38 research outputs found

    A comparison of university student and community gamblers: Motivations, impulsivity, and gambling cognitions

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    Abstract Background and aims The present study tested whether the associations among motivational, cognitive, and personality correlates of problem gambling severity differed across university student gamblers (n = 123) and gamblers in the general adult community (n = 113) Methods The participants completed a survey that included standardized measures of gambling motivation, gambling related cognitions, and impulsivity. The survey also asked participants to report the forms of gambling in which they engaged to test whether gambling involvement (number of different forms of gambling) was related to problem gambling severity. After completing the survey, participants played roulette online to examine whether betting patterns adhered to the gambler's fallacy. Results Gambling involvement was significantly related to problem gambling severity for the community sample but not for the student sample. A logistic regression analysis that tested the involvement, motivation, impulsivity and cognitive correlates showed that money motivation and gambling related cognitions were the only significant independent predictors of gambling severity. Adherence to the gambler's fallacy was stronger for students than for the community sample, and was associated with gambling related cognitions. Discussion The motivational, impulsivity and cognitive, and correlates of problem gambling function similarly in university student gamblers and in gamblers from the general adult community. Interventions for both groups should focus on the financial and cognitive supports of problem gambling

    On the origin of replication effect size differences

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    Erratum to: Processing letters in words at different levels

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    Evidence against the computation of abstract letter identities in visual processing.

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    Processing letters in words at different levels

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    Hierarchical organization as a determinant of sequential learning

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