19 found
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  1.  43
    Categorization as causal reasoning⋆.Bob Rehder - 2003 - Cognitive Science 27 (5):709-748.
    A theory of categorization is presented in which knowledge of causal relationships between category features is represented in terms of asymmetric and probabilistic causal mechanisms. According to causal‐model theory, objects are classified as category members to the extent they are likely to have been generated or produced by those mechanisms. The empirical results confirmed that participants rated exemplars good category members to the extent their features manifested the expectations that causal knowledge induces, such as correlations between feature pairs that are (...)
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  2.  42
    Causal‐Based Property Generalization.Bob Rehder - 2009 - Cognitive Science 33 (3):301-344.
    A central question in cognitive research concerns how new properties are generalized to categories. This article introduces a model of how generalizations involve a process of causal inference in which people estimate the likely presence of the new property in individual category exemplars and then the prevalence of the property among all category members. Evidence in favor of this causal‐based generalization (CBG) view included effects of an existing feature’s base rate (Experiment 1), the direction of the causal relations (Experiments 2 (...)
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  3.  18
    Category coherence and category-based property induction.Bob Rehder & Reid Hastie - 2004 - Cognition 91 (2):113-153.
  4.  33
    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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  5.  67
    The Development of Causal Categorization.Brett K. Hayes & Bob Rehder - 2012 - Cognitive Science 36 (6):1102-1128.
    Two experiments examined the impact of causal relations between features on categorization in 5‐ to 6‐year‐old children and adults. Participants learned artificial categories containing instances with causally related features and noncausal features. They then selected the most likely category member from a series of novel test pairs. Classification patterns and logistic regression were used to diagnose the presence of independent effects of causal coherence, causal status, and relational centrality. Adult classification was driven primarily by coherence when causal links were deterministic (...)
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  6.  3
    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 learning via interactions with (...)
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  7.  41
    The Development of Causal Categorization.Brett K. Hayes & Bob Rehder - 2012 - Cognitive Science 36 (6):1102-1128.
    Two experiments examined the impact of causal relations between features on categorization in 5- to 6-year-old children and adults. Participants learned artificial categories containing instances with causally related features and noncausal features. They then selected the most likely category member from a series of novel test pairs. Classification patterns and logistic regression were used to diagnose the presence of independent effects of causal coherence, causal status, and relational centrality. Adult classification was driven primarily by coherence when causal links were deterministic (...)
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  8.  15
    The role of causal models in multiple judgments under uncertainty.Brett K. Hayes, Guy E. Hawkins, Ben R. Newell, Martina Pasqualino & Bob Rehder - 2014 - Cognition 133 (3):611-620.
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  9.  22
    Essentialism as a generative theory of classification.Bob Rehder - 2007 - In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press. pp. 190--207.
  10.  27
    A Process Model of Causal Reasoning.Zachary J. Davis & Bob Rehder - 2020 - Cognitive Science 44 (5):e12839.
    How do we make causal judgments? Many studies have demonstrated that people are capable causal reasoners, achieving success on tasks from reasoning to categorization to interventions. However, less is known about the mental processes used to achieve such sophisticated judgments. We propose a new process model—the mutation sampler—that models causal judgments as based on a sample of possible states of the causal system generated using the Metropolis–Hastings sampling algorithm. Across a diverse array of tasks and conditions encompassing over 1,700 participants, (...)
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  11.  23
    A causal model theory of categorization.Bob Rehder - 1999 - In Martin Hahn & S. C. Stoness (eds.), Proceedings of the 21st Annual Meeting of the Cognitive Science Society. Lawrence Erlbaum. pp. 595--600.
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  12.  26
    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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  13. A new theory of classification and feature inference learning: An exemplar fragment model.B. Colner & Bob Rehder - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. pp. 371--376.
     
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  14. Feature inference and eyetracking.Bob Colner, Bob Rehder & Aaron B. Hoffman - 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. 1170--1175.
     
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  15. Attentional and representational flexibility of feature inference learning.Aaron B. Hoffman & Bob Rehder - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. pp. 1864--1869.
     
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  16. Knowledge effect the selective attention in category learning: An eyetracking study.S. Kim & Bob Rehder - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. pp. 230--235.
  17. The role of coherence in causal-based categorization.Bob Rehder & S. Kim - 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. 285--290.
     
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  18.  17
    Taking the rationality out of probabilistic models.Bob Rehder - 2011 - Behavioral and Brain Sciences 34 (4):210-211.
    Rational models vary in their goals and sources of justification. While the assumptions of some are grounded in the environment, those of others are induced and so require more traditional sources of justification, such as generalizability to dissimilar tasks and making novel predictions. Their contribution to scientific understanding will remain uncertain until standards of evidence are clarified.
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  19.  22
    Why does explaining help learning? Insight from an explanation impairment effect.Joseph Jay Williams, Tania Lombrozo & Bob Rehder - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society.
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