2017 - Cognition 168 (C):164-175.
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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 – namely, Behaviorism and evolutionary psychology – that set aside mechanistic explanations or make use of optimality assumptions. Through (...)
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  • The uncertain reasoner: Bayes, logic, and rationality.Mike Oaksford & Nick Chater - 2009 - 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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  • Associative learning or Bayesian inference? Revisiting backwards blocking reasoning in adults.Deon T. Benton & David H. Rakison - 2023 - Cognition 241 (C):105626.
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  • The Impact of a Construction Play on 5- to 6-Year-Old Children’s Reasoning About Stability.Anke Maria Weber, Timo Reuter & Miriam Leuchter - 2020 - Frontiers in Psychology 11:531199.
    Theory Young children have an understanding of basic science concepts such as stability, yet their theoretical assumptions are often not concerned with stability. The literature on theory theory and theory-evidence coordination suggests that children construct intuitive theories about their environment which can be adjusted in the face of counterevidence that cannot be assimilated into the prior theory. With increasing age, children acquire a Center theory when balancing objects and try to balance every object at their middle, succeeding with symmetrical objects. (...)
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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.
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  • Rational variability in children’s causal inferences: The Sampling Hypothesis.Stephanie Denison, Elizabeth Bonawitz, Alison Gopnik & Thomas L. Griffiths - 2013 - Cognition 126 (2):285-300.
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  • Is statistical learning constrained by lower level perceptual organization?Lauren L. Emberson, Ran Liu & Jason D. Zevin - 2013 - Cognition 128 (1):82-102.
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  • Changing Structures in Midstream: Learning Along the Statistical Garden Path.Andrea L. Gebhart, Richard N. Aslin & Elissa L. Newport - 2009 - Cognitive Science 33 (6):1087-1116.
    Previous studies of auditory statistical learning have typically presented learners with sequential structural information that is uniformly distributed across the entire exposure corpus. Here we present learners with nonuniform distributions of structural information by altering the organization of trisyllabic nonsense words at midstream. When this structural change was unmarked by low‐level acoustic cues, or even when cued by a pitch change, only the first of the two structures was learned. However, both structures were learned when there was an explicit cue (...)
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  • 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 (...)
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  • (1 other version)Letting Structure Emerge: Connectionist and Dynamical Systems Approaches to Cognition.Linda B. Smith James L. McClelland, Matthew M. Botvinick, David C. Noelle, David C. Plaut, Timothy T. Rogers, Mark S. Seidenberg - 2010 - Trends in Cognitive Sciences 14 (8):348.
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  • A Probabilistic Model of Melody Perception.David Temperley - 2008 - Cognitive Science 32 (2):418-444.
    This study presents a probabilistic model of melody perception, which infers the key of a melody and also judges the probability of the melody itself. The model uses Bayesian reasoning: For any “surface” pattern and underlying “structure,” we can infer the structure maximizing P(structure|surface) based on knowledge of P(surface, structure). The probability of the surface can then be calculated as ∑ P(surface, structure), summed over all structures. In this case, the surface is a pattern of notes; the structure is a (...)
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  • Probabilistic models as theories of children's minds.Alison Gopnik - 2011 - Behavioral and Brain Sciences 34 (4):200-201.
    My research program proposes that children have representations and learning mechanisms that can be characterized as causal models of the world Bayesian Fundamentalism.”.
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