logo
Loading...
Ultradian Rhythms in Task Performance, Self-Evaluation, and EEG Activity
PsychologyPerceptual and Motor Skills

Ultradian Rhythms in Task Performance, Self-Evaluation, and EEG Activity

M. Hayashi, K. Sato, et al.

EEG power spectra, mood, performance, and self-evaluation measured every 15 minutes for 9 hours in 10 male students revealed distinct ultradian cycles: a ~90–100 minute rest–activity rhythm linked to behavior and subjective states, and multiple EEG rhythms (6 and 10–18 cycles/day) supporting a multioscillator model. This research was conducted by Mitsuo Hayashi, Kayo Sato, and Tadao Hori.... show more
Abstract
Many studies have shown the existence of cycles of approximately 90 to 100 minutes (corresponding to Kleitman's basic rest-activity cycle) and several hours ('slow ultradian rhythm' cycles). EEG power spectra, mood, performance, and self-evaluation of performance were measured every 15 minutes for 9 hours for 10 male university students. Principal component analysis was applied to extract ultradian fluctuations in EEG activity, task performance, and subjective variables. The analysis indicated two common temporal fluctuations: one in behavioral and subjective variables, and the other in EEG activity. Spectral analysis indicated that the former fluctuated at a rate of 12 cycles per day (corresponding to the basic rest-activity cycle), and the latter comprised both a slower (6 cycles per day) and a faster (10 to 18 cycles per day) cycle, supporting the multioscillator hypothesis of ultradian rhythm.
Publisher
Perceptual and Motor Skills
Published On
Authors
Mitsuo Hayashi, Kayo Sato, Tadao Hori
Tags
ultradian rhythmbasic rest-activity cycleEEG power spectraprincipal component analysisspectral analysismood and performancemultioscillator hypothesis
Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 22+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny