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Human Meets Algorithm: AI in Psychology

Welcome - Research Initiative Artificial Intelligence at the Institutes of Psychology

Technological advancements in the field of artificial intelligence (AI) are opening up a fascinating and groundbreaking research domain for scientific psychology. AI offers new possibilities to understand and analyze human behavior, emotions, and cognitive processes. It enables us to process vast amounts of data, decode data structures, and thus expand the boundaries of traditional psychological approaches. Psychological research applying AI promises even deeper insights into the mechanisms of human experience and behavior.

The application of AI in psychology, for instance, opens up new avenues for diagnosing mental disorders and developing personalized therapeutic approaches. Furthermore, the combination of AI with technologies such as virtual reality and wearables allows for the creation of interactive environments that facilitate the exploration of behaviors in realistic scenarios. This presents new opportunities for the development of intervention strategies. Companies and HR departments can also benefit from AI, for example, in the preselection of applicants. However, the ongoing development of AI also brings ethical and societal challenges, which the Institutes of Psychology addresses. Questions of privacy, accountability, and the potential for bias in the data must be carefully considered.

Artificial intelligence is therefore a research focus at the University of Münster and the Institutes of Psychology. In numerous research projects, third-party funded projects, and publications, researchers and psychologists at our institutes investigate the role of AI in psychology and its disciplines. This website provides an overview of these activities.

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  • People and Research Groups

    AI & Cognitive Neuroscience
    We use machine learning and artificial intelligence as a method for data analysis (e.g., for analyzing EEG data via Multivariate Pattern Analysis or Support Vector Machines) and as tools for predicting psychological processes (such as predicting action intentions from neural and behavioral data). We study human memory and perception processes and how our brain uses generative models to understand and respond to the environment. With the help of AI, we are working on predictive models that, in the best case, are able to explain and further understand neuronal activity in the human brain.

    You can find more info here:

    AI & Clinical Psychology
    In our research, we use virtual reality worlds as an effective tool to study behavior change mechanisms and the effectiveness of psychotherapy. By simulating environments close to reality, we can capture participants' responses and behaviors in controlled experiments and gain valuable insights. These innovative approaches allow us to explore new avenues for psychotherapy practice and evaluate the effectiveness of interventions in a safe and controlled environment.

    You can find more info here:

    AI & Personality and Assessment
    Machine learning and artificial intelligence are used in many ways in our research and teaching. In addition to other applications, we use AI, for example, to predict selection decisions. We also systematically benchmark the performance of AI prediction models. In doing so, we compare them with simple prediction models as well as human raters. In our research, we use a systematic combination of AI approaches for data generation and data integration and use AI, for example, to predict differences in the level and trajectories of well-being and performance in different application contexts.

    You can find more info here:

    AI & Statistics and Psychological Methods
    In our research, we combine classical psychological statistical approaches with machine learning approaches and investigate the statistical properties of these combinations (e.g., lasso and ridge regularization and structural equation models). We also extend machine learning models so that they can be used adequately in data situations that often arise in psychology (e.g., gradient tree bossting and hierarchical data).

    You can find more info here:

    AI & Organizational and Business Psychology
    In organizational and business psychology, we use self-generated AI systems for exploratory research and complement or comparison with theoretical models in the areas of (a) assessing the aesthetics of websites, (b) performance effects of teamwork, and (c) personnel selection, among others. In addition, we address human-machine interaction and investigate human trust in AI systems to better understand how people perceive, evaluate, and build trust in interactions with these systems.

    You can find more info here:

    KI in School &  University
    How do learners and teachers deal with AI technology? What expectations do they have of its competence and trustworthiness? What attributions/anthropomorphizations do they make? These and related questions are explored in our research. In addition, AI technologies are used to analyze language and usage behavior of learners.

    You can find more info here:

  • Publikationen / Publications

    KI & Allgemeine und biologische Psychologie / AI & Cognitive Neuroscience

    • Balestrieri, E., & Busch, N. A. (2022). Spontaneous alpha-band oscillations bias subjective contrast perception. Journal of Neuroscience, 42(25), 5058-5069.
    • Bremer, G., Stein, N., & Lappe, M. (2022). Do They Look Where They Go? Gaze Classification During Walking. In NeuRIPS 2022 Workshop on Gaze Meets ML.
    • Bremer, G., Stein, N., & Lappe, M. (2022). Machine Learning Prediction of Locomotion Intention from Walking and Gaze Data. International Journal of Semantic Computing, 1-24.
    • Rolff T., Stein N., Lappe M., Steinicke F., Frintrop S. (2022). Metrics for Time-to-Event Prediction of Gaze Events. Proceedings for Machine Learning Research (PMLR).
    • Stein, N., Bremer, G., Lappe, M. (2022). Eye Tracking-based LSTM for Locomotion Prediction in VR. Proceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces (VR) (pp. 493-503). IEEE.
    • Bremer, G., Stein, N., Lappe, M. (2021) Predicting Future Position From Natural Walking and Eye Movements with Machine Learning. Proceedings of the IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR) 2021, 19-28 (Best paper award).
    • Pomp, J., Heins, N., Trempler, I., Kulvicius, T., Tamosiunaite, M., Mecklenbrauck, F., Wurm, M. F., Wörgötter, F., Schubotz, R. I. (2021) Touching events predict human action segmentation in brain and behavior. NeuroImage, 243, 118534.
    • Crouzet, S. M., Busch, N. A., & Ohla, K. (2015). Taste quality decoding parallels taste sensations. Current Biology, 25(7), 890-896.

    KI & Klinische Psychologie / AI & Clinical Psychology

    • Beer, U. M., Neerincx, M.A., Morina, N., & Brinkman, W. P. (2020). Computer-based perspective broadening support for appraisal training: Acceptance and Effects. International Journal of Technology and Human Interaction, 16, 3, 86-108.

    • Beer, U. M., Neerincx, M.A., & Morina, N., Brinkman, W. P. (2017). Virtual agent-mediated appraisal training: A single case series among Dutch firefighters. European Journal of Psychotraumatology, 8(1):1378053.

    • Brinkman, W.P., Hartanto, D., Kang, N., de Vliegher, D., Neerincx, M.A., Kampmann, I.L., Morina, N., & Emmelkamp, P.M.G. (2012). A virtual reality dialogue system for the treatment of social phobia. Proceedings of the 30th international conference on Human factors in computing systems (CHI'12). Austin, TX, USA: ACM.

    • Emmelkamp, P. M. G., Meyerbröker, K., & Morina, N. (2020). Virtual reality therapy in social anxiety disorder. Current Psychiatry Reports, 22, 32.

    • Favie, J., Vakili, V. G., Brinkman, W.-P., Morina, N., & Neericx, M. A. (2016). State of the art in technology supported resilience training for military professionals. In A. Gaggioli, A. Ferscha, G. Riva, S. Dunne, & I. Viaud-Delmon, I. (eds.). Human Computer Confluence: Transforming human experience through symbiotic technologies. Warsaw: De Gruyter Open.

    • Hartanto, D., Brinkman, W. P., Kampmann, I. L., Morina, N., Emmelkamp, P.  M. G., & Neerincx, M.A. (2016). Home-based virtual reality exposure therapy with virtual health agent support. Communications in Computer and Information Science, 85-98.

    • Hartanto, D., Brinkman, W. P., Kampmann, I. L., Morina, N., Emmelkamp, P.  M. G., & Neerincx, M.A. (2015). Design and Implementation of Home-Based Virtual Reality Exposure Therapy System with a Virtual eCoach. Lecture Notes in Artificial Intelligence, 9238, 287-291.

    • Hartanto, D., Brinkman, W. P., Kampmann, I. L., Morina, N., Emmelkamp, P.  M. G., & Neerincx, M.A. (2014). Controlling Social Stress in Virtual Reality Environments. PLOS ONE, 9(3), e92804.

    • Hartanto, D., Kampmann, I. L., Morina, N., Emmelkamp, P. G., Neerincx, M. A., & Brinkman, W. P. (2019). Correction: Controlling Social Stress in Virtual Reality Environments. PloS One, 14(10), e0223988.

    • Hartanto, D., Kang, N., Brinkman, W. P., Kampmann, I. L., Morina, N., Emmelkamp, P.  M. G., & Neerincx, M.A. (2012). Automatic mechanisms for measuring subjective unit of discomfort. Studies in Health Technology and Informatics, 181, 192-196.

    • Kampmann, I. L., Emmelkamp, P. M. G., Hartanto, D., Brinkman, W.P., Zijlstra, B. J. H., & Morina, N. (2016). Exposure to virtual social interactions in the treatment of social anxiety disorder: A randomized controlled trial. Behaviour Research and Therapy, 77, 147-156.

    • Kampmann, I. L., Emmelkamp, P. M. G., & Morina, N. (2019). Cognitive predictors of treatment outcome for exposure therapy: Do changes in self-efficacy, self-focused attention, and estimated social costs predict symptom improvement in social anxiety disorder? BMC Psychiatry. 19:80.

    • Kampmann, I. L., Emmelkamp, P. M. G., & Morina, N. (2018). Does exposure therapy lead to changes in attention bias and approach-avoidance bias in patients with social anxiety disorder? Cognitive Therapy and Research, 42, 865-866.

    • Kampmann, I. L., Emmelkamp, P. M. G., & Morina, N. (2018). Self-report questionnaires, behavioral assessment tasks, and an implicit behavior measure: Do they predict social anxiety in everyday life? PeerJ, 6:e5441.

    • Kampmann, I. L., Emmelkamp, P. M. G., & Morina, N. (2016). Meta-analysis of technology-assisted interventions for social anxiety disorder. Journal of Anxiety Disorders, 42, 71-84.

    • Kampmann, I. L., Meyer, T., & Morina, N. (2020). Social comparison modulates coping with fear in virtual environments. Journal of Anxiety Disorders, 72, 102226.

    • Lancee, J. van Straten, A., Morina, N., Kaldo, V., & Kamphuis, J.H. (2016). Guided online or face-to-face cognitive behavioral treatment for insomnia: A randomized wait-list controlled trial. Sleep. 39, 183-191.

    • Ling, Y, Brinkman, W. P., Nefs, H. T., Morina, N. & Heynderickx, I. (2014). A meta-analysis on the relationship between self-reported presence and anxiety in virtual reality exposure therapy for anxiety disorders. PLOS ONE. 9(5): e96144.

    • Meyerbröker, K. & Morina, N. (2021). The use of Virtual Reality in assessment and treatment of anxiety and related disorders. Clinical Psychology and Psychotherapy, 28, 466–476.

    • Meyerbröker, K., Morina, N., & Emmelkamp, P.M.G. (2018). Enhancement of exposure therapy in participants with specific phobia: a randomized controlled trial comparing yohimbine, propranolol and placebo. Journal of Anxiety Disorders, 57, 48-56

    • Meyerbröker, K., Morina, N., Kerkhof, G., & Emmelkamp, P.M.G. (2022). Potential predictors of virtual reality exposure therapy for fear of flying: anxiety sensitivity, self-efficacy and the therapeutic alliance. Cognitive Therapy and Research, 46, 646–654.

    • Meyerbroeker, K., Morina, N., Kerkhof, G. A., & Emmelkamp, P.M.G. (2013). Virtual Reality Exposure Therapy does not provide any additional value in agoraphobic patients: A randomized controlled trial. Psychotherapy & Psychosomatics, 82, 170-176.

    • Meyerbröker, K., Morina, N., Kerkhof, G., & Emmelkamp, P. M. G. (2011). Virtual reality exposure treatment of Agoraphobia: A comparison of computer automatic virtual environment and head-mounted display. Studies in Health Technology and Informatics, 167, 51-56.  

    • Morina, N., Brinkman, W.P., Hartanto, D., Kampmann, I. L., & Emmelkamp, P.M.G. (2015). Social interactions in virtual reality exposure therapy: a proof-of-concept pilot study. Technology and Health Care, 23, 581-589

    • Morina, N., Brinkman, W.P., Hartanto, D., & Emmelkamp, P.M.G. (2014). Sense of presence and anxiety during virtual social interactions between a human and virtual humans. PeerJ, 2:e337.

    • Vakili, V., Brinkman, W.-P., Morina, N., Neerincx, M.A. (2014). Characteristics of successful technological interventions in mental resilience training. Journal of Medical Systems, 38, 113.

    • Van Meggelen, M., Morina, N., Van der Heiden, C., Brinkman, W.P., Yocarini, I.E., Tielman, M.L., Rodenburg, J., Van Ee-Blankers, E., Van Schie, K., Broekman, M.E., & Franken, I.H.A. (2022). A randomized controlled trial to pilot the efficacy of a computer-based intervention for the treatment of posttraumatic stress disorder. Frontiers in Digital Health, 4:974668. 

    KI & Persönlichkeitspsychologie und Psychologische Diagnostik / AI & Personality and Assessment

    • Breil, S. M., Lievens, F., Forthmann, B., & Back, M. D. (in press). Interpersonal behavior in assessment center role-play exercises: Investigating structure, consistency, and effectiveness. Personnel Psychology.
    • Cannata, D., Breil, S. M., Lepri, B., Back, M. D., O’Hora, D. (2022). Toward an Integrative Approach to Nonverbal Personality Detection: Connecting Psychological and Artificial Intelligence Research. Technology, Mind, and Behavior, 3(2: Summer 2022).
    • Grunenberg, E. & Berkessel, J. (2022). Big Data in Social and Personality Psychology. Coupled Symposium at at the 52nd congress of the German Psychological Society (Hildesheim, Germany, September 10-15, 2022).
    • Grunenberg, E., Peters, H., Francis M., Back, M. D., Matz, S. C (2021). Leveraging Text Data in Recruiting: Using Machine Learning to Predict Personality from Application Data. Talk at the 16th meeting of the section Differential Psychology and Psychological Assessment of the German Psychological Society (Ulm, Germany, September 12-15, 2021).
    • Grunenberg, E., Peters, H., Francis M., Back, M. D., Matz, S. C. (2021). Machine Learning in Recruiting: Predicting Personality from Short Text Excerpts. Data Blitz at the SPSP‘21 Preconference: Psychology of Media and Technology (Online, February 9th, 2021).
    • Grunenberg, E., Peters, H., Francis M., Back, M. D., Matz, S. C. (2021). Machine Learning in Recruiting: Predicting Personality from Short Text Excerpts. Data Blitz at the East Coast Doctoral Conference 2021 (Online, April 30th, 2021).
    • Grunenberg, E., Stachl, C., Breil, S., Back, M.D. (2022, September 13). Predicting Social Judgments: A computer-based Approach to Cue Extraction and Integration. [Conference presentation]. 52nd Congress of the German Psychological Society, Hildesheim, Germany.
    • Grunenberg, E. & Schödel, R. (2021). Personality Computing. Symposium at the 16th Biennial Conference of the German Psychological Society – Personality Psychology and Psychological Diagnostics (DPPD) Section (Ulm, Germany, September 12-15, 2021).
    • Hätscher, O., Junga, A., Schulze, H., Kockwelp, P., Risse, B., Back, M. D., Marschall, B. (2023, April 13). Integration of VR into Medical Education [Conference presentation]. Würtual Reality XR Meeting, Würzburg, Germany.
    • Klinz, J. L., Grunenberg, E., & Back, M. D. (2022, September 13). It’s not what you say but how you say it – towards a better understanding of assessment center ratings using paralinguistic speech analysis. [Conference presentation]. 52nd Congress of the German Psychological Society, Hildesheim, Germany.
    • Klinz, J. L., Grunenberg, E., & Back, M. D. (2022, July 15). Towards a better understanding of assessment center ratings – a machine learning-based approach to paralinguistic behavior analysis [Conference presentation]. European Conference on Personality 2022, Madrid, Spain.
    • Mõttus, R., Wood, D., Condon, D. M., Back, M. D., Baumert, A., Costantini, G., Epskamp, S., Greiff, S., Johnson, W., Lukaszewski, A., Murray, A., Revelle, W., Wright, A., Yarkoni, T., Ziegler, M., & Zimmermann, J. (2020). Descriptive, predictive and explanatory personality research: Different goals, different approaches, but a shared need to move beyond the Big Few traits. European Journal of Personality, 34, 1175–1201.
    • Mahmoodi, J., Leckelt, M., van Zalk, M.W. H., Geukes, K., & Back, M. D.(2017). Big Data approaches in social and behavioral science: Four key trade-offs and a call for integration. Current Opinion in Behavioral Sciences, 18, 57—62. 

    KI & Statistik und Psychologische Methodenlehre / AI & Statistics and Psychological Methods

    • Nestler, S. (2022). Regularized 2SLS estimation of structural equation model parameters. Structural Equation Modeling: A Multidisciplinary Journal, 29, 920-932.

    • Nestler, S. & Humberg, S. (2022). A Lasso and a regression tree mixed-effect model with random effects for the level, the residual variance, and the autocorrelation. Psychometrika, 87, 506-532.

    • Salditt, M., Humberg, S., & Nestler, S. (in press). Gradient tree boosting for hierarchical data. Multivariate Behavioral Research.

    • Scharf, F., & Nestler, S. (2019). Should regularization replace simple structure rotation in Exploratory Factor Analysis? Structural Equation Modeling: A Multidisciplinary Journal, 26, 576-590.

    KI & Organisations- und Wirtschaftspsychologie / AI & Organizational and Business Psychology

    • Eisbach, S., Daugs, F., Thielsch, M. T., Böhmer, M., & Hertel, G. (2023). Predicting Rating Distributions of Website Aesthetics with Deep Learning for AI-Based Research. ACM Transactions on Computer-Human Interaction (TOCHI), 30 (3), Article 37, 1-28. https://doi.org/10.1145/3569889
    • Eisbach, S., Heghmanns, M. & Hertel, G. (2022). Künstliche Intelligenz im Strafverfahren am Beispiel von Kriminalprognosen. Zeitschrift für Internationale Strafrechtswissenschaft, 7-8, 489-496.
    • Eisbach, S., Langer, M., Hertel, G. (2023). Optimizing human-AI collaboration: Effects of motivation and accuracy information in AI-supported decision-making. Computers in Human Behavior: Artificial Humans, 1(2), 100015. https://doi.org/10.1016/j.chbah.2023.100015
    • Eisbach, S., Mai, O., Hertel, G. (2024). Combining theoretical modelling and machine learning approaches: The case of teamwork effects on individual effort expenditure. New Ideas in Psychology, 73, 101077, https://doi.org/10.1016/j.newideapsych.2024.101077 
    • Eisbach, S., Mai, O., Hertel, G. (2023). Synergy between theoretical modelling and machine learning approaches: The case of teamwork effects on individual motivation. Manuscript under review.
    • Grunenberg, E., Stachl, C., Breil, S., Schäpers, P., & Back, M. (in press). Predicting and explaining assessment center judgments: A cross-validated behavioral approach to performance judgments in interpersonal assessment center exercises. Human Resource Management.
    • Hertel, G., Fisher, S. & Van Fossen, J. (2024). Motivated Trust in AI: An integrative model considering multiple stakeholder views in HRM. Research in Human Resource Management: The Future of Human Resource Management. Information Age Publishing.
    • Hertel, G., Fisher, S. & Van Fossen, J. (2023). Motivated trust in AI. Invited presentation at the Annual ENOP Symposium 2023 «Trust and Morality in a Post-Truth World», Paris.

    • Hertel, G., Fisher, S. L., & Van Fossen, J. (2023, May 24-27). Motivated Trust in AI - An Integrative Model Considering Multiple Stakeholder Views in HRM [Conference presentation]. 21st Congress of the European Association of Work and Organizational Psychology, Katowice, Poland.

    • Hertel, G. (2019). Vertrauen im Zeitalter von KI: Ersetzen Algorithmen soziale Kompetenz im Recruiting und Assessment? Keynote auf dem BVMW Personal Kongress, Münster.
    • Höddinghaus, M., Sondern, D. & Hertel, G. (2021). The automation of leadership functions: Would people trust decision algorithms? Computers in Human Behavior, 116, 106635. 
    • Hommel, B., Nilsson, A., & Schäpers, P. (2022, 15 July). The Effectiveness of High-Dimensional Semantic Construct-Vectors in Measuring Personality Traits with Open-Ended Questions. European Conference of Personality 2022, Madrid, Spain.
    • Krumm, S., Thiel, A., Reznik, N., Freudenstein, J.-P., Schäpers, P., & Mussel, P. (in press). Creating a psychological test in a few seconds: Can ChatGPT develop a psychometrically sound situational judgment test? European Journal of Psychological Assessment.

    Abgeschlossene Promotionen / completed Dissertations

    • Simon Eisbach (2023): The integration of machine learning in the psychological research process.
    • Miriam Höddinghaus (2021): Leadership in the digital age: Implications of digitalization on context, communication, and functions of leadership.

  • Teaching

    The Institutes of Psychology addresses the topic of "artificial intelligence in psychology" in various courses and offers students an introduction to the theoretical foundations of machine learning (ML). The following topics, among others, are addressed and taught in the teaching:

    • Personality psychology: ML prediction of personality based on social media data.
    • Organizational and business psychology: AI in human resource management (e.g., automated review of job applications by AI, identification of high-potential employees by AI, or the development and use of AI-controlled chatbots)
    • Statistics & Methods: ML methods (e.g., regularizing regression models such as ridge and lasso regression, regression trees and forests) are part of the statistics lectures in the Master of Psychology.
    • Clinical psychology: Lectures on AI interventions, including internet therapy, app-based psychological interventions, and virtual reality therapy
    • Experimental Psychology: Advanced research classes on ML in the Master's program. The content includes methods (optimization, supervised / unsupervised learning, data preparation) as well as algorithms (support vector machines, decision trees, artificial neural networks). In addition, application areas are presented and strengths and weaknesses of machine learning are highlighted. In combination with this, the concepts learned will be applied step by step using an example of a neural network, so that each student develops a complete, functioning neural network by the end of the course.
    • Committee for Artificial Intelligence in Teaching for the Field of Psychology: Discussion forum, training series, sharing of teaching resources - Learnweb

  • Research projects