Effects of visual and auditory delta band stimuli on sleep

Abstract

AbstractThis research explored the effect of teaching conditional discriminations with three procedures on the derivation of 36 stimuli relations (derived relations). The stimuli used consisted of three characteristics musical instruments, along with the corresponding picture. In the first experiment six university students were trained with simple stimuli and tested with compound auditory–visual samples; therefore, a one‐to‐many structure was used. In the second experiment, auditory stimuli were replaced by visual stimuli, for the samples used, for new students. A third experiment was implemented with an extra phase of training with compound stimuli for six new students. The structure of the experiments was: pretests (Xbcd–A; Xacd–B; Xabd–C; Xabc–D), training (A–B; A–C; A–D), and posttests (same as pretests). The difference between these conditions was the kind of stimuli used and a new phase of teaching used in condition 3: (Xbcd–A). The results indicate that training with simple stimuli on discriminations that include stimuli that are easy to discriminate from each other (words and sounds) is a sufficient condition for good posttest performance. However, when comparisons are made difficult (words only), participants show better performance on new tests if they have a learning history with compound stimuli.

Publication
IBEC (International Biomedical Engineering Conference), Seoul, Korea, 2016
Heejun Lee
Heejun Lee
PhD Student, University of Groningen

His research interests include biomedical signal processing and wearable IT.

Minsoo Yeo
Minsoo Yeo
Software Engineer, DRAX, Department of Technology Research Center

His research interests include biomedical signal processing and embedded software.

Youngjoo Kim
Youngjoo Kim
ASML, Netherlands

Her research interests include visual analytics in big data and blind signal processing with applications in astronomy and biomedicine.

Cheolsoo Park
Cheolsoo Park
Professor

His research interests include machine learning, adaptive signal processing, computational neuroscience, and wearable technology.