Introduction to Bayesian Inference for Psychology

A tutorial on fitting joint models of M/EEG and behavior to understand cognition

We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document.

A third kind of episodic memory: Context familiarity is a distinct process from item familiarity and recollection

Episodic memory is accounted for with two processes: 'familiarity' when generally recognizing an item and 'recollection' when retrieving the full contextual details bound with the item. Paradoxically, people sometimes report contextual information as familiar but without recollecting details, which is not easily accounted for by existing theories. We tested a combination of item recognition confidence and source memory, focusing upon 'item-only hits with source unknown' ('item familiarity'), 'low-confidence hits with correct source memory' ('context familiarity'), and 'high-confidence hits with correct source memory' ('recollection'). Results across multiple within-subjects (trial-wise) and between subjects (individual variability) levels indicated these were behaviorally and physiologically distinct. Behaviorally, a crossover interaction was evident in response times, with context familiarity being slower than each condition during item recognition, but faster during source memory. Electrophysiologically, a Condition x Time x Location triple dissociation was evident in event-related potentials (ERPs), which was then independently replicated. Context familiarity exhibited an independent negative central effect from 800-1200 ms, differentiated from positive ERPs for item-familiarity (400 to 600 ms) and recollection (600 to 900 ms). These three conditions thus reflect mutually exclusive, fundamentally different processes of episodic memory. Context familiarity is a third distinct process of episodic memory.

Significance statement/Summary

Memory for past events is widely believed to operate through two different processes: one called 'recollection' when retrieving confident, specific details of a memory, and another called 'familiarity' when only having an unsure but conscious awareness that an item was experienced before. When people successfully retrieve details such as the source or context of a prior event, it has been assumed to reflect recollection. We demonstrate that familiarity of context is functionally distinct from familiarity of items and recollection and offer a new trivariate model. The three memory responses were differentiated across multiple behavioral and brain wave measures. What has traditionally been thought to be two kinds of memory processes are actually three, becoming evident when using sensitive enough multi-measures. Results are independently replicated across studies from different labs. These data reveal that context familiarity is a third process of human episodic memory.

Bayesian hierarchical modeling: an introduction and reassessment

With the recent development of easy-to-use tools for Bayesian analysis, psychologists have started to embrace Bayesian hierarchical modeling. Bayesian hierarchical models provide an intuitive account of inter- and intraindividual variability and are particularly suited for the evaluation of repeated-measures designs. Here, we provide guidance for model specification and interpretation in Bayesian hierarchical modeling and describe common pitfalls that can arise in the process of model fitting and evaluation. Our introduction gives particular emphasis to prior specification and prior sensitivity, as well as to the calculation of Bayes factors for model comparisons. We illustrate the use of state-of-the-art software programs Stan and brms. The result is an overview of best practices in Bayesian hierarchical modeling that we hope will aid psychologists in making the best use of Bayesian hierarchical modeling.

Heuristic strategy of intuitive statistical inferences in 7- to 10-year-old children

Intuitive statistical inferences refer to making inferences about uncertain events based on limited probabilistic information, which is crucial for both human and non-human species' survival and reproduction. Previous research found that 7- and 8-year-old children failed in intuitive statistical inference tasks after heuristic strategies had been controlled. However, few studies systematically explored children's heuristic strategies of intuitive statistical inferences and their potential numerical underpinnings. In the current research, Experiment 1 (N = 81) examined 7- to 10-year-olds' use of different types of heuristic strategies; results revealed that children relied more on focusing on the absolute number strategy. Experiment 2 (N = 99) and Experiment 3 (N = 94) added continuous-format stimuli to examine whether 7- and 8-year-olds could make genuine intuitive statistical inferences instead of heuristics. Results revealed that both 7- and 8-year-olds and 9- and 10-year-olds performed better in intuitive statistical inference tasks with continuous-format stimuli, even after focusing on the absolute number strategy had been controlled. The results across the three experiments preliminarily hinted that the ratio processing system might rely on the approximate number system. Future research could clarify what specific numerical processing mechanism may be used and how it might support children's statistical intuitions.

Utility of Single Items within the Suicidal Behaviors Questionnaire-Revised (SBQ-R): A Bayesian Network Approach and Relative Importance Analysis

The Suicidal Behaviors Questionnaire-Revised (SBQ-R) comprises four content-specific items widely used to assess the history of suicide-related thoughts, plans or attempts, frequency of suicidal ideation, communication of intent to die by suicide and self-reported likelihood of a suicide attempt. Each item focuses on a specific parameter of the suicide-related thoughts and behaviors construct. Past research has primarily focused on the total score. This study used Bayesian network modeling and relative importance analyses on SBQ-R data from 1160 U.S. and 1141 Chinese undergraduate students. The Bayesian network analysis results showed that Item 1 is suitable for identifying other parameters of the suicide-related thoughts and behaviors construct. The results of the relative importance analysis further highlighted the relevancy of each SBQ-R item score when examining evidence for suicide-related thoughts and behaviors. These findings provided empirical support for using the SBQ-R item scores to understand the performances of different suicide-related behavior parameters. Further, they demonstrated the potential value of examining individual item-level responses to offer clinically meaningful insights. To conclude, the SBQ-R allows for the evaluation of each critical suicide-related thought and behavior parameter and the overall suicide risk.

The long and the short of it: Salivary telomere length as a candidate biomarker for hypertension and age-related changes in blood pressure

Hypertension becomes more prevalent with increasing age. Telomere length (TL) has been proposed as a candidate biomarker and can be accessibly extracted from saliva. However, clarity is needed to evaluate the suitability of using TL as a predictor in such instances. This study investigated salivary TL in a cohort of older adults from the 2008 Health and Retirement Study (n = 3329; F: 58%, mean age: 69.4, SD: 10.3 years) to examine any associations with blood pressure (BP). A Bayesian robust regression model was fit using weakly informative priors to predict the effects of TL with age, sex, systolic BP (SBP), diastolic BP (DBP), and treatment status. There were small effects of treatment (β: -0.07, 95% CrI [-0.33, 0.19], pd: 71.91%) and sex (β: -0.10, 95% CrI [-0.27, 0.07], pd: >86.78%). Population effects showed a reduction of 0.01 log2 units in TL with each year of advancing age (95% CrI [-0.01, -0.00]). Conditional posterior predictions suggest that females, and treated individuals, experience greater change in TL with increasing age. Bayes R2 was ~2%. TL declines with increasing age, differs between sexes, and appears to be influenced by antihypertensive drugs. Overall, all effects were weak. The data do not currently support the suitability of salivary TL as a biomarker to predict or understand any age-related changes in BP.

The effect of individual characteristics on susceptibility to aggressive and/or intimidating approaches: quantifying probability pathways by creating a victimization model

Background: A significant body of literature has identified multiple factors that contribute to established victimization by aggressive and/or intimidating behaviours. These studies primarily originate from the fields of intimate partner violence (IPV), bullying, sexual abuse, and/or commercial sexual exploitation (CSE), and generally focus on female victims. It appears, however, complicated to quantify the cumulative contribution of these factors on susceptibility to intimidating and/or hostile engagements on an individual level.Objective: To develop a comprehensive risk model to quantify, on an individual level, the cumulative effects of previously reported characteristics on susceptibility to aggressive/intimidating approaches, leading to victimization (e.g. in the context of IPV/sexual abuse).Methods: A Bayesian belief network was developed using data from previous studies, capturing the multivariate contribution of previously reported characteristics on the likelihood of becoming victimized by aggressive and/or intimidating approaches (e.g. in the IPV/CSE context) in female victims aged 12-24 years.Results: The model showed that specific combinations of characteristics may contribute to an increased likelihood of victimization (e.g. in the context of IPV/bullying/sexual abuse or CSE). This likelihood could be quantified and categorized into specific clusters of factors differentiating between victimization by physically violent, non-physical, and/or sexual aggressive/intimidating approaches.Conclusion: The present model appears to be the first to successfully quantify the cumulative contribution of individual characteristics on the likelihood of becoming victimized by aggressive and/or intimidating approaches, typically leading to victimization. Moreover, the present scientific effort and resulting model suggest that there may be a latent variable mediating between the implemented factors and overall outcome, i.e. the susceptibility to aggressive and/or intimidating approaches. From that perspective, the model may also be considered as an initial outline to effectively indicate susceptibility to such approaches.

baymedr: an R package and web application for the calculation of Bayes factors for superiority, equivalence, and non-inferiority designs

BACKGROUND

Clinical trials often seek to determine the superiority, equivalence, or non-inferiority of an experimental condition (e.g., a new drug) compared to a control condition (e.g., a placebo or an already existing drug). The use of frequentist statistical methods to analyze data for these types of designs is ubiquitous even though they have several limitations. Bayesian inference remedies many of these shortcomings and allows for intuitive interpretations, but are currently difficult to implement for the applied researcher.

RESULTS

We outline the frequentist conceptualization of superiority, equivalence, and non-inferiority designs and discuss its disadvantages. Subsequently, we explain how Bayes factors can be used to compare the relative plausibility of competing hypotheses. We present baymedr, an R package and web application, that provides user-friendly tools for the computation of Bayes factors for superiority, equivalence, and non-inferiority designs. Instructions on how to use baymedr are provided and an example illustrates how existing results can be reanalyzed with baymedr.

CONCLUSIONS

Our baymedr R package and web application enable researchers to conduct Bayesian superiority, equivalence, and non-inferiority tests. baymedr is characterized by a user-friendly implementation, making it convenient for researchers who are not statistical experts. Using baymedr, it is possible to calculate Bayes factors based on raw data and summary statistics.

Can synchronised tones facilitate immediate memory for printed lists?

In verbal list recall, adding features redundant with the ones to be recalled theoretically could assist recall, by providing additional retrieval cues, or it could impede recall, by draining attention away from the features to be recalled. We examined young adults' immediate memory of lists of printed digits when these lists were sometimes accompanied by synchronised, concurrent tones, one per digit. Unlike most previous irrelevant-sound effects, the tones were not asynchronous with the printed items, which can corrupt the episodic record, and did not repeat within a list. Memory of the melody might bring to mind the associated digits like lyrics in a song. Sometimes there were instructions to sing the digits covertly in the tone pitches. In three experiments, there was no evidence that these methods enhanced memory. Instead, there appeared to be a distraction effect from the synchronised tones, as in the irrelevant sound effect with asynchronised tones.

Mathematical modelling with Bayesian inference to quantitatively characterize therapeutic cell behaviour in nerve tissue engineering

Cellular engineered neural tissues have significant potential to improve peripheral nerve repair strategies. Traditional approaches depend on quantifying tissue behaviours using experiments in isolation, presenting a challenge for an overarching framework for tissue design. By comparison, mathematical cell-solute models benchmarked against experimental data enable computational experiments to be performed to test the role of biological/biophysical mechanisms, as well as to explore the impact of different design scenarios and thus accelerate the development of new treatment strategies. Such models generally consist of a set of continuous, coupled, partial differential equations relying on a number of parameters and functional forms. They necessitate dedicated in vitro experiments to be informed, which are seldom available and often involve small datasets with limited spatio-temporal resolution, generating uncertainties. We address this issue and propose a pipeline based on Bayesian inference enabling the derivation of experimentally informed cell-solute models describing therapeutic cell behaviour in nerve tissue engineering. We apply our pipeline to three relevant cell types and obtain models that can readily be used to simulate nerve repair scenarios and quantitatively compare therapeutic cells. Beyond parameter estimation, the proposed pipeline enables model selection as well as experiment utility quantification, aimed at improving both model formulation and experimental design.

Why does visual working memory ability improve with age: More objects, more feature detail, or both? A registered report

We investigated how visual working memory (WM) develops with age across the early elementary school period (6-7 years), early adolescence (11-13 years), and early adulthood (18-25 years). The work focuses on changes in two parameters: the number of objects retained at least in part, and the amount of feature-detail remembered for such objects. Some evidence suggests that, while infants can remember up to three objects, much like adults, young children only remember around two objects. This curious, nonmonotonic trajectory might be explained by differences in the level of feature-detail required for successful performance in infant versus child/adult memory paradigms. Here, we examined if changes in one of two parameters (the number of objects, and the amount of detail retained for each object) or both of them together can explain the development of visual WM ability as children grow older. To test it, we varied the amount of feature-detail participants need to retain. In the baseline condition, participants saw an array of objects and simply were to indicate whether an object was present in a probed location or not. This phase begun with a titration procedure to adjust each individual's array size to yield about 80% correct. In other conditions, we tested memory of not only location but also additional features of the objects (color, and sometimes also orientation). Our results suggest that capacity growth across ages is expressed by both improved location-memory (whether there was an object in a location) and feature completeness of object representations.

Let's Talk about Stats: Revising Our Approach to Teaching Statistics in Psychology

Undergraduate statistics in psychology is an important, often challenging, course for students. The focus in psychology tends to be on hypothesis tests, such as t tests and analysis of variance. While adequate for some questions, there are many other topics we might include that could improve that data analytic abilities of students and improve psychological science in the long run. Topics such as generalized linear modeling, multilevel modeling, Bayesian statistics, model building and comparison, and causal analysis, could be introduced in an undergraduate psychological statistics course. For each topic, I discuss their importance and provide sources for instructor's continuing education. These topics would give students greater flexibility in analyzing data, allow them to conduct more meaningful analyses, allow them to understand more modern data analytic approaches, and potentially help the field of psychology in the long run, by being one part of the strategy to address the reproducibility problem.

Big little lies: a compendium and simulation of p-hacking strategies

In many research fields, the widespread use of questionable research practices has jeopardized the credibility of scientific results. One of the most prominent questionable research practices is p-hacking. Typically, p-hacking is defined as a compound of strategies targeted at rendering non-significant hypothesis testing results significant. However, a comprehensive overview of these p-hacking strategies is missing, and current meta-scientific research often ignores the heterogeneity of strategies. Here, we compile a list of 12 p-hacking strategies based on an extensive literature review, identify factors that control their level of severity, and demonstrate their impact on false-positive rates using simulation studies. We also use our simulation results to evaluate several approaches that have been proposed to mitigate the influence of questionable research practices. Our results show that investigating p-hacking at the level of strategies can provide a better understanding of the process of p-hacking, as well as a broader basis for developing effective countermeasures. By making our analyses available through a Shiny app and R package, we facilitate future meta-scientific research aimed at investigating the ramifications of p-hacking across multiple strategies, and we hope to start a broader discussion about different manifestations of p-hacking in practice.

Bayesian analysis of Ecological Momentary Assessment (EMA) data collected in adults before and after hearing rehabilitation

This paper presents a new Bayesian method for analyzing Ecological Momentary Assessment (EMA) data and applies this method in a re-analysis of data from a previous EMA study. The analysis method has been implemented as a freely available Python package EmaCalc, RRID:SCR 022943. The analysis model can use EMA input data including nominal categories in one or more situation dimensions, and ordinal ratings of several perceptual attributes. The analysis uses a variant of ordinal regression to estimate the statistical relation between these variables. The Bayesian method has no requirements related to the number of participants or the number of assessments by each participant. Instead, the method automatically includes measures of the statistical credibility of all analysis results, for the given amount of data. For the previously collected EMA data, the analysis results demonstrate how the new tool can handle heavily skewed, scarce, and clustered data that were collected on ordinal scales, and present results on interval scales. The new method revealed results for the population mean that were similar to those obtained in the previous analysis by an advanced regression model. The Bayesian approach automatically estimated the inter-individual variability in the population, based on the study sample, and could show some statistically credible intervention results also for an unseen random individual in the population. Such results may be interesting, for example, if the EMA methodology is used by a hearing-aid manufacturer in a study to predict the success of a new signal-processing method among future potential customers.

Efficiency in sequential testing: Comparing the sequential probability ratio test and the sequential Bayes factor test

In a sequential hypothesis test, the analyst checks at multiple steps during data collection whether sufficient evidence has accrued to make a decision about the tested hypotheses. As soon as sufficient information has been obtained, data collection is terminated. Here, we compare two sequential hypothesis testing procedures that have recently been proposed for use in psychological research: Sequential Probability Ratio Test (SPRT; Psychological Methods, 25(2), 206-226, 2020) and the Sequential Bayes Factor Test (SBFT; Psychological Methods, 22(2), 322-339, 2017). We show that although the two methods have different philosophical roots, they share many similarities and can even be mathematically regarded as two instances of an overarching hypothesis testing framework. We demonstrate that the two methods use the same mechanisms for evidence monitoring and error control, and that differences in efficiency between the methods depend on the exact specification of the statistical models involved, as well as on the population truth. Our simulations indicate that when deciding on a sequential design within a unified sequential testing framework, researchers need to balance the needs of test efficiency, robustness against model misspecification, and appropriate uncertainty quantification. We provide guidance for navigating these design decisions based on individual preferences and simulation-based design analyses.

With Bayesian estimation one can get all that Bayes factors offer, and more

In classical statistics, there is a close link between null hypothesis significance testing (NHST) and parameter estimation via confidence intervals. However, for the Bayesian counterpart, a link between null hypothesis Bayesian testing (NHBT) and Bayesian estimation via a posterior distribution is less straightforward, but does exist, and has recently been reiterated by Rouder, Haaf, and Vandekerckhove (2018). It hinges on a combination of a point mass probability and a probability density function as prior (denoted as the spike-and-slab prior). In the present paper, it is first carefully explained how the spike-and-slab prior is defined, and how results can be derived for which proofs were not given in Rouder, Haaf, and Vandekerckhove (2018). Next, it is shown that this spike-and-slab prior can be approximated by a pure probability density function with a rectangular peak around the center towering highly above the remainder of the density function. Finally, we will indicate how this 'hill-and-chimney' prior may in turn be approximated by fully continuous priors. In this way, it is shown that NHBT results can be approximated well by results from estimation using a strongly peaked prior, and it is noted that the estimation itself offers more than merely the posterior odds on which NHBT is based. Thus, it complies with the strong APA requirement of not just mentioning testing results but also offering effect size information. It also offers a transparent perspective on the NHBT approach employing a prior with a strong peak around the chosen point null hypothesis value.

Frequentist and Bayesian Hypothesis Testing: An Intuitive Guide for Urologists and Clinicians

Given the limitations of frequentist method for null hypothesis significance testing, different authors recommend alternatives such as Bayesian inference. A poor understanding of both statistical frameworks is common among clinicians. The present is a gentle narrative review of the frequentist and Bayesian methods intended for physicians not familiar with mathematics. The frequentist p-value is the probability of finding a value equal to or higher than that observed in a study, assuming that the null hypothesis (H0) is true. The H0 is rejected or not based on a p threshold of 0.05, and this dichotomous approach does not express the probability that the alternative hypothesis (H1) is true. The Bayesian method calculates the probability of H1 and H0 considering prior odds and the Bayes factor (Bf). Prior odds are the researcher's belief about the probability of H1, and the Bf quantifies how consistent the data is concerning H1 and H0. The Bayesian prediction is not dichotomous but is expressed in continuous scales of the Bf and of the posterior odds. The JASP software enables the performance of both frequentist and Bayesian analyses in a friendly and intuitive way, and its application is displayed at the end of the paper. In conclusion, the frequentist method expresses how consistent the data is with H0 in terms of p-values, with no consideration of the probability of H1. The Bayesian model is a more comprehensive prediction because it quantifies in continuous scales the evidence for H1 versus H0 in terms of the Bf and the posterior odds.

A novel and effective item-source complex trial protocol: Discrimination of guilty from both knowledgeable and unknowledgeable innocent subjects

Innocent subjects who are knowledgeable of crime-related information will often be misclassified as "guilty" in P300-based complex trial protocol (CTP). Therefore, it is necessary to develop a more rigorous CTP that can effectively discriminate the guilty from both the knowledgeable and the unknowledgeable innocents. Sometimes the guilty and the knowledgeable innocents possess the same item memories but different source memories. The present study designed a novel item-source complex trial protocol based on the differences of source memory among the three kinds of individuals. Either the crime-related probe (e.g., the stolen ring) or one of the crime-unrelated stimuli (e.g., watch, earring, bracelet, or bangle) (item memory) was presented in the first part of each trail, and either a stealing-source word (e.g., stole) or other-source word (e.g., fetched) (source memory) was presented in the second part of each trail. The results showed that: (1) the P300 evoked by item memory could effectively discriminated the guilty from the unknowledgeable innocent (AUC = 0.76) but failed to effectively discriminate the guilty from the knowledgeable innocent (AUC = 0.60); (2) the late positive component evoked by source memory could effectively discriminated the guilty from both the knowledgeable innocent (AUC = 0.94) and the unknowledgeable innocent (AUC = 0.84) in one test.

Exposure to humans after weaning does not reduce the behavioural reactivity of extensively reared Merino lambs

Human-directed fear in extensively reared sheep is often high due to the aversive nature of common husbandry procedures and infrequent interactions with humans. This study investigated whether additional human exposure provided to weaned lambs reduced human-directed fear and behavioural reactivity. Ninety Merino lambs were either exposed to low or moderately stressful human exposure sessions, or had no additional human contact, and their fear responses and behavioural reactivity to humans, a startle stimulus and confinement were tested. Overall, the imposed interventions did not reduce behavioural reactivity during these tests, suggesting fear towards humans had not been altered.

A practical guide for researchers and reviewers using the ABCD Study and other large longitudinal datasets

As the largest longitudinal study of adolescent brain development and behavior to date, the Adolescent Brain Cognitive Development (ABCD) Study® has provided immense opportunities for researchers across disciplines since its first data release in 2018. The size and scope of the study also present a number of hurdles, which range from becoming familiar with the study design and data structure to employing rigorous and reproducible analyses. The current paper is intended as a guide for researchers and reviewers working with ABCD data, highlighting the features of the data (and the strengths and limitations therein) as well as relevant analytical and methodological considerations. Additionally, we explore justice, equity, diversity, and inclusion efforts as they pertain to the ABCD Study and other large-scale datasets. In doing so, we hope to increase both accessibility of the ABCD Study and transparency within the field of developmental cognitive neuroscience.

A Bayesian method for measuring risk propensity in the Balloon Analogue Risk Task

The Balloon Analogue Risk Task (BART) is widely-used to measure risk propensity in theoretical, clinical, and applied research. In the task, people choose either to pump a balloon to increase its value at the risk of the balloon bursting and losing all value, or to bank the current value of the balloon. Risk propensity is most commonly measured as the average number of pumps on trials for which the balloon does not burst. Burst trials are excluded because they necessarily underestimate the number of pumps people intended to make. However, their exclusion discards relevant information about people's risk propensity. A better measure of risk propensity uses the statistical method of censoring to incorporate all of the trials. We develop a new Bayesian method, based on censoring, for measuring both risk propensity and behavioral consistency in the BART. Through applications to previous data we demonstrate how the method can be extended to consider the correlation of risk propensity with external measures, and to compare differences in risk propensity between groups. We provide implementations of all of these methods in R, MATLAB, and the GUI-based statistical software JASP.

Combine Statistical Thinking With Open Scientific Practice: A Protocol of a Bayesian Research Project

Current developments in the statistics community suggest that modern statistics education should be structured holistically, that is, by allowing students to work with real data and to answer concrete statistical questions, but also by educating them about alternative frameworks, such as Bayesian inference. In this article, we describe how we incorporated such a holistic structure in a Bayesian research project on ordered binomial probabilities. The project was conducted with a group of three undergraduate psychology students who had basic knowledge of Bayesian statistics and programming, but lacked formal mathematical training. The research project aimed to (1) convey the basic mathematical concepts of Bayesian inference; (2) have students experience the entire empirical cycle including collection, analysis, and interpretation of data and (3) teach students open science practices.

Reporting all results efficiently: A RARE proposal to open up the file drawer

While the social sciences have made impressive progress in adopting transparent research practices that facilitate verification, replication, and reuse of materials, the problem of publication bias persists. Bias on the part of peer reviewers and journal editors, as well as the use of outdated research practices by authors, continues to skew literature toward statistically significant effects, many of which may be false positives. To mitigate this bias, we propose a framework to enable authors to report all results efficiently (RARE), with an initial focus on experimental and other prospective empirical social science research that utilizes public study registries. This framework depicts an integrated system that leverages the capacities of existing infrastructure in the form of public registries, institutional review boards, journals, and granting agencies, as well as investigators themselves, to efficiently incentivize full reporting and thereby, improve confidence in social science findings. In addition to increasing access to the results of scientific endeavors, a well-coordinated research ecosystem can prevent scholars from wasting time investigating the same questions in ways that have not worked in the past and reduce wasted funds on the part of granting agencies.

Aspiring to greater intellectual humility in science

The replication crisis in the social, behavioural and life sciences has spurred a reform movement aimed at increasing the credibility of scientific studies. Many of these credibility-enhancing reforms focus, appropriately, on specific research and publication practices. A less often mentioned aspect of credibility is the need for intellectual humility or being transparent about and owning the limitations of our work. Although intellectual humility is presented as a widely accepted scientific norm, we argue that current research practice does not incentivize intellectual humility. We provide a set of recommendations on how to increase intellectual humility in research articles and highlight the central role peer reviewers can play in incentivizing authors to foreground the flaws and uncertainty in their work, thus enabling full and transparent evaluation of the validity of research.

An intuitive framework for Bayesian posterior simulation methods

Purpose

Bayesian inference has become popular. It offers several pragmatic approaches to account for uncertainty in inference decision-making. Various estimation methods have been introduced to implement Bayesian methods. Although these algorithms are powerful, they are not always easy to grasp for non-statisticians. This paper aims to provide an intuitive framework of four essential Bayesian computational methods for epidemiologists and other health researchers. We do not cover an extensive mathematical discussion of these approaches, but instead offer a non-quantitative description of these algorithms and provide some illuminating examples.

Materials and methods

Bayesian computational methods, namely importance sampling, rejection sampling, Markov chain Monte Carlo, and data augmentation are presented.

Results and conclusions

The substantial amount of research published on Bayesian inference has highlighted its popularity among researchers, while the basic concepts are not always straightforward for interested learners. We show that alternative approaches such as a weighted prior approach, which are intuitively appealing and easy-to-understand, work well in the case of low-dimensional problems and appropriate prior information. Otherwise, MCMC is a trouble-free tool in those cases.

How Pain Shapes Depression and Anxiety: A Hybrid Self-regulatory/Predictive Mind Perspective

Because many persons living with chronic pain achieve a relatively balanced lifestyle without experiencing functional disability, medical psychologists must explain the well-documented co-occurrence of pain complaints and DSM-5-disorders (including but not limited to depression and anxiety) in a significant subset of individuals. The question of differential resilience versus susceptibility has received modest theoretical and empirical attention, but remains open. In this review, I deconstruct the temporally extended pain adaptation process in order to address this vexing question, relying upon two complementary explanatory frames. The first is a motivational/cybernetic systems formulation labeled the Goal-Centered, Self-Regulatory, Automated Social Systems Psychology (GRASSP) model, erected upon feedback sensitive, goal-guided, hierarchically organized self-regulatory processes. Depression and anxiety presumably result from compromised regulatory functions undermining pain processing, goal pursuit, and everyday performance. The second perspective postulates a "Bayesian Brain"/"Predictive Mind" capable of unifying perception, action, and emotion via predictive processing. From a Bayesian perspective, predictive processing implies that our brains evolved to compare, without conscious direction, incoming environmental information against prior, model-based predictions in order to arrive at accurate perceptual representations of the world. Maladjustment results from failures of active inference. When applied to the perception of visceral information, the embodied process, termed interoceptive inference, can also yield pathogenic outcomes. The Bayesian model holds that depression and anxiety in individuals with pain result from error-prone (biased, rigid, or highly certain) prior evaluations of aversive feeling states and their relation to the external milieu. I consider how the hybrid conceptual framework advanced by the two models points to several novel and familiar avenues of intervention.

Divergent effects of oxytocin on "mind-reading" in healthy males

The neuropeptide oxytocin (OT) has been associated with a broad range of human behaviors, particularly in the domain of social cognition, and is being discussed to play a role in a range of psychiatric disorders. Studies using the Reading The Mind In The Eyes Test (RMET) to investigate the role of OT in mental state recognition reported inconsistent outcomes. The present study applied a randomized, double-blind, cross-over design, and included measures of serum OT. Twenty healthy males received intranasal placebo or OT (24 IU) before performing the RMET. Frequentist and Bayesian analyses showed that contrary to previous studies (Domes et al., 2007; Radke & de Bruijn, 2015), individuals performed worse in the OT condition compared to the placebo condition (p = 0.023, Cohen’s d = 0.55, 95% confidence interval [CI] [0.08, 1.02], BF₁₀ = 6.93). OT effects did not depend on item characteristics (difficulty, valence, intensity, sex) of the RMET. Furthermore, OT serum levels did not change after intranasal OT administration. Given that similar study designs lead to heterogeneous outcomes, our results highlight the complexity of OT effects and support evidence that OT might even interfere with social cognitive abilities. However, the Bayesian analysis approach shows that there is only moderate evidence that OT influences mind-reading, highlighting the need for larger-scale studies considering the discussed aspects that might have led to divergent study results.

Association between the donor to recipient ICG-PDR variation rate and the functional recovery of the graft after orthotopic liver transplantation: A case series

Background

Despite current advances in liver transplant surgery, post-operative early allograft dysfunction still complicates the patient prognosis and graft survival. The transition from the donor has not been yet fully understood, and no study quantifies if and how the liver function changes through its transfer to the recipient. The indocyanine green dye plasma disappearance rate (ICG-PDR) is a simple validated tool of liver function assessment. The variation rate between the donor and recipient ICG-PDR still needs to be investigated.

Materials and methods

Single-center retrospective study. ICG-PDR determinations were performed before graft retrieval (T1) and 24 hours after transplant (T2). The ICG-PDR relative variation rate between T1 and T2 was calculated to assess the graft function and suffering/recovering. Matched data were compared with the MEAF model of graft dysfunction.

Objective

To investigate whether the variation rate between the donor ICG-PDR value and the recipient ICG-PDR measurement on first postoperative day (POD₁) can be associated with the MEAF score.

Results

36 ICG-PDR measurements between 18 donors and 18 graft recipients were performed. The mean donor ICG-PDR was 22.64 (SD 6.35), and the mean receiver’s ICG-PDR on 1st POD was 17.68 (SD 6.60), with a mean MEAF value of 4.51 (SD 1.23). Pearson’s test stressed a good, linear inverse correlation between the ICG-PDR relative variation and the MEAF values, correlation coefficient -0.580 (p = 0.012).

Conclusion

The direct correlation between the donor to recipient ICG-PDR variation rate and MEAF was found. Measurements at T1 and T2 showed an up- or downtrend of the graft performance that reflect the MEAF values.

Model-Based Wisdom of the Crowd for Sequential Decision-Making Tasks

We study the wisdom of the crowd in three sequential decision-making tasks: the Balloon Analogue Risk Task (BART), optimal stopping problems, and bandit problems. We consider a behavior-based approach, using majority decisions to determine crowd behavior and show that this approach performs poorly in the BART and bandit tasks. The key problem is that the crowd becomes progressively more extreme as the decision sequence progresses, because the diversity of opinion that underlies the wisdom of the crowd is lost. We also consider model-based approaches to each task. This involves inferring cognitive models for each individual based on their observed behavior, and using these models to predict what each individual would do in any possible task situation. We show that this approach performs robustly well for all three tasks and has the additional advantage of being able to generalize to new problems for which there are no behavioral data. We discuss potential applications of the model-based approach to real-world sequential decision problems and discuss how our approach contributes to the understanding of collective intelligence.

Order processing of number symbols is influenced by direction, but not format

This study probed the cognitive mechanisms that underlie order processing for number symbols, specifically the extent to which the direction and format in which number symbols are presented influence the processing of numerical order, as well as the extent to which the relationship between numerical order processing and mathematical achievement is specific to Arabic numerals or generalisable to other notational formats. Seventy adults who were bilingual in English and Chinese completed a Numerical Ordinality Task, using number sequences of various directional conditions (i.e., ascending, descending, mixed) and notational formats (i.e., Arabic numerals, English number words, and Chinese number words). Order processing was found to occur for ascending and descending number sequences (i.e., ordered but not non-ordered trials), with the overall pattern of data supporting the theoretical perspective that the strength and closeness of associations between items in the number sequence could underlie numerical order processing. However, order processing was found to be independent of the notational format in which the numerical stimuli were presented, suggesting that the psychological representations and processes associated with numerical order are abstract across different formats of number symbols. In addition, a relationship between the processing speed for numerical order judgements and mathematical achievement was observed for Arabic numerals and Chinese number words, and to a weaker extent, English number words. Together, our findings have started to uncover the cognitive mechanisms that could underlie order processing for different formats of number symbols, and raise new questions about the generalisability of these findings to other notational formats.

Cortical Activation to Social and Mechanical Stimuli in the Infant Brain

From the early days of life infants distinguish between social and non-social physical entities and have different expectations for the way these two entities should move and interact. At the same time, we know very little about the cortical systems that support this early emerging ability. The goal of the current research was to assess the extent to which infant's processing of social and non-social physical entities is mediated by distinct information processing systems in the temporal cortex. Using a cross-sectional design, infants aged 6-9 months (Experiment 1) and 11-18 months (Experiment 2) were presented with two types of events: social interaction and mechanical interaction. In the social interaction event (patterned after Hamlin et al., 2007), an entity with googly eyes, hair tufts, and an implied goal of moving up the hill was either helped up, or pushed down, a hill through the actions of another social entity. In the mechanical interaction event, the googly eyes and hair tufts were replaced with vertical black dots and a hook and clasp, and the objects moved up or down the hill via mechanical interactions. FNIRS was used to measure activation from temporal cortex while infants viewed the test events. In both age groups, viewing social and mechanical interaction events elicited different patterns of activation in the right temporal cortex, although responses were more specialized in the older age group. Activation was not obtained in these areas when the objects moved in synchrony without interacting, suggesting that the causal nature of the interaction events may be responsible, in part, to the results obtained. This is one of the few fNIRS studies that has investigated age-related patterns of cortical activation and the first to provide insight into the functional development of networks specialized for processing of social and non-social physical entities engaged in interaction events.

Beyond the p-value: Bayesian Statistics and Causation

Statistical paradigms limit the perspective and tools social work researchers use to study the world and answer questions impacting people and policy. Currently, quantitative social work researchers overwhelmingly rely on the frequentist paradigm of statistics. This paper discusses foundational differences between the frequentist and Bayesian statistical paradigms, describes basic concepts of Bayesian analysis, compares Bayesian and frequentist statistical analysis for a sample social work problem, and introduces two types of causal analyses built on Bayesian statistical thinking: counterfactual causality, and causality based on work by computer scientist Judea Pearl. Implications for social work research are discussed.

Tracking intermediate performance of vigilant attention using multiple eye metrics

Vigilance deficits account for a substantial number of accidents and errors. Current techniques to detect vigilance impairment measure only the most severe level evident in eyelid closure and falling asleep, which is often too late to avoid an accident or error. The present study sought to identify ocular biometrics of intermediate impairment of vigilance and develop a new technique that could detect a range of deficits in vigilant attention (VA). Sixteen healthy adults performed well-validated Psychomotor Vigilance Test (PVT) for tracking vigilance attention while undergoing simultaneous recording of eye metrics every 2 hours during 38 hours of continuous wakefulness. A novel marker was found that measured VA when the eyes were open-the prevalence of microsaccades. Notably, the prevalence of microsaccades decreased in response to sleep deprivation and time-on-task. In addition, a novel algorithm for detecting multilevel VA was developed, which estimated performance on the PVT by integrating the novel marker with other eye-related indices. The novel algorithm also tracked changes in intermediate level of VA (specific reaction times in the PVT, i.e. 300-500 ms) during prolonged time-on-task and sleep deprivation, which had not been tracked previously by conventional techniques. The implication of the findings is that this novel algorithm, named "eye-metrical estimation version of the PVT: PVT-E," can be used to reduce human-error-related accidents caused by vigilance impairment even when its level is intermediate.

Phonological but not semantic influences on the speech-to-song illusion

In the speech to song illusion, a spoken phrase begins to sound as if it is being sung after several repetitions. Castro et al. (2018) used Node Structure Theory (NST; MacKay, 1987), a model of speech perception and production, to explain how the illusion occurs. Two experiments further test the mechanisms found in NST-priming, activation, and satiation-as an account of the speech to song illusion. In Experiment 1, words varying in the phonological clustering coefficient influenced how quickly a lexical node could recover from satiation, thereby influencing the song-like ratings to lists of words that were high versus low in phonological clustering coefficient. In Experiment 2, we used equivalence testing (i.e., the TOST procedure) to demonstrate that once lexical nodes are satiated the higher level semantic information associated with the word cannot differentially influence song-like ratings to lists of words varying in emotional arousal. The results of these two experiments further support the NST account of the speech to song illusion.

Simulation Studies as a Tool to Understand Bayes Factors

When social scientists wish to learn about an empirical phenomenon, they perform an experiment. When they wish to learn about a complex numerical phenomenon, they can perform a simulation study. The goal of this Tutorial is twofold. First, it introduces how to set up a simulation study using the relatively simple example of simulating from the prior. Second, it demonstrates how simulation can be used to learn about the Jeffreys-Zellner-Siow (JZS) Bayes factor, a currently popular implementation of the Bayes factor employed in the BayesFactor R package and freeware program JASP. Many technical expositions on Bayes factors exist, but these may be somewhat inaccessible to researchers who are not specialized in statistics. In a step-by-step approach, this Tutorial shows how a simple simulation script can be used to approximate the calculation of the Bayes factor. We explain how a researcher can write such a sampler to approximate Bayes factors in a few lines of code, what the logic is behind the Savage-Dickey method used to visualize Bayes factors, and what the practical differences are for different choices of the prior distribution used to calculate Bayes factors.

How peer influence shapes value computation in moral decision-making

Moral behavior is susceptible to peer influence. How does information from peers influence moral preferences? We used drift-diffusion modeling to show that peer influence changes the value of moral behavior by prioritizing the choice attributes that align with peers' goals. Study 1 (N = 100; preregistered) showed that participants accurately inferred the goals of prosocial and antisocial peers when observing their moral decisions. In Study 2 (N = 68), participants made moral decisions before and after observing the decisions of a prosocial or antisocial peer. Peer observation caused participants' own preferences to resemble those of their peers. This peer influence effect on value computation manifested as an increased weight on choice attributes promoting the peers' goals that occurred independently from peer influence on initial choice bias. Participants' self-reported awareness of influence tracked more closely with computational measures of prosocial than antisocial influence. Our findings have implications for bolstering and blocking the effects of prosocial and antisocial influence on moral behavior.

The Origins of Religious Disbelief: A Dual Inheritance Approach

Widespread religious disbelief represents a key testing ground for theories of religion. We evaluated the predictions of three prominent theoretical approaches—secularization, cognitive byproduct, and dual inheritance—in a nationally representative (United States, N = 1,417) data set with preregistered analyses and found considerable support for the dual inheritance perspective. Of key predictors of religious disbelief, witnessing fewer credible cultural cues of religious commitment was the most potent, β = .28, followed distantly by reflective cognitive style, β = .13, and less advanced mentalizing, β = .05. Low cultural exposure predicted about 90% higher odds of atheism than did peak cognitive reflection, and cognitive reflection only predicted disbelief among those relatively low in cultural exposure to religion. This highlights the utility of considering both evolved intuitions and transmitted culture and emphasizes the dual roles of content- and context-biased social learning in the cultural transmission of disbelief (preprint https://psyarxiv.com/e29rt/ ).

Making ERP research more transparent: Guidelines for preregistration

A combination of confirmation bias, hindsight bias, and pressure to publish may prompt the (unconscious) exploration of various methodological options and reporting only the ones that lead to a (statistically) significant outcome. This undisclosed analytic flexibility is particularly relevant in EEG research, where a myriad of preprocessing and analysis pipelines can be used to extract information from complex multidimensional data. One solution to limit confirmation and hindsight bias by disclosing analytic choices is preregistration: researchers write a time-stamped, publicly accessible research plan with hypotheses, data collection plan, and the intended preprocessing and statistical analyses before the start of a research project. In this manuscript, we present an overview of the problems associated with undisclosed analytic flexibility, discuss why and how EEG researchers would benefit from adopting preregistration, provide guidelines and examples on how to preregister data preprocessing and analysis steps in typical ERP studies, and conclude by discussing possibilities and limitations of this open science practice.

Evaluating the strength of evidence for statistically significant rehabilitation treatment effects

BACKGROUND

Relying solely on null hypothesis significance testing to investigate rehabilitation interventions may result in researchers erroneously concluding the presence of a treatment effect.

OBJECTIVE

We sought to quantify the strength of evidence in favour of rehabilitation treatment effects by calculating Bayes factors (BF10s) for significant findings. Additionally, we sought to examine associations between BF10s, p-values, and Cohen's d effect sizes.

METHODS

We searched the Cochrane Database of Systematic Reviews for meta-analyses with "rehabilitation" as a keyword that evaluated a rehabilitation intervention. We extracted means, standard deviations, and sample sizes for treatment and comparison groups from individual findings within 175 meta-analyses. Investigators independently classified the interventions according to the Rehabilitation Treatment Specification System. We calculated t-statistics, p-values, effect sizes, and BF10s for each finding. We isolated statistically significant findings (p ≤ 0.05); applied evidential categories to BF10s, p-values, and effect sizes; and examined relationships descriptively.

RESULTS

We analysed 1,935 rehabilitation findings. Across intervention types, 25% of significant findings offered only anecdotal evidence in favour of a treatment effect; only 48% indicated strong evidence. This pattern persisted within intervention types and when conducting robustness analyses. Smaller p-values and larger effect sizes were associated with stronger evidence in favour of a treatment effect. However, a notable portion of findings with p-value 0.01 to 0.05 (63%) or a large effect size (18%) offered anecdotal evidence in favour of an effect.

CONCLUSIONS

For a substantial portion of statistically significant rehabilitation findings, the data neither support nor refute the presence of a treatment effect. This was the case among a notable portion of large treatment effects and for most findings with p-value > 0.01. Rehabilitation evidence would be improved by researchers adopting more conservative levels of significance, complementing the use of null hypothesis significance testing with Bayesian techniques and reporting effect sizes.

Modeling for Change of Daily Nurse Calls After Surgery in an Orthopedics Ward Using Bayesian Statistics

Nurse call data may be used to evaluate the quality of nursing. However, traditional frequency-based statistics may not easily apply to nurse calls due to the large individual variability and daily call changes. We intended to propose a probabilistic modeling of nurse calls based on Bayesian statistics. We constructed the model including nurse call daily changes, individual variability, and adjustment according to characteristics (age and sex). Nurse call differences after surgery were analyzed based on data from the orthopedic ward from April 2014 to October 2017. Results show that there were differences in nurse calls from day 1 to day 10 after surgery between patients who had undergone orthopedic surgery and those who had undergone other surgeries such as tumor surgery. Furthermore, there were differences in nurse calls from day 1 to day 8 after surgery between patients who used extra pain relief medicine and those who did not. Although the analysis required multiple comparisons regarding daily nurse call changes and fixed data samples per day, our approach using Bayesian statistics could detect the periods and significant differences. This indicates that our nurse call modeling based on Bayesian statistics may be used to analyze nurse call changes.

No credible evidence for links between 2D:4D and COVID-19 outcomes: A probabilistic perspective on digit ratio, ACE variants, and national case fatalities

Research into COVID-19 susceptibility and outcomes are critical, but claims must be carefully evaluated to inform policy decisions. In a recent series of articles, Manning and Fink [1-3] use national-level data to describe associations between case-fatality ratios and male and female finger ratios (2D:4D), a suggested measure of prenatal androgen exposure, as well as angiotensin-converting enzyme (ACE) allele and genotype frequencies. The authors suggest that 2D:4D is linked with ACE variant prevalence, and that higher male 2D:4D is associated with higher case fatality ratios, and point to 2D:4D as a useful prognostic measure for COVID-19 susceptibility. A critical review and robust Bayesian analysis of the hypothesis is described here, finding no conclusive evidence of COVID-19 mortality and 2D:4D, nor associations between 2D:4D and ACE1 allele or ACE2 genotype frequency. This absence of evidence is present for data taken from the second wave of COVID-19 in October 2020. Problematic theoretical grounding, individual-level conclusions drawn from national-level data, and issues with statistical inference in the original articles are discussed. Taken together, the current data offer no clear utility of 2D:4D in determining COVID-19 outcomes.

COVID-19, Coronavirus, Wuhan Virus, or China Virus? Understanding How to "Do No Harm" When Naming an Infectious Disease

When labeling an infectious disease, officially sanctioned scientific names, e.g., “H1N1 virus,” are recommended over place-specific names, e.g., “Spanish flu.” This is due to concerns from policymakers and the WHO that the latter might lead to unintended stigmatization. However, with little empirical support for such negative consequences, authorities might be focusing on limited resources on an overstated issue. This paper empirically investigates the impact of naming against the current backdrop of the 2019–2020 pandemic. The first hypothesis posited that using place-specific names associated with China (e.g., Wuhan Virus or China Virus) leads to greater levels of sinophobia, the negative stigmatization of Chinese individuals. The second hypothesis posited that using a scientific name (e.g., Coronavirus or COVID-19) leads to increased anxiety, risk aversion, beliefs about contagiousness of the virus, and beliefs about mortality rate. Results from two preregistered studies [N_{(Study 1)} = 504; N_{(Study 2)} = 412], conducted across three countries with the first study during the early outbreak (April 2020) and the second study at a later stage of the pandemic (August 2020), found no evidence of any adverse effects of naming on sinophobia and strong support for the null hypothesis using Bayesian analyses. Moreover, analyses found no impact of naming on anxiety, risk aversion, beliefs about contagiousness of the virus, or beliefs about mortality rate, with mild to strong support for the null hypothesis across outcomes. Exploratory analyses also found no evidence for the effect of naming being moderated by political affiliation. In conclusion, results provide no evidence that virus naming impacted individual’s attitudes toward Chinese individuals or perceptions of the virus, with the majority of analyses finding strong support for the null hypothesis. Therefore, based on the current evidence, it appears that the importance given to naming infectious diseases might be inflated.

Bayesian power equivalence in latent growth curve models

Longitudinal studies are the gold standard for research on time-dependent phenomena in the social sciences. However, they often entail high costs due to multiple measurement occasions and a long overall study duration. It is therefore useful to optimize these design factors while maintaining a high informativeness of the design. Von Oertzen and Brandmaier (2013,Psychology and Aging, 28, 414) applied power equivalence to show that Latent Growth Curve Models (LGCMs) with different design factors can have the same power for likelihood-ratio tests on the latent structure. In this paper, we show that the notion of power equivalence can be extended to Bayesian hypothesis tests of the latent structure constants. Specifically, we show that the results of a Bayes factor design analysis (BFDA; Schönbrodt & Wagenmakers (2018,Psychonomic Bulletin and Review, 25, 128) of two power equivalent LGCMs are equivalent. This will be useful for researchers who aim to plan for compelling evidence instead of frequentist power and provides a contribution towards more efficient procedures for BFDA.

A Bayesian analysis of non-significant rehabilitation findings: Evaluating the evidence in favour of truly absent treatment effects

BACKGROUND

Relying solely on null hypothesis significance testing (NHST) to investigate rehabilitation interventions may result in researchers erroneously concluding the absence of a treatment effect.

OBJECTIVE

We aimed to distinguish between truly null treatment effects and data that are insensitive to detecting treatment effects by calculating Bayes factors (BF₀₁s) for non-significant findings in the rehabilitation literature. Additionally, to examine associations between BF₀₁, sample size, and observed P-values.

METHOD

We searched the Cochrane Database of Systematic Reviews for meta-analyses with "rehabilitation" as a keyword that clearly evaluated a rehabilitation intervention. We extracted means, standard deviations, and sample sizes for treatment and comparison groups for individual findings within 175 meta-analyses. Two independent investigators classified the interventions into 4 categories using the Rehabilitation Treatment Specification System. We calculated t-statistics and associated P-values for each finding in order to extract non-significant results (P>0.05). We calculated BF₀₁s for 5790 non-significant results and classified BF₀₁s based on the strength of evidence in favour of the null hypothesis (i.e., anecdotal, moderate, and strong) across and within intervention types. We examined correlations between BF₀₁, sample size, and P-values across and within intervention types.

RESULTS

Across all intervention types, most (71.9%) findings were deemed anecdotal, and this pattern remained within distinct intervention types (58.4-76.0%). Larger sample sizes tended to be associated with greater strength in favour of the null hypothesis, both across and within intervention types. Larger P-values were not associated with greater strength in favour of the null hypothesis; this finding was present both across and within intervention types.

CONCLUSION

Our findings indicate that most non-significant rehabilitation findings are unable to distinguish between the true absence of a treatment effect and data that are merely insensitive to detecting a treatment effect. Findings also suggest that rehabilitation researchers may improve the strength of their statistical conclusions by increasing sample size and that Bayes factors may offer unique benefits relative to P-values.

A Bayesian Analysis of Evidence in Support of the Null Hypothesis in Gerontological Psychology (or Lack Thereof)

OBJECTIVES

Nonsignificant p values derived from null hypothesis significance testing do not distinguish between true null effects or cases where the data are insensitive in distinguishing the hypotheses. This study aimed to investigate the prevalence of Bayesian analyses in gerontological psychology, a statistical technique that can distinguish between conclusive and inconclusive nonsignificant results, by using Bayes factors (BFs) to reanalyze nonsignificant results from published gerontological research.

METHODS

Nonsignificant results mentioned in abstracts of articles published in 2017 volumes of 10 top gerontological psychology journals were extracted (N = 409) and categorized based on whether Bayesian analyses were conducted. BFs were calculated from nonsignificant t-tests within this sample to determine how frequently the null hypothesis was strongly supported.

RESULTS

Nonsignificant results were directly tested with BFs in 1.22% of studies. Bayesian reanalyses of 195 nonsignificant t-tests found that only 7.69% of the findings provided strong evidence in support of the null hypothesis.

CONCLUSIONS

Bayesian analyses are rarely used in gerontological research, and a large proportion of null findings were deemed inconclusive when reanalyzed with BFs. Researchers are encouraged to use BFs to test the validity of nonsignificant results and ensure that sufficient sample sizes are used so that the meaningfulness of null findings can be evaluated.

Exploring the Effects of an Acute Dose of Antipsychotic Medication on Motivation-mediated BOLD Activity Using fMRI and a Perceptual Decision-making Task

The left inferior frontal gyrus and the bilateral ventral striatum are thought to be involved in motivation-mediated decision-making. Antipsychotics may influence this relationship, and atypical antipsychotics improve secondary negative symptoms in schizophrenia, such as loss of motivation, although the acute effects of pharmacological medication on motivation are not fully understood. In this single-blinded, randomized controlled trial, 49 healthy volunteers were randomized into three groups to receive a single dose of haloperidol, aripiprazole or placebo. Between 4.0 and 5.6 h later, participant's brain blood-oxygen-level dependent (BOLD) activity was recorded using functional magnetic resonance imaging (fMRI) while completing a perceptual decision-making fMRI task consisting of one neutral and one motivated condition. Response bias, reflecting the participant's willingness to say that the target stimulus is present, was calculated using signal detection theory. Concurrent with widespread changes in BOLD signal in the motivated vs. neutral condition, a less conservative, mathematically optimal response bias was observed in the motivated condition across the whole sample. Within-group differences in BOLD signal in the left inferior frontal gyrus and bilateral ventral striatum were observed between conditions in the aripiprazole and haloperidol groups, but not in the placebo group. No robust between-group differences in brain activity in the left inferior frontal gyrus or the bilateral ventral striatum were found. Overall, we found no robust evidence for an effect of either aripiprazole or haloperidol on motivationally mediated behavior. An interesting pattern of correlations possibly related to pharmacologically induced alterations in the dopamine system was observed, although findings remain inconclusive and must be replicated in larger samples.