Bayesian inference for psychology. Part II: Example applications with JASP

Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely. An important impediment to the widespread adoption of Bayesian tests is arguably the lack of user-friendly software for the run-of-the-mill statistical problems that confront psychologists for the analysis of almost every experiment: the t-test, ANOVA, correlation, regression, and contingency tables. In Part II of this series we introduce JASP (http://www.jasp-stats.org), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems. JASP is based in part on the Bayesian analyses implemented in Morey and Rouder’s BayesFactor package for R. Armed with JASP, the practical advantages of Bayesian hypothesis testing are only a mouse click away.     

Working with Research Integrity—Guidance for Research Performing Organisations: The Bonn PRINTEGER Statement

This document presents the Bonn PRINTEGER Consensus Statement: Working with Research Integrity—Guidance for research performing organisations. The aim of the statement is to complement existing instruments by focusing specifically on institutional responsibilities for strengthening integrity. It takes into account the daily challenges and organisational contexts of most researchers. The statement intends to make research integrity challenges recognisable from the work-floor perspective, providing concrete advice on organisational measures to strengthen integrity. The statement, which was concluded February 7th 2018, provides guidance on the following key issues:

1. § 1.
   Providing information about research integrity
    
2. § 2.
   Providing education, training and mentoring
    
3. § 3.
   Strengthening a research integrity culture
    
4. § 4.
   Facilitating open dialogue
    
5. § 5.
   Wise incentive management
    
6. § 6.
   Implementing quality assurance procedures
    
7. § 7.
   Improving the work environment and work satisfaction
    
8. § 8.
   Increasing transparency of misconduct cases
    
9. § 9.
   Opening up research
    
10. § 10.
    Implementing safe and effective whistle-blowing channels
     
11. § 11.
    Protecting the alleged perpetrators
     
12. § 12.
    Establishing a research integrity committee and appointing an ombudsperson
     
13. § 13.
    Making explicit the applicable standards for research integrity
     

     

Countering illegibility: Religion,ethnicity and sexuality in public debates and lived experience in Belgium

In this article, I set out to explore various intersectional social constructions of ethnicity, religion and sexuality. First, I conduct an analysis of recent public controversies in Flanders (Belgium) about women’s and (homo)sexual equality as set against religious authorities and religious-ethnic minorities. It reveals how dominant understandings of ethnicity, sexuality and religion are constructed, reinforced and, if needed, defended. Second, I foreground a critical counter-voice negotiating these what I call ‘ethno-sexular’ boundary constructions. I analyse the lived experiences of Hajar, a volunteer of an antiracist LGBTQI organisation located in Brussels, and argue that because of a dominant ethno-sexular discourse, Hajar’s hybrid identifications and critical voice is made illegible in much of her social environment.     

Tuning down the hedonic brain: Cognitive load reduces neural responses to high-calorie food pictures in the nucleus accumbens

The present research examined whether cognitive load modulates the neural processing of appetitive, high-calorie food stimuli. In a functional magnetic resonance imaging (fMRI) study, participants quickly categorized high-calorie and low-calorie food pictures versus object pictures as edible or inedible while they concurrently performed a digit-span task that varied between low and high cognitive load (memorizing six digits vs. one digit). In line with predictions, the digit-span task engaged the dorsolateral prefrontal cortex (DLPFC) when cognitive load was high compared to low. Moreover, exposure to high-calorie compared to low-calorie food pictures led to increased activation in the nucleus accumbens (NAcc), but only when cognitive load was low and not when it was high. In addition, connectivity analyses showed that load altered the functional coupling between NAcc and right DLPFC during presentation of the high-calorie versus low-calorie food pictures. Together, these findings indicate that loading the cognitive system moderates hedonic brain responses to high-calorie food pictures via interactions between NAcc and DLPFC. Our findings are consistent with the putative cognitive nature of food motivation. Implications for future research are discussed.     

Retrieving relevant factors with exploratory SEM and principal-covariate regression: A comparison

Behavioral researchers often linearly regress a criterion on multiple predictors, aiming to gain insight into the relations between the criterion and predictors. Obtaining this insight from the ordinary least squares (OLS) regression solution may be troublesome, because OLS regression weights show only the effect of a predictor on top of the effects of other predictors. Moreover, when the number of predictors grows larger, it becomes likely that the predictors will be highly collinear, which makes the regression weights’ estimates unstable (i.e., the “bouncing beta” problem). Among other procedures, dimension-reduction-based methods have been proposed for dealing with these problems. These methods yield insight into the data by reducing the predictors to a smaller number of summarizing variables and regressing the criterion on these summarizing variables. Two promising methods are principal-covariate regression (PCovR) and exploratory structural equation modeling (ESEM). Both simultaneously optimize reduction and prediction, but they are based on different frameworks. The resulting solutions have not yet been compared; it is thus unclear what the strengths and weaknesses are of both methods. In this article, we focus on the extents to which PCovR and ESEM are able to extract the factors that truly underlie the predictor scores and can predict a single criterion. The results of two simulation studies showed that for a typical behavioral dataset, ESEM (using the BIC for model selection) in this regard is successful more often than PCovR. Yet, in 93% of the datasets PCovR performed equally well, and in the case of 48 predictors, 100 observations, and large differences in the strengths of the factors, PCovR even outperformed ESEM.     

Stress, glucocorticoids and liquorice in human pregnancy: programmers of the offspring brain

A suboptimal prenatal environment may induce permanent changes in cells, organs and physiology that alter social, emotional and cognitive functioning, and increase the risk of cardiometabolic and mental disorders in subsequent life ("developmental programming"). Although animal studies have provided a wealth of data on programming and its mechanisms, including on the role of stress and its glucocorticoid mediators, empirical evidence of these mechanisms in humans is still scanty. We review the existing human evidence on the effects of prenatal maternal stress, anxiety and depression, glucocorticoids and intake of liquorice (which inhibits the placental barrier to maternal glucocorticoids) on offspring developmental outcomes including, for instance, alterations in psychophysiological and neurocognitive functioning and mental health. This work lays the foundations for biomarker discovery and affords opportunities for prevention and interventions to ameliorate adverse outcomes in humans.     

Immediate and long-term priming effects are independent of prime awareness

Subliminal primes are assumed to produce weaker and short-lived effects on subsequent behavior compared to clearly visible primes. However, this difference in priming effect may be due to differences in signal strength, rather than level of awareness. In the present study we manipulated prime discriminability by using metacontrast masks and pseudomasks, while keeping the prime strength equal. This manipulation resulted in large differences in discriminability of the primes. However, both immediate response priming and long-term response priming (measured with conflict adaptation) was equal for the poorly discriminable and well discriminable primes, and equal for groups that differed markedly in terms of how well they could discriminate the primes. Our findings imply that discriminability of information is independent of both the immediate and long-term effects that information can have on behavior.     

Prime and probability: causal knowledge affects inferential and predictive effects on self-agency experiences

Experiences of having caused a certain outcome may arise from motor predictions based on action–outcome probabilities and causal inferences based on pre-activated outcome representations. However, when and how both indicators combine to affect such self-agency experiences is still unclear. Based on previous research on prediction and inference effects on self-agency, we propose that their (combined) contribution crucially depends on whether people have knowledge about the causal relation between actions and outcomes that is relevant to subsequent self-agency experiences. Therefore, we manipulated causal knowledge that was either relevant or irrelevant by varying the probability of co-occurrence (50% or 80%) of specific actions and outcomes. Afterwards, we measured self-agency experiences in an action–outcome task where outcomes were primed or not. Results showed that motor prediction only affected self-agency when relevant actions and outcomes were learned to be causally related. Interestingly, however, inference effects also occurred when no relevant causal knowledge was acquired.