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Sahil Luthra; Austin Luor; Adam T. Tierney; Frederic Dick; Lori L. Holt – npj Science of Learning, 2025
Humans implicitly pick up on probabilities of stimuli and events, yet it remains unclear how statistical learning builds expectations that affect perception. Across 29 experiments, we examine the influence of task-irrelevant distributions--defined across acoustic frequency--on both tone detection in noise and tone duration judgments. The shape and…
Descriptors: Probability, Statistics, Expectation, Auditory Perception
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Agus, Mirian; Peró-Cebollero, Maribel; Guàrdia-Olmos, Joan; Portoghese, Igor; Mascia, Maria Lidia; Penna, Maria Pietronilla – EURASIA Journal of Mathematics, Science and Technology Education, 2020
This paper reports some experiments on probabilistic reasoning designed to investigate the impact of the probabilistic problem presentation format (verbal-numerical and graphical-pictorial) on subjects' confidence in the correctness of their performance, other than the calibration between confidence and accuracy. To understand the potential effect…
Descriptors: Accuracy, Self Efficacy, Context Effect, Statistics
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Morsanyi, Kinga; Handley, Simon J.; Serpell, Sylvie – British Journal of Educational Psychology, 2013
Background: The equiprobability bias is a tendency for individuals to think of probabilistic events as "equiprobable" by nature, and to judge outcomes that occur with different probabilities as equally likely. The equiprobability bias has been repeatedly found to be related to formal education in statistics, and it is claimed to be based…
Descriptors: Probability, Bias, Training, Cognitive Ability
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Hilbig, Benjamin E. – Cognition, 2012
Extending the well-established negativity bias in human cognition to truth judgments, it was recently shown that negatively framed statistical statements are more likely to be considered true than formally equivalent statements framed positively. However, the underlying processes responsible for this effect are insufficiently understood.…
Descriptors: Response Style (Tests), Value Judgment, Probability, Models
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Hahn, Ulrike; Warren, Paul A. – Psychological Review, 2010
In this postscript the authors summarize where Sun, Tweney, and Wang (see record 2010-06891-015) now agree with their original analysis and where differences of opinion remain. Sun et al.'s (see record 2010-06891-018) postscript contrasted two positions, one emphasizing the "limited and finite nature of people's experience" and one emphasizing the…
Descriptors: Probability, Statistics, Differences, Opinions
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Hahn, Ulrike; Warren, Paul A. – Psychological Review, 2010
We (Hahn & Warren, 2009) recently proposed a new account of the systematic errors and biases that appear to be present in people's perception of randomly generated events. In a comment on that article, Sun, Tweney, and Wang (2010) critiqued our treatment of the gambler's fallacy. We had argued that this fallacy was less gross an error than it…
Descriptors: Probability, Incidence, Prediction, Misconceptions
Stilling, Stephanie T.; Critchfield, Thomas S. – Journal of the Experimental Analysis of Behavior, 2010
The utility of a quantitative model depends on the extent to which its fitted parameters vary systematically with environmental events of interest. Professional football statistics were analyzed to determine whether play selection (passing versus rushing plays) could be accounted for with the generalized matching equation, and in particular…
Descriptors: Play, Team Sports, Probability, Bias
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Sun, Yanlong; Tweney, Ryan D.; Wang, Hongbin – Psychological Review, 2010
In this postscript the authors applaud Hahn and Warren's effort in their reply to remove the ambiguity in distinguishing events such as occurrence, occurrence at least once, and nonoccurrence in Hahn and Warren (2009). Still, it appears that differences between us exist regarding the nature of waiting time and its connections to the probability of…
Descriptors: Probability, Statistics, Logical Thinking, Philosophy
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Bedford, Crayton W. – Mathematics Teacher, 1972
The Wilcoxon two-sample test used to examine judge bias. (MM)
Descriptors: Bias, Mathematics, Probability, Statistical Analysis