Jan Wacker’s research while affiliated with Hamburg University and other places

Frontal alpha asymmetry has been proposed as a ubiquitous marker of state and trait approach motivation, but recent meta-analyses found weak or nonexistent links with personality traits. It has been suggested that frontal asymmetry may show stronger individual differences in situations that elicit approach motivation (state–trait interaction). To investigate this with sufficient statistical power, we utilized data from the CoScience project (N = 740). Frontal asymmetry was measured during a resting period, a picture viewing task, and a guessing task, which were expected to trigger different levels of approach motivation. Results showed that frontal asymmetry was not reliably affected by task manipulations and did not relate to self-reported traits. Furthermore, Bayesian statistics and a cooperative forking path analysis were used to supplement the preregistered analyses. To conclude, this comprehensive analysis could not support the validity of frontal asymmetry as a marker of approach motivation, neither as a reliable state nor as a trait marker.

Personality neuroscience seeks to uncover the neurobiological underpinnings of personality. Identifying links between measures of brain activity and personality traits is important in this respect. Using an entirely inductive approach, Jach et al. (2020) attempted to predict personality trait scores from resting-state spectral electroencephalography (EEG) using multivariate pattern analysis (MVPA) and found meaningful results for Agreeableness. The exploratory nature of this work and concerns about replicability in general require a rigorous replication, which was the aim of the current study. We applied the same analytic approach to a large data set (N = 772) to evaluate the robustness of the previous results. Similar to Jach et al. (2020), 8 min of resting-state EEG before and after unrelated tasks with both eyes open and closed were analyzed using support vector regressions (SVR). A 10-fold cross-validation was used to evaluate the prediction accuracy between the spectral power of 59 EEG electrodes within 30 frequency bins ranging from 1 to 30 Hz and Big Five personality trait scores. We were not able to replicate the findings for Agreeableness. We extended the analysis by parameterizing the total EEG signal into its periodic and aperiodic signal components. However, neither component was meaningfully associated with the Big Five personality traits. Our results do not support the initial results and indicate that personality traits may at least not be substantially predictable from resting-state spectral power. Future identification of robust and replicable brain-personality associations will likely require alternative analysis methods and rigorous preregistration of all analysis steps.

Personality neuroscience seeks to uncover the neurobiological underpinnings of personality. Identifying links between measures of brain activity and personality traits is important in this respect. Using an entirely inductive approach, Jach et al. (2020) attempted to predict personality trait scores from resting-state spectral electroencephalography (EEG) using multivariate pattern analysis (MVPA) and found meaningful results for Agreeableness. The exploratory nature of this work and concerns about replicability in general require a rigorous replication, which was the aim of the current study. We applied the same analytic approach to a large data set (N = 772) to evaluate the robustness of the previous results. Similar to Jach et al. (2020), 8 minutes of resting-state EEG before and after unrelated tasks with both eyes open and closed were analyzed using support vector regressions (SVR). A 10-fold cross-validation was used to evaluate the prediction accuracy between the spectral power of 59 EEG electrodes within 30 frequency bins ranging from 1 to 30 Hz and Big Five personality trait scores. We were not able to replicate the findings for Agreeableness. We extended the analysis by parameterizing the total EEG signal into its periodic and aperiodic signal components. However, neither component was meaningfully associated with the Big Five personality traits. Our results do not support the initial results and indicate that personality traits may at least not be substantially predictable from resting-state spectral power. Future identification of robust and replicable brain-personality associations will likely require alternative analysis methods and rigorous preregistration of all analysis steps.

Initial studies suggest that extraversion and executive functions (EFs) are associated because of shared dopaminergic mechanisms. Aiming to conceptually replicate these findings we conducted a preregistered study to investigate (1) associations between extraversion and performance in three tasks (3-back, switching, AX-CPT) and (2) whether these associations are sensitive to administration of the dopamine D2 receptor blocker sulpiride in a placebo-controlled between-subjects design (N = 200). Against expectations, neither (agentic) extraversion, nor its interaction with substance condition explained performance in any of the EF tasks. As the current results are limited by an unexpectedly low reliability of the measures derived from the switching task and the AX-CPT, further preregistered studies using psychometrically superior measures are needed.

The issues at the centre of the replicability crisis, such as low statistical power and undisclosed flexibility in data analysis, are amplified in research aiming to link individual differences in EEG markers to variations in personality due to between-subjects research designs and high complexity of data processing. The CoScience Team, a collaboration of ten EEG-personality laboratories, employs the principles of cooperative forking paths analysis for the first time, aiming to address this unsatisfactory state of affairs by (1) significantly increasing sample size (and statistical power) through sharing the load of data collection across laboratories, and by (2) eliminating undisclosed flexibility in data analysis. The latter is achieved through collaborative identification of both the most appropriate and all defensible pre-processing and analysis paths, and documentation of the resulting multiverse of millions of alternative analyses and results. Being at the data analysis stage of the CoScience project, the presentation will, after a conceptual overview of this novel approach, focus on the challenges encountered as well as solutions under development to overcome them. The discussion will compare our approach with other ongoing initiatives and recommendations with similar goals and provide practical guidance for researchers in the field interested in taking steps aimed at increasing the replicability of their findings.

Despite a plethora of research, associations between individual differences in personality and electroencephalogram (EEG) parameters remain poorly understood due to concerns of low replicability and insufficiently powered data analyses due to relatively small effect sizes. The present article describes how a multi-laboratory team of EEG-personality researchers aims to alleviate this unsatisfactory status quo. In particular, the present article outlines the design and methodology of the project, provides a detailed overview of the resulting large-scale dataset that is available for use by future collaborators, and forms the basis for consistency and depth to the methodology of all resulting empirical articles. Through this article, we aim to inform researchers in the field of Personality Neuroscience of the freely available dataset. Furthermore, we assume that researchers will generally benefit from this detailed example of the implementation of cooperative forking paths analysis.

This paper represents the outcome of a multidisciplinary discussion on what works, what does not, and what can be improved, in ongoing work on biobehavioral taxonomies and their biomarkers. The authors of this paper, representing a wide spectrum of biobehavioral disciplines (clinical, developmental, differential psychology, neurophysiology, endocrinology, psychiatry, neurochemistry, and neurosciences), have contributed more extensive opinions to the Theme Issue 'Neurobiology of temperament, personality and psychopathology: what's next?'. The authors identified 10 directions in international and multidisciplinary cooperation, and multiple insights for ‘what is next’ for each of these directions.

Based on a brief review of frequently investigated associations between measures of brain activity and personality, we argue that such links remain elusive. The following features of previous research in this field may account for the scarcity of replicable findings: (1) A focus on broad heterogeneous traits, (2) low reliability of some brain activity measures, (3) lack of well-founded theories, (4) small sample sizes, and (5) undisclosed flexibility in complex preprocessing/analysis routines for brain activity data. Furthermore, we argue that whereas (1) and (2) can be addressed within a typical single-lab study, resolving the remaining (and probably more serious) issues will benefit considerably from close cooperation among researchers with similar interests during all stages of the research cycle. We conclude by describing the ‘CoScience’ approach to cooperative research. Our initial application thereof will hopefully prove instrumental in moving the field forward.