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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)
Leganes-Fonteneau, Mateo; Nikolaou, Kyriaki; Scott, Ryan; Duka, Theodora – Learning & Memory, 2019
Stimuli conditioned with a substance can generate drug-approach behaviors due to their acquired motivational properties. According to implicit theories of addiction, these stimuli can decrease cognitive control automatically. The present study (n = 49) examined whether reward-associated stimuli can interfere with cognitive processes in the absence…
Descriptors: Knowledge Level, Rewards, Conditioning, Bayesian Statistics
Gershman, Samuel J.; Blei, David M.; Niv, Yael – Psychological Review, 2010
A. Redish et al. (2007) proposed a reinforcement learning model of context-dependent learning and extinction in conditioning experiments, using the idea of "state classification" to categorize new observations into states. In the current article, the authors propose an interpretation of this idea in terms of normative statistical inference. They…
Descriptors: Conditioning, Statistical Inference, Inferences, Bayesian Statistics
Gallistel, C. R. – Psychological Review, 2009
Null hypotheses are simple, precise, and theoretically important. Conventional statistical analysis cannot support them; Bayesian analysis can. The challenge in a Bayesian analysis is to formulate a suitably vague alternative, because the vaguer the alternative is (the more it spreads out the unit mass of prior probability), the more the null is…
Descriptors: Bayesian Statistics, Statistical Analysis, Probability, Hypothesis Testing