By applying and advancing multivariate statistical and psychometric modeling techniques, our research aims at better understanding individual differences in general cognitive functioning and social cognition. We develop and evaluate computerized test batteries rooted in experimental psychology for measuring human abilities and combine psychometric, neurometric (EEG, (f)MRI), molecular-genetic and hormonal assessments to investigate within- and between-person variations in cognition, emotion and personality. A special focus of our research is the processing of invariant and variant facial information – a basic domain of social cognition. We ask how are abilities in the social domain special as compared with cognitive processing in general. To this aim we investigate typically functioning individuals across the life span, including old age and pathological conditions. Beyond these goals, we enjoy contemplating about conceptual issues in psychological measurement.

Ongoing projects

METEOR – Mastering the oppressive number of forking paths unfolded by noisy and complex neural data, DFG Priority Programme "META-REP" (SPP 2317).

Since April 2022

There is a replication crisis and a “real-world or the lab” dilemma in psychology and cognitive neuroscience. Solving the dilemma and overcoming the crisis at the same time is arguably a serious challenge. One of the main aims in cognitive neuroscience is to discover brain-cognition associations which are replicable across laboratories. However, to date we do not know well enough how much hitherto unsuccessful replications are due to the oppressive number of methodological decisions researchers have to make á priori to testing a brain-cognition association. Moreover, we do not yet have standards with respect to the unit of analysis at which replications should be considered successful. We also lack a knowledge framework containing a systematic and exhaustive overview of potential methodological choices that are defensible in a typical individual differences analysis workflow for mobile EEG or fMRI, as well as multivariate behavioural data. Finally, hitherto proposed statistical approaches for analysing the multiverse of potentially constructed datasets for noisy and highly complex multidimensional neural data need extensions through tools available for big data analysis. Such approaches would allow learning about influential decisions and would predict potential heterogeneity of future findings. To take a large step toward filling these gaps, METEOR brings together a group of scientists with different and complementary expertise.

PIs: Prof. Dr. Andrea Hildebrandt, Dr. Carsten Gießing, Prof. Dr. Stefan Debener, Prof. Dr. Christiane Thiel

Involved scientists (PMuS Lab): Dr. Daniel Kristanto, Dr. Nadine Jacobsen

Improving reliability of ERP estimates and their associations with traits in three methodological domains: EEG preprocessing decisions, within-person, and between-person ERP variability

Since January 2023, funded by the Programme Budget of the University’s Presidential Chair

The unacceptably low replicability reported by many recent replication projects, spanning psychology, health sciences, life sciences and other scientific disciplines, has given rise to an emerging field of replication science. Scientists are increasingly engaged in discussions and empirical work aimed at understanding and alleviating potential core problems at the heart of this unsatisfactory revelation. One of the core issues identified is the undisclosed flexibility in data analysis, which leaves the influence of analysis decisions on the outcome unknown and, in extreme cases, provides a potential for exploiting 'researcher degrees of freedom'. A common initiative that aims to address the many alternative yet defensible preprocessing and analysis pipelines in EEG research, and which is gaining popularity, is multiverse analysis, where results from a variety of defensible preprocessing paths are analysed and compared to assess robustness and increase transparency. However, we argue that there are methodological components of ERP quantification that can be improved prior to multiverse computation that would enhance the validity of results, including those obtained from a multiverse of preprocessing pipelines. This project investigates ERP quantification techniques and tests their reliability in capturing valid ERP amplitudes across individuals and trials and reliable associations between these ERPs and personality, compared to traditional approaches. Further, through the application of machine learning methods, the project explores statistically and computationally efficient approaches to analysing the EEG multiverse of ERP and personality associations.

PI: Dr. Cassie Short

Involved scientists (PMuS Lab): Prof. Dr. Andrea Hildebrandt, Dr. Daniel Kristanto

Collaborators: Prof. Dr. Jan Wacker & the CoScience project  

Psychological profiling for hearing-related mHealth applications, DFG Cluster of Excellence “Hearing4All” (EXC 2177)

Since April 2022

Hearing related psychological traits and states are crucial to the design of personalized treatment recommendations and hearing aid self-fitting algorithms in the context of a Virtual Hearing Clinic (VHC). An envisioned VHC app will be an easily accessible mobile hearing clinic for auditory diagnostics and intervention. We apply interactive experience sampling techniques of behavioral and psychophysiological data to monitor hearing performance and its fluctuations during the course of particular daily psychological states. Additionally, we measure individual sound preferences and their daily fluctuations, as well as their relationship with hearing performance and daily psychological states. All these measurements are used for predictive modeling to inform individualized hearing health care.

PI: Prof. Dr. Andrea Hildebrandt 

Involved scientists (PMuS Lab): MSc. Giulia Angonese

Collaborators: Prof. Dr. Birger Kollmeier, Dr. Mareike Buhl, Dr. Lena Schnell-Major

Understanding intraindividual hearing variability in daily life, DFG Cluster of Excellence “Hearing4All” (EXC 2177)

Since July 2019

Ecological Momentary Assessment (EMA) tools have a great potential in terms of self-assessment before visiting a clinician or for monitoring an intervention process in everyday life. EMA’s advantages are real-time tracking of the environment, self-report or behaviors and states while avoiding retrospective bias. Smartphones are especially useful tools for EMA since they are widespread and offer broad technological possibilities to track performance, states and behaviors. In this project we are collecting and modeling hearing related data in daily life to better understand why some days are good and others are rather bad for hearing. Variance heterogeneous random coefficient models and dynamic structural equation are applied to map hearing fluctuations and their psychophysiological determinants.

PI: Prof. Dr. Andrea Hildebrandt

Involved scientists (PMuS Lab): MSc. Inka Kuhlmann

Collaborators: Prof. Dr. Birger Kollmeier, Prof. Dr. Christiane Thiel

Monitoring far-transfer effects of neuromodulation and cognitive training on interference control in daily activities after stroke by means of experience sampling methods, DFG Research Training Group “Neuromodulation of Motor and Cognitive Function in Brain Health and Disease” (RTG 2783)

Since October 2023

Individuals’ real-world cognitive functioning can be considered the ultimate clinical outcome relevant for diagnosis and evaluation of intervention success. However, assessment challenges for such outcomes are immense. Retrospective self-reports are often a poor proxy of the targeted outcome due to a multitude of biases. Ambulatory assessment and experience sampling methodologies are powerful emerging tools to immediately capture behavior in different situations at different times of the day. This project will develop a specific instrument for assessing internal and external cognitive interference during daily life activities as experienced by stroke patients and will evaluate the instrument as an experience sampling method.

PI: Prof. Dr. Andrea Hildebrandt

Neural basis of audiovisual integration in neonates

Since February 2020

The ability to combine auditory and visual stimuli into a unified percept was demonstrated to determine developmental advantages in several cognitive domains, for instance in language, attention, affect discrimination and social cognition. However, we do not know sufficiently much about the early stages of audio-visual integration development and its neural basis. In this project, we aim to elucidate the neural correlates and behavioral markers of the audio-visual integration ability during the first 24 months of life and explore how it relates to preterm birth. By means of MRI-related techniques and functional near-infrared spectroscopy (fNIRS) we study the audio-visual integration brain network from a structural and a functional perspective.

PIs: Prof. Dr. Andrea Hildebrandt, Prof. Dr. Axel Heep

Involved scientists (PMuS Lab): MSc. Juan F. Quinones

Collaborators: Prof. Dr. Christiane Thiel, Dr. Carsten Gießing

Self-regulation development after preterm birth

Since July 2021

Since perinatal care has rapidly improved over the last decades, premature delivery is nowadays accompanied by high survival rates of the infants. The first few weeks of a preterm infant are, nonetheless, spent in highly stressful hospital environments, which in combination with the premature delivery negatively affects neural development in this stage of high cerebral plasticity. Additionally, impaired behavioural, emotional and cognitive self-regulation after preterm birth is often observed in infancy and childhood. Since impairments in self-regulation are related to a variety of negative life outcomes, such as lower mental health, academic success and increased substance abuse, it is important to investigate whether these differences between preterm and full term born individuals persist into adolescence and adulthood. In this project we aim to better understand how is self-regulation associated with microstructural properties of the brain and how does preterm birth influence these associations. We are approaching these questions by means of multivariate statistical modelling approaches in the structural equation modelling framework and diffusion tensor imaging (DTI), a technique to indirectly infer the microstructural organization of the brain.

PI: Prof. Dr. Andrea Hildebrandt, Prof. Dr. Axel Heep

Involved scientists (PMuS Lab): MSc. Merle Marek, MSc. Juan F. Quinones

1/f brain activity and creativity

Since September 2022

Creativity is the ability to produce novel and useful ideas and is central to the progression of human civilization, prosperity, and well-being. For decades, researchers have made great effort to explore the cognitive and neural basis of creative thinking. However, we still do not well understand how non-oscillatory, complex brain activity is associated with creativity. In this project we relate individuals’ complex patterns of the Electroencephalography (EEG) signals’ spectrum with creativity as measured by divergent thinking tasks.

PI: Dr. Jing Teng

Involved scientists: Prof. Dr. Andrea Hildebrandt, Dr. Yadwinder Kaur

EMOTIC – Enfacement manipulation in transmitted inter-facial communication, DFG Priority Programme “The Active Self” (SPP 2134) 

Since July 2019

The relation between selfhood and intersubjectivity is debated. It has been stressed that the sense of self does not only include a differentiation between oneself and others, but it also contains the person’s readiness to be affected by others. The ENFACEMENT effect illustrates this readiness: In case of synchronous stimulation of the own face and the face of a partner, it has been shown that the facial features of the partner have been incorporated into the self-face representation. In this interdisciplinary project, we 1) developed an experimental setting called the Open Virtual Mirror of individualized face avatars that can be manipulated in real-time interactions with an interacting individual (Link) and 2) use this methodology to embed enfacement into the dynamic facial communication process in which it may occur in real life. By doing so, we investigate the plasticity of the self-face illusion, postulating that enfacement occurs if the transmission process of intention and emotion related facial cues is successful.

PI: Prof. Dr. Andrea Hildebrandt, Dr. Stefan Zachow

Involved scientists: MCs. Martin Grewe, MSc. Tuo Liu