Neural and Behavioral Evidence for Vibrotactile Beat Perception and Bimodal Enhancement

The ability to synchronize movements to a rhythmic stimulus, referred to as sensorimotor synchronization (SMS), is a behavioral measure of beat perception. Although SMS is generally superior when rhythms are presented in the auditory modality, recent research has demonstrated near-equivalent SMS for vibrotactile presentations of isochronous rhythms [Ammirante, P., Patel, A. D., & Russo, F. A. Synchronizing to auditory and tactile metronomes: A test of the auditory–motor enhancement hypothesis. Psychonomic Bulletin & Review, 23, 1882–1890, 2016]. The current study aimed to replicate and extend this study by incorporating a neural measure of beat perception. Nonmusicians were asked to tap to rhythms or to listen passively while EEG data were collected. Rhythmic complexity (isochronous, nonisochronous) and presentation modality (auditory, vibrotactile, bimodal) were fully crossed. Tapping data were consistent with those observed by Ammirante et al. (2016), revealing near-equivalent SMS for isochronous rhythms across modality conditions and a drop-off in SMS for nonisochronous rhythms, especially in the vibrotactile condition. EEG data revealed a greater degree of neural entrainment for isochronous compared to nonisochronous trials as well as for auditory and bimodal compared to vibrotactile trials. These findings led us to three main conclusions. First, isochronous rhythms lead to higher levels of beat perception than nonisochronous rhythms across modalities. Second, beat perception is generally enhanced for auditory presentations of rhythm but still possible under vibrotactile presentation conditions. Finally, exploratory analysis of neural entrainment at harmonic frequencies suggests that beat perception may be enhanced for bimodal presentations of rhythm.

Why optional stopping can be a problem for Bayesians

Recently, optional stopping has been a subject of debate in the Bayesian psychology community. Rouder (Psychonomic Bulletin & Review21(2), 301–308, 2014) argues that optional stopping is no problem for Bayesians, and even recommends the use of optional stopping in practice, as do (Wagenmakers, Wetzels, Borsboom, van der Maas & Kievit, Perspectives on Psychological Science7, 627–633, 2012). This article addresses the question of whether optional stopping is problematic for Bayesian methods, and specifies under which circumstances and in which sense it is and is not. By slightly varying and extending Rouder’s (Psychonomic Bulletin & Review21(2), 301–308, 2014) experiments, we illustrate that, as soon as the parameters of interest are equipped with default or pragmatic priors—which means, in most practical applications of Bayes factor hypothesis testing—resilience to optional stopping can break down. We distinguish between three types of default priors, each having their own specific issues with optional stopping, ranging from no-problem-at-all (type 0 priors) to quite severe (type II priors).

Are small additions solved by direct retrieval from memory or automated counting procedures? A rejoinder to Chen and Campbell (2018)

Contrary to the longstanding and consensual hypothesis that adults mainly solve small single-digit additions by directly retrieving their answer from long-term memory, it has been recently argued that adults could solve small additions through fast automated counting procedures. In a recent article, Chen and Campbell (Psychonomic Bulletin & Review, 25, 739–753, 2018) reviewed the main empirical evidence on which this alternative hypothesis is based, and concluded that there is no reason to jettison the retrieval hypothesis. In the present paper, we pinpoint the fact that Chen and Campbell reached some of their conclusions by excluding some of the problems that need to be considered for a proper argumentation against the automated counting procedure theory. We also explain why, contrary to Chen and Campbell’s assumption, the network interference model proposed by Campbell (Mathematical Cognition, 1, 121–164, 1995) cannot account for our data. Finally, we clarify a theoretical point of our model.

The Contributions of Singular and Plural Nouns to Sentence Processing Complexity: Evidence from Reading Time

The nature of semantic representations of plural nouns has been a subject of debates in the literature. The present research investigated the extent to which there are differences in the processing of plural versus single noun descriptions (e.g., the large chairs vs. the large chair). In two reading experiments, we tested whether plural (versus singular) nouns appearing in sentences were more difficult to process initially and/or led to increased processing difficulty when occurring in sentences that contain a temporary syntactic ambiguity. Reading time on syntactically ambiguous sentences containing plural or singular nouns were compared with reading time on unambiguous control sentences. The results of both experiments demonstrated significant effects of sentence ambiguity. No effects or interactions involving noun number were observed, indicating that the complexity of plural nouns does not result in processing difficulty during sentence comprehension. 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Fast and accurate calculations for first-passage times in Wiener diffusion models

We propose a new method for quickly calculating the probability density function for first-passage times in simple Wiener diffusion models, extending an earlier method used by [Van Zandt, T., Colonius, H., & Proctor, R. W. (2000). A comparison of two response-time models applied to perceptual matching. Psychonomic Bulletin & Review, 7, 208–256]. The method relies on the observation that there are two distinct infinite series expansions of this probability density, one of which converges quickly for small time values, while the other converges quickly at large time values. By deriving error bounds associated with finite truncation of either expansion, we are able to determine analytically which of the two versions should be applied in any particular context. The bounds indicate that, even for extremely stringent error tolerances, no more than 8 terms are required to calculate the probability density. By making the calculation of this distribution tractable, the goal is to allow more complex extensions of Wiener diffusion models to be developed.

Accounting for Estimation Uncertainty and Shrinkage in Bayesian Within-Subject Intervals: A Comment on Nathoo, Kilshaw, and Masson (2018)

To facilitate the interpretation of systematic mean differences in within-subject designs, Nathoo, Kilshaw, and Masson (2018, Journal of Mathematical Psychology, 86, 1-9) proposed a Bayesian within-subject highest-density interval (HDI). However, their approach rests on independent maximum-likelihood estimates for the random effects which do not take estimation uncertainty and shrinkage into account. I propose an extension of Nathoo et al.'s method using a fully Bayesian, two-step approach. First, posterior samples are drawn for the linear mixed model. Second, the within-subject HDI is computed repeatedly based on the posterior samples, thereby accounting for estimation uncertainty and shrinkage. After marginalizing over the posterior distribution, the two-step approach results in a Bayesian within-subject HDI with a width similar to that of the classical within-subject confidence interval proposed by Loftus and Masson (1994, Psychonomic Bulletin & Review, 1, 476-490).

Problematic low power in many Psychonomic Society journal articles

Low power, often the result of an insufficient number of participants, contributes to replication failures (e.g. Button et al., 2013). It is usually recommended that studies have at least an 80% chance of identifying an effect. However, following Cohen’s (1962) work, many reviews have reported that studies are under-powered. We calculated the power to detect small, medium and large effects of a test from the first study in all relevant Psychonomic Society journal articles published in 2017. Overall, mean power to detect medium sized effects was only 65%, and two-thirds of papers failed to achieve 80% power. Nearly half of papers in Psychonomic Bulletin & Review and Memory & Cognition achieved the 80% criteria, but other journals were much lower. The mean power to detect small effects was 22%, indicating that they would almost always be missed. Larger samples are generally needed to avoiding missing real effects.

Quantifying Support for the Null Hypothesis in Psychology: An Empirical Investigation

In the traditional statistical framework, nonsignificant results leave researchers in a state of suspended disbelief. In this study, we examined, empirically, the treatment and evidential impact of nonsignificant results. Our specific goals were twofold: to explore how psychologists interpret and communicate nonsignificant results and to assess how much these results constitute evidence in favor of the null hypothesis. First, we examined all nonsignificant findings mentioned in the abstracts of the 2015 volumes of Psychonomic Bulletin & Review, Journal of Experimental Psychology: General, and Psychological Science ( N = 137). In 72% of these cases, nonsignificant results were misinterpreted, in that the authors inferred that the effect was absent. Second, a Bayes factor reanalysis revealed that fewer than 5% of the nonsignificant findings provided strong evidence (i.e., BF01 > 10) in favor of the null hypothesis over the alternative hypothesis. We recommend that researchers expand their statistical tool kit in order to correctly interpret nonsignificant results and to be able to evaluate the evidence for and against the null hypothesis.

Editorial: Bayesian methods for advancing psychological science

The editorial for a Special Issue of the journal Psychonomic Bulletin & Review.

Triple play: Additive contributions of enhanced expectancies, autonomy support, and external attentional focus to motor learning

In the OPTIMAL theory of motor learning [Wulf, G., & Lewthwaite, R. (2016). Optimizing performance through intrinsic motivation and attention for learning: The OPTIMAL theory of motor learning. Psychonomic Bulletin & Review, 23, 1382-1414], 3 factors are postulated to facilitate learning: enhanced expectancies (EE) for performance, autonomy support (AS), and an external focus (EF) of attention. In 3 recent studies, combinations of 2 of these variables resulted in superior learning relative to the presence of only 1 variable, or none. We examined whether the combination of all 3 factors would enhance learning relative to combinations of 2 factors. Our design included EE–AS, EE–EF, AS–EF, and AS–EE–EF groups. Participants threw balls at a target with their non-dominant arm. In the EE conditions, they received positive social–comparative feedback. In the AS conditions, they were allowed to throw with their dominant arm on trial blocks chosen by them. In the EF conditions, participants were asked to focus on the target. On a delayed retention test, the AS–EE–EF group outperformed all other groups. The findings provide evidence that enhanced expectancies, autonomy support, and an external focus can contribute in an additive fashion to optimize motor learning.