On aggregation invariance of multinomial processing tree models.

Multinomial processing tree (MPT) models are prominent and frequently used tools to model and measure cognitive processes underlying responses in many experimental paradigms. Although MPT models typically refer to cognitive processes within single individuals, they have often been applied to group data aggregated across individuals. We investigate the conditions under which MPT analyses of aggregate data make sense.

Sequential analysis of variance: Increasing efficiency of hypothesis testing.

Researchers commonly use analysis of variance (ANOVA) to statistically test results of factorial designs. Performing an a priori power analysis is crucial to ensure that the ANOVA is sufficiently powered, however, it often poses a challenge and can result in large sample sizes, especially if the expected effect size is small. Due to the high prevalence of small effect sizes in psychology, studies are frequently underpowered as it is often economically unfeasible to gather the necessary sample size for adequate Type-II error control.

A deep learning method for comparing Bayesian hierarchical models.

Bayesian model comparison (BMC) offers a principled approach to assessing the relative merits of competing computational models and propagating uncertainty into model selection decisions. However, BMC is often intractable for the popular class of hierarchical models due to their high-dimensional nested parameter structure. To address this intractability, we propose a deep learning method for performing BMC on any set of hierarchical models which can be instantiated as probabilistic programs.

Post-hypnotic suggestion improves confidence and speed of memory access with long-lasting effects.

In our study, we use the post-hypnotic suggestion of easy remembering to improve memory with long-lasting effects. We tested 24 highly suggestible participants in an online study. Participants learned word lists and recalled them later in a recognition memory task.

Waldian t tests: Sequential Bayesian t tests with controlled error probabilities.

Bayesian tests have become increasingly popular alternatives to null-hypothesis significance testing (NHST) in psychological research. In contrast to NHST, they allow for the quantification of evidence in favor of the null hypothesis and for optional stopping. A major drawback of Bayesian tests, however, is that error probabilities of statistical decisions remain uncontrolled.

On the efficiency of the independent segments procedure: A direct comparison with sequential probability ratio tests.

In this comment, we report a simulation study that assesses error rates and average sample sizes required to reach a statistical decision for two sequential procedures, the sequential probability ratio test (SPRT) originally proposed by Wald (1947) and the independent segments procedure (ISP) recently suggested by Miller and Ulrich (2020). Following Miller and Ulrich (2020), we use sequential one-tailed t tests as examples. In line with the optimal efficiency properties of the SPRT already proven by Wald and Wolfowitz (1948), the SPRT outperformed the ISP in terms of efficiency without compromising error probability control.

The truth revisited: Bayesian analysis of individual differences in the truth effect.

The repetition-induced truth effect refers to a phenomenon where people rate repeated statements as more likely true than novel statements. In this paper, we document qualitative individual differences in the effect. While the overwhelming majority of participants display the usual positive truth effect, a minority are the opposite-they reliably discount the validity of repeated statements, what we refer to as negative truth effect.

Improving the efficiency of surveys with randomized response models: A sequential approach based on curtailed sampling.

Randomized response models (RRMs) aim at increasing the validity of measuring sensitive attributes by eliciting more honest responses through anonymity protection of respondents. This anonymity protection is achieved by implementing randomization in the questioning procedure. On the other hand, this randomization increases the sampling variance and, therefore, increases sample size requirements.

Predictors of focal to bilateral tonic-clonic seizures during long-term video-EEG monitoring.

Objective: To determine predictors of focal to bilateral tonic-clonic seizures (FBTCS) during video-electroencephalography (EEG) monitoring (VEM).

Methods: All adult patients undergoing presurgical VEM from 2014 to 2015 in the department of epileptology were eligible (N = 229). Those with refractory focal epilepsy and epileptic seizures recorded during VEM were analyzed (N = 188, Group 1).

Controlling decision errors with minimal costs: The sequential probability ratio t test.

For several years, the public debate in psychological science has been dominated by what is referred to as the reproducibility crisis. This crisis has, inter alia, drawn attention to the need for proper control of statistical decision errors in testing psychological hypotheses. However, conventional methods of error probability control often require fairly large samples.

Homogeneity of item material boosts the list length effect in recognition memory: A global matching perspective.

Kinnell and Dennis (2012) showed that the list length effect in recognition memory is only observed for homogeneous stimulus material. On the basis of the global matching model MINERVA 2 (Hintzman, 1986, 1988), we offer a theoretical explanation for this finding. According to our analysis, homogeneous material immunizes against the disruptive influence of preexperimental items, which might mask the intralist interference predicted by global matching models for familiar heterogeneous material.