EPÆG bridges the gulf between basic sciences and applied sciences to generate creative approaches and practicable solutions in the context of Ergonomics, Psychological Æsthetics and Gestaltung.
EPÆG brings together a team of scientists with expertise in the respective fields. Our research agenda ranges from psychophysics to high level cognitive mechanisms. We develop and empirically test psychological theories of Æsthetic perception and cognition. We develop guidelines and principles for applied solutions in ergonomics and design.
What does EPÆG stand for?
Ergonomie (ergonomics) aims to enhance the interaction of humans and machines. Here, our group investigates the needs and interests of users as well as the improvement of the used machines.
Psychologische Æsthetik (Psychological Aesthetics)
In the field of Psychologische Æsthetik (psychological aesthetics) we link concepts of humanities such as beauty and Erhabenheit (the sublime) with psychological concepts such as attention, classification, preference and memory. Our aim is to contribute to an interdisciplinary understanding of Æsthetics, to fundamental Æsthetic theories in empirical and psychological research. We also believe that research in Æsthetics contributes to the advancement of psychological theories in general as it is an ideal scientific domain to systematically investigate the interaction of cognitive and affective functions.
In the field of Gestalt (English: just also Gestalt), we basically follow the tradition of Gestalt psychology to further explore and develop higher principles of Gestalt perception, which enables us to give advice for Gestalt guidelines and thus, support the creative processes of product development and marketing strategies. Further, we develop test paradigms to predict future Æsthetic appreciation, cycles of perceived innovativeness and typicality to enhance the validity of product evaluations. This helps to optimize the designing of consumer products, especially if they are highly innovative.
What follows is a short description of methods employed by EPÆG. You’ll find more detailed information here:
- Carbon, C. C. (2018). Empirical Aesthetics: In quest of a clear terminology and valid methodology. In Kapoula, Z., Volle, E., Renoult, J., & Andreatta, M. (Eds.), Exploring Transdisciplinarity in Art and Sciences (S. 107-119). Springer Nature: Heidelberg.
- Carbon, C. C. (2016). Integration of user-centric psychological and neuroscience perspectives in experimental design research. In P. Cash, T. Stanković, & M. Štorga (Eds.), Experimental design research: Exploring complex design activity (pp. 113-126; 1. ed.). Cham: Springer.
EPÆG follows a multimethodical and multimodal research strategy—multimethodical in terms of behavioural, neurophysiological and neuropsychological research procedures; multimodal in terms of going beyond single modalities by investigating cross-modal and multimodal aspects of, for instance, aesthetics in car interiors (visual properties, haptics) or adaptation effects (visual and acoustical).
- Descriptive statistics (numeral and graphical inspection and analysis)
- Inferential statistics (e.g., experimental designs, correlational analysis, group comparisons, analysis of variance, multivariate methods, multilevel models)
- Practical statistics (e.g., regression analysis, bi-dimensional regression, causal path analysis)
- Statistical classification (e.g., cluster analysis, muliti-dimensional scaling, discrimination analysis)
- Further complex statistical methods (e.g., structural equation models, multi-group analysis)
- Empirical test construction (e.g., factor analysis)
- To explore multifaceted phenomena (like the emergence and dissemination of conspiracy theories), we also collect data in the field. The gathering of qualitative data — e.g., with a chinese whisper design of repeated playing and re-production — is an integral part of our research. These studies are usually accompanied by quantitative pen-and-paper approaches; and are, in most cases but a first step towards a controlled examination of single aspects in the lab.
Cognitive modelingIn addition to statistical approaches, we apply the full range of machine learning algorithms to find regularities in the data of multi-factorial, exploratory studies. By using classification algorithms (like decision trees and ID3), artificial neural networks (ANN) and other means, we get a grip on phenomena with lots of independent variables – where, with regression analysis or an ANOVA, hundreds of test subjects would be needed. This helps us identify the most important determinants, that then will enter a classical design.
Specific experimental designs
- Adaptation paradigm
- Repeated Evaluation Technique (RET)
- SDT implementation
- Measure of emotional reaction via startle reflex
- IAT/mdIAT (multi-dimensional Implicit Association Test)
- Eyetracking (scan paths, pupillometry, and attentional landscapes)
- EDA/SCR/GSR (electrodermal activity/skin conductance response/galvanic skin response)
- EEG/MEG (electroencephalogram, magnetoencephalogram)
- EMG (electromyogram)
- fMRI (functional magnet resonance imaging)