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Jarecki, Jana B.; Meder, Björn; Nelson, Jonathan D. – Cognitive Science, 2018
Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class-conditional independence of features. This feature independence assumption simplifies the inference…
Descriptors: Classification, Conditioning, Inferences, Novelty (Stimulus Dimension)
Wu, Charley M.; Meder, Björn; Filimon, Flavia; Nelson, Jonathan D. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2017
While the influence of presentation formats have been widely studied in Bayesian reasoning tasks, we present the first systematic investigation of how presentation formats influence information search decisions. Four experiments were conducted across different probabilistic environments, where subjects (N = 2,858) chose between 2 possible search…
Descriptors: Questioning Techniques, Information Seeking, Search Strategies, Search Engines
Nelson, Jonathan D. – Psychological Review, 2007
Reports an error in "Finding Useful Questions: On Bayesian Diagnosticity, Probability, Impact, and Information Gain" by Jonathan D. Nelson (Psychological Review, 2005[Oct], Vol 112[4], 979-999). In Table 13, the data should indicate that 7% of females had short hair and 93% of females had long hair. The calculations and discussion in the article…
Descriptors: Probability, Females, Norms, Bayesian Statistics

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