TY  - JOUR
A1  - Rodrigues, Johannes
A1  - Ziebell, Philipp
A1  - Müller, Mathias
A1  - Hewig, Johannes
T1  - Standardizing continuous data classifications in a virtual T-maze using two-layer feedforward networks
JF  - Scientific Reports
N2  - There continues to be difficulties when it comes to replication of studies in the field of Psychology. In part, this may be caused by insufficiently standardized analysis methods that may be subject to state dependent variations in performance. In this work, we show how to easily adapt the two-layer feedforward neural network architecture provided by Huang1 to a behavioral classification problem as well as a physiological classification problem which would not be solvable in a standardized way using classical regression or “simple rule” approaches. In addition, we provide an example for a new research paradigm along with this standardized analysis method. This paradigm as well as the analysis method can be adjusted to any necessary modification or applied to other paradigms or research questions. Hence, we wanted to show that two-layer feedforward neural networks can be used to increase standardization as well as replicability and illustrate this with examples based on a virtual T-maze paradigm\(^{2−5}\) including free virtual movement via joystick and advanced physiological data signal processing.
KW  - standardized analysis method
KW  - neural network architecture
KW  - two‑layer feedforward networks
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-301096
VL  - 12
IS  - 1
ER  -