Psychonomic bulletin & review
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Contextual cuing refers to a response time (RT) benefit that occurs when observers search through displays that have been repeated over the course of an experiment. Although it is generally agreed that contextual cuing arises via an associative learning mechanism, there is uncertainty about the type(s) of process(es) that allow learning to influence RT. We contrast two leading accounts of the contextual cuing effect that differ in terms of the general process that is credited with producing the effect. ⋯ Our results reveal both individual differences in the process(es) involved in contextual cuing but also identify several striking regularities across observers. We find strong support for both the decision threshold account as well as the novel perceptual learning account. We find relatively weak support for the expedited search account.
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This article explores whether the null hypothesis significance testing (NHST) framework provides a sufficient basis for the evaluation of statistical model assumptions. It is argued that while NHST-based tests can provide some degree of confirmation for the model assumption that is evaluated-formulated as the null hypothesis-these tests do not inform us of the degree of support that the data provide for the null hypothesis and to what extent the null hypothesis should be considered to be plausible after having taken the data into account. ⋯ Without assessing the prior plausibility of the model assumptions, it remains fully uncertain whether the model of interest gives an adequate description of the data and thus whether it can be considered valid for the application at hand. Although addressing the prior plausibility is difficult, ignoring the prior plausibility is not an option if we want to claim that the inferences of our statistical model can be relied upon.
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Markov Chain Monte-Carlo (MCMC) is an increasingly popular method for obtaining information about distributions, especially for estimating posterior distributions in Bayesian inference. This article provides a very basic introduction to MCMC sampling. It describes what MCMC is, and what it can be used for, with simple illustrative examples. Highlighted are some of the benefits and limitations of MCMC sampling, as well as different approaches to circumventing the limitations most likely to trouble cognitive scientists.
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In the practice of data analysis, there is a conceptual distinction between hypothesis testing, on the one hand, and estimation with quantified uncertainty on the other. Among frequentists in psychology, a shift of emphasis from hypothesis testing to estimation has been dubbed "the New Statistics" (Cumming 2014). ⋯ The article reviews frequentist and Bayesian approaches to hypothesis testing and to estimation with confidence or credible intervals. The article also describes Bayesian approaches to meta-analysis, randomized controlled trials, and power analysis.
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Numerous studies have demonstrated that associative learning can affect visual cognition. In one such effect, search times for a target hidden among similar distractors are faster for repeated search configurations compared with novel configurations. This contextual cuing effect is particularly interesting, because researchers routinely have failed to find evidence of recognition of the repeated configurations, concluding that the effect is a form of nonconscious learning. ⋯ The data support the absence of a positive relationship between recognition and the cuing effect both at the participant and configuration level, the probability of which being a false negative is very low in a model assuming a single memory source drives learning and awareness. This was the case using both conventional and Bayesian analyses. The combination of this theoretical and empirical analysis suggests that contextual cuing is not dependent on cue recognition and provides evidence that it reflects a genuine form of nonconscious learning.