From Brain Signals to Emotional and Cognitive Markers: An Introduction to Electroencephalography
Stanisław Adamczyk, PhD Candidate
Centre for Brain Research, Jagiellonian University
Doctoral School in Social Sciences, Jagiellonian University
When approached with caution, electroencephalography (EEG) offers unique insights into the neural correlates of perception, attention, and emotion. It gathers information up to one thousand times per second, allowing us to observe brain dynamics that are too brief to be captured by other methods.
However, special care should be taken when interpreting EEG results. This method has limitations in pinpointing the exact location of the measured activity and is highly susceptible to confounds caused by among others, muscles. Moreover, we often measure only correlates of cognitive processes – sometimes poorly understood – rather than the processes themselves. Keeping these limitations in mind is essential to avoid drawing overly confident conclusions.
During my workshop, I will address these topics in an approachable way to help you gain an initial understanding of EEG. I will also connect this introduction to examples from social neuroscience, giving you ideas on how to incorporate EEG into your own research. We will cover topics such as signal preprocessing, event-related potentials, spectral analysis, connectivity analysis, source localization, and statistical evaluation of EEG data.
The workshop will include both lecture and practical exercises, allowing you to experience some of the challenges involved in working with brain signals firsthand. Finally, the last part of the workshop will be dedicated entirely to your questions about EEG and its potential applications in your research.
Dyadic Data Analyses with the Actor-Partner Interdependence Model
Kay Brauer, PhD
Martin Luther University Halle-Wittenberg, Germany
The study of dyads (e.g., romantic couples, parent-child, colleague, supervisor-employee, teacher-student, therapist-client dyads) is gaining increasing popularity in research on the analysis of social relationships. The evaluation of datasets characterized by pairwise interdependence among dyad members requires specialized analytical approaches. The Actor-Partner Interdependence Model (APIM, Cook & Kenny, 2005) is the state-of-the-art method for dyadic data analysis. The APIM enables the computation of relationships between predictor and outcome variables at intrapersonal and interpersonal levels (actor and partner effects) while taking dyadic interdependence into account. In this workshop, I will provide a theoretical introduction and practical insights into APIM analyses. This includes computing a standard APIM analysis on basis of an example data set with the freely available demo version of Mplus. The aim of this workshop is to provide participants with a basic understanding of setting up, computing, and interpreting a standard APIM analysis of a dyadic data set.
Science Communication: Good Practices
Dominika Bulska, PhD
University of Warsaw
SWPS University
How should we talk about the results of scientific research? What tools should we use to do it well? Does science communication depend on the medium used? Why is it worth discussing scientific research results? These and other questions will be addressed during a practical workshop devoted to the communication of scientific research findings to the public.
Methods for Longitudinal Data Analysis
Maciej Górski, PhD Candidate
Faculty of Psychology, University of Warsaw
Institute of Psychology, Polish Academy of Sciences
Wojciech Podsiadłowski, PhD Candidate
Institute for Social Studies, University of Warsaw
The workshop focuses on applying longitudinal data analysis methods to participants’ research projects, with particular emphasis on research in the field of social psychology. The workshop is primarily application-oriented. Successive methods will be presented along with examples of implementation in RStudio and interpretation of results, and their selection will be tailored to the knowledge and needs of the group. Next, participants will work on real data in RStudio, deepening their knowledge of selected techniques depending on their level of advancement and interests. After completing the workshop, participants will be able to conduct and interpret basic longitudinal analyses.
How Artificial Intelligence Makes Decisions: An Introduction to Decision Trees
Agnieszka Szymańska, PhD; DSc, Associate Professor
Institute of Psychology, Cardinal Stefan Wyszynski University in Warsaw
The workshop focuses on decision trees as one of the fundamental and most interpretable approaches used in contemporary artificial intelligence and machine learning systems. Decision trees constitute an important starting point for understanding how AI models make decisions based on data and how the process of automated inference operates.
During the workshop, the concept of decision trees will be discussed in the context of artificial intelligence, including their structure, underlying logic, and role in building predictive models. Particular emphasis will be placed on model interpretability and the possibility of tracing the decision-making process, which is crucial both in scientific research and in the responsible application of AI. Participants will become familiar with the general principles of training decision-based models as well as with typical challenges related to their practical use.
The workshop has an introductory character and focuses on understanding the ideas underlying the use of decision trees in AI systems, without requiring prior technical background or knowledge of specific algorithms.
The workshop is intended for participants interested in artificial intelligence, data analysis, and interpretable decision-making models, especially those who wish to gain a better understanding of how AI “makes decisions”.