Personality Computing Network (PCN)

• Applicant: Professorin Dr. Ramona Schödel
• Subject Area: Personality Psychology, Clinical and Medical Psychology, Methodology
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 516600480

With the advent of the digital age and the seemingly infinite availability of various digital data, computational modeling approaches are currently experiencing a massive expansion in many scientific disciplines, including personality psychology. These novel modeling techniques with their capability to handle large amounts of complex data are a prerequisite to exploiting the full potential of digital data regarding behavioral and situational information recorded in real life. Accordingly, Personality Computing (PC) has started to emerge as an approach drawing on modeling techniques from the data sciences, such as machine learning (ML), to apply to personality research. However, in its current nascent state, PC still lacks standardization on a meta-methodological level. This deficiency especially refers to standards for the application of ML algorithms, the reporting of their results, as well as their validation for personality assessment. Within the framework of a scientific network and in collaboration with experts from the field, we, therefore, aim to establish best practices for the application and reporting of methodological procedures and to develop validation procedures for ML-based personality research. To this end, the network will produce a best practices article integrating interdisciplinary perspectives while fostering networking and the expansion of individual PC research agendas. We will disseminate the network’s results via joint symposia, workshops for early career researchers, and an Internet presence on a website. These steps will contribute to the overarching goal of our network to further consolidate the integration of PC into personality psychology and, thus, advance our understanding of personality structure and processes.

• DFG Programme: Scientific Networks