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In recent years, discussions comparing high-threshold and continuous accounts of recognition-memory judgments have increasingly turned their attention toward critical testing. One of the de ning features of this approach is its requirement for the relationship between theoretical assumptions and predictions to be laid out in a transparent and precise way. One of the (fortunate) consequences of this requirement is that it encourages researchers to debate the merits of the different assumptions at play. The present work addresses a recent attempt to overturn the dismissal of high-threshold models by getting rid of a background selective- in uence assumption. However, it can be shown that the contrast process proposed to explain this violation undermines a more general assumption that we dubbed“single-item generalization.” We argue that the case for the dismissal of these assumptions and the claimed support for the proposed high-threshold contrast account does not stand the scrutiny of their theoretical properties and empirical implications. 

Abstract We develop alternative families of Bayes factors for use in hypothesis tests as alternatives to the popular default Bayes factors. The alternative Bayes factors are derived for the statistical analyses most commonly used in psychological research – one-sample and two-samplet tests, regression, and ANOVA analyses. They possess the same desirable theoretical and practical properties as the default Bayes factors and satisfy additional theoretical desiderata while mitigating against two features of the default priors that we consider implausible. They can be conveniently computed via an R package that we provide. Furthermore, hypothesis tests based on Bayes factors and those based on significance tests are juxtaposed. This discussion leads to the insight that default Bayes factors as well as the alternative Bayes factors are equivalent to test-statistic-based Bayes factors as proposed by Johnson.Journal of the Royal Statistical Society Series B: Statistical Methodology,67, 689–701. (2005). We highlight test-statistic-based Bayes factors as a general approach to Bayes-factor computation that is applicable to many hypothesis-testing problems for which an effect-size measure has been proposed and for which test power can be computed. 

Individuals’ decisions under risk tend to be in line with the notion that“losses loom larger than gains.” This loss aversion in decision making is commonly understood as a stable individual preference that is manifested across different contexts. The presumed stability and generality, which underlies the prominence of loss aversion in the literature at large, has been recently questioned by studies reporting how loss aversion can disappear, and even reverse, as a function of the choice context. The present study investigated whether loss aversion re ects a trait-like attitude of avoiding losses or rather individuals’ adaptability to different con- texts. We report three experiments investigating the within-subject context sensitivity of loss aversion in a two-alternative forced-choice task. Our results show that the choice context can shift people’s loss aversion, though somewhat inconsistently. Moreover, individual estimates of loss aversion are shown to have a con- siderable degree of stability. Altogether, these results indicate that even though the absolute value of loss aversion can be affected by external factors such as the choice context, estimates of people’s loss aversion still capture the relative dispositions toward gains and losses across individuals.