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  1.  7
    Traditional Difference-Score Analyses of Reasoning Are Flawed.Evan Heit & Caren M. Rotello - 2014 - Cognition 131 (1):75-91.
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  2.  13
    Assessing the Belief Bias Effect with ROCs: It's a Response Bias Effect.Chad Dube, Caren M. Rotello & Evan Heit - 2010 - Psychological Review 117 (3):831-863.
  3. Why Learning and Development Can Lead to Poorer Recognition Memory.Brett K. Hayes & Evan Heit - 2004 - Trends in Cognitive Sciences 8 (8):337-339.
  4.  5
    The Belief Bias Effect is Aptly Named: A Reply to Klauer and Kellen.Chad Dube, Caren M. Rotello & Evan Heit - 2011 - Psychological Review 118 (1):155-163.
  5.  27
    The Pervasive Effects of Argument Length on Inductive Reasoning.Evan Heit & Caren M. Rotello - 2012 - Thinking and Reasoning 18 (3):244 - 277.
    Three experiments examined the influence of argument length on plausibility judgements, in a category-based induction task. The general results were that when arguments were logically invalid they were considered stronger when they were longer, but for logically valid arguments longer arguments were considered weaker. In Experiments 1a and 1b when participants were forewarned to avoid using length as a cue to judging plausibility, they still did so. Indeed, participants given the opposite instructions did not follow those instructions either. In Experiment (...)
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  6. Inductive Reasoning: Experimental, Developmental, and Computational Approaches.Aidan Feeney & Evan Heit (eds.) - 2007 - Cambridge University Press.
    Without inductive reasoning, we couldn't generalize from one instance to another, derive scientific hypotheses, or predict that the sun will rise again tomorrow morning. Despite the widespread nature of inductive reasoning, books on this topic are rare. Indeed, this is the first book on the psychology of inductive reasoning in twenty years. The chapters survey recent advances in the study of inductive reasoning and address questions about how it develops, the role of knowledge in induction, how best to model people's (...)
     
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  7. Models of Inductive Reasoning.Evan Heit - 2008 - In Ron Sun (ed.), The Cambridge Handbook of Computational Psychology. Cambridge University Press. pp. 322--338.
  8.  50
    Properties of the Diversity Effect in Category-Based Inductive Reasoning.Aidan Feeney & Evan Heit - 2011 - Thinking and Reasoning 17 (2):156 - 181.
    Four experiments investigated how people judge the plausibility of category-based arguments, focusing on the diversity effect, in which arguments with diverse premise categories are considered particularly strong. In Experiment 1 we show that priming people as to the nature of the blank property determines whether sensitivity to diversity is observed. In Experiment 2 we find that people's hypotheses about the nature of the blank property predict judgements of argument strength. In Experiment 3 we examine the effect of our priming methodology (...)
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  9.  77
    Feature-Sampling and Random-Walk Models of Individual-Stimulus Recognition.Koen Lamberts, Noellie Brockdorff & Evan Heit - 2003 - Journal of Experimental Psychology: General 132 (3):351.
  10.  5
    Relations Among Categorization, Induction, Recognition, and Similarity: Comment on Sloutsky and Fisher.Evan Heit & Brett K. Hayes - 2005 - Journal of Experimental Psychology: General 134 (4):596-605.
  11.  20
    Observational Learning From Internal Feedback: A Simulation of an Adaptive Learning Method.Dorrit Billman & Evan Heit - 1988 - Cognitive Science 12 (4):587-625.
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  12.  52
    Missing the Party: Political Categorization and Reasoning in the Absence of Party Label Cues.Evan Heit & Stephen P. Nicholson - 2016 - Topics in Cognitive Science 8 (3):697-714.
    This research addressed theoretical approaches in political science arguing that the American electorate is either poorly informed or dependent on party label cues, by assessing performance on political judgment tasks when party label information is missing. The research materials were created from the results of a national opinion survey held during a national election. The experiments themselves were run on nationally representative samples of adults, identified from another national electoral survey. Participants saw profiles of simulated individuals, including information about demographics (...)
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  13.  5
    Brain Imaging, Forward Inference, and Theories of Reasoning.Evan Heit - 2014 - Frontiers in Human Neuroscience 8.
  14. Metacognitive Judgments of Improvement Are Uncorrelated with Learning Rate.C. Townsend & Evan Heit - 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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  15.  8
    Predicting Reasoning From Memory.Evan Heit & Brett K. Hayes - 2011 - Journal of Experimental Psychology: General 140 (1):76-101.
  16.  35
    Predicting Reasoning From Visual Memory.Evan Heit & Brett K. Hayes - 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. 83--88.
  17.  41
    The Opposite of Republican: Polarization and Political Categorization.Evan Heit & Stephen P. Nicholson - 2010 - Cognitive Science 34 (8):1503-1516.
    Two experiments examined the typicality structure of contrasting political categories. In Experiment 1, two separate groups of participants rated the typicality of 15 individuals, including political figures and media personalities, with respect to the categories Democrat or Republican. The relation between the two sets of ratings was negative, linear, and extremely strong, r = −.9957. Essentially, one category was treated as a mirror image of the other. Experiment 2 replicated this result, showing some boundary conditions, and extending the result to (...)
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  18.  71
    Illuminating Reasoning and Categorization.Evan Heit & Brett K. Hayes - 2005 - Behavioral and Brain Sciences 28 (1):27-27.
    The proposal regarding rules and similarity is considered in terms of ability to provide insights regarding previous work on reasoning and categorization. For reasoning, the issue is the relation between this proposal and one-process as well as two-process accounts of deduction and induction. For categorization, the issue is how the proposal would simultaneously explain both similarity-to-rule and rule-to-similarity shifts.
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  19.  17
    In Praise of Secular Bayesianism.Evan Heit & Shanna Erickson - 2011 - Behavioral and Brain Sciences 34 (4):202-202.
    It is timely to assess Bayesian models, but Bayesianism is not a religion. Bayesian modeling is typically used as a tool to explain human data. Bayesian models are sometimes equivalent to other models, but have the advantage of explicitly integrating prior hypotheses with new observations. Any lack of representational or neural assumptions may be an advantage rather than a disadvantage.
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  20.  10
    Metacognition and Confidence: Comparing Math to Other Academic Subjects.Shanna Erickson & Evan Heit - 2015 - Frontiers in Psychology 6.
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  21.  9
    A Dynamic Model of Reasoning and Memory.Guy E. Hawkins, Brett K. Hayes & Evan Heit - 2016 - Journal of Experimental Psychology: General 145 (2):155-180.
  22.  8
    Representational Shifts Made Visible: Movement Away From the Prototype in Memory for Hue.Laura J. Kelly & Evan Heit - 2014 - Frontiers in Psychology 5.
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  23.  3
    Putting Replication in its Place.Evan Heit & Caren M. Rotello - 2018 - Behavioral and Brain Sciences 41.
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  24.  6
    What is the Probability of the Bayesian Model, Given the Data?Evan Heit - 2001 - Behavioral and Brain Sciences 24 (4):672-673.
    The great advantage of Tenenbaum and Griffiths's model is that it incorporates both specific and general prior knowledge into category learning. Two phenomena are presented as supporting the detailed assumptions of this model. However, one phenomenon, effects of diversity, does not seem to require these assumptions, and the other phenomenon, effects of sample size, is not representative of most reported results. [Tenenbaum & Griffiths].
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