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Benjamin M. Rottman [8]Benjamin Margolin Rottman [1]
  1.  88
    Causal Systems Categories: Differences in Novice and Expert Categorization of Causal Phenomena.Benjamin M. Rottman, Dedre Gentner & Micah B. Goldwater - 2012 - Cognitive Science 36 (5):919-932.
    We investigated the understanding of causal systems categories—categories defined by common causal structure rather than by common domain content—among college students. We asked students who were either novices or experts in the physical sciences to sort descriptions of real-world phenomena that varied in their causal structure (e.g., negative feedback vs. causal chain) and in their content domain (e.g., economics vs. biology). Our hypothesis was that there would be a shift from domain-based sorting to causal sorting with increasing expertise in the (...)
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  2.  86
    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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  3. Causal Inference When Observed and Unobserved Causes Interact.Benjamin M. Rottman & Woo-Kyoung Ahn - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. pp. 1477--1482.
    When a cause interacts with unobserved factors to produce an effect, the contingency between the observed cause and effect cannot be taken at face value to infer causality. Yet, it would be computationally intractable to consider all possible unobserved, interacting factors. Nonetheless, two experiments found that when an unobserved cause is assumed to be fairly stable over time, people can learn about such interactions and adjust their inferences about the causal efficacy of the observed cause. When they observed a period (...)
     
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  4. The Placebo Effect: To Explore or to Exploit?Kirsten Barnes, Benjamin Margolin Rottman & Ben Colagiuri - 2021 - Cognition 214:104753.
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  5.  27
    What Matters in Scientific Explanations: Effects of Elaboration and Content.Benjamin M. Rottman & Frank C. Keil - 2011 - Cognition 121 (3):324-337.
  6.  4
    Motivated Reasoning in an Explore-Exploit Task.Zachary A. Caddick & Benjamin M. Rottman - 2021 - Cognitive Science 45 (8):e13018.
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  7.  12
    Causal Strength Induction From Time Series Data.Kevin W. Soo & Benjamin M. Rottman - 2018 - Journal of Experimental Psychology: General 147 (4):485-513.
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  8.  4
    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.
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  9.  4
    Distinguishing Causation and Correlation: Causal Learning From Time-Series Graphs with Trends.Kevin W. Soo & Benjamin M. Rottman - 2020 - Cognition 195:104079.
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