Publications by authors named "Tycho T de Back"
Functional near-infrared spectroscopy (fNIRS) is a promising technique for non-invasively assessing cortical brain activity during learning. This technique is safe, portable, and, compared to other imaging techniques, relatively robust to head motion, ocular and muscular artifacts and environmental noise. Moreover, the spatial resolution of fNIRS is superior to electroencephalography (EEG), a more commonly applied technique for measuring brain activity non-invasively during learning.
View Article and Find Full Text PDFAlthough many scholars deem non-invasive measures of neurophysiology to have promise in assessing learning, these measures are currently not widely applied, neither in educational settings nor in training. How can non-invasive neurophysiology provide insight into learning and how should research on this topic move forward to ensure valid applications? The current article addresses these questions by discussing the mechanisms underlying neurophysiological changes during learning followed by a SWOT (strengths, weaknesses, opportunities, and threats) analysis of non-invasive neurophysiology in learning and training. This type of analysis can provide a structured examination of factors relevant to the current state and future of a field.
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- Prior research suggests neurophysiological measures can effectively provide insights into learning, but most studies focus on single outcomes rather than the interaction between various measures.
- The current study investigated implicit visuomotor sequence learning by using multiple neurophysiological measurements, such as EEG and skin conductance, alongside behavioral performance during a task involving arm movements.
- Results showed that while behavior indicated sensitivity to sequence learning through quicker responses, neurophysiological measures did not specifically reflect this learning; however, some measures did respond to general task demands, indicating broader learning effects.
Non-invasive neurophysiological measures of learning: A meta-analysis.
Neurosci Biobehav Rev
April 2019
Article Synopsis
- A meta-analysis of 113 experiments revealed that neurophysiological measures, primarily using electroencephalography and eye-tracking, showed large effect sizes in the context of learning.
- While neurophysiological outcomes were influenced by factors such as the sensory system, learning duration, feedback, and participant age, behavioral outcomes were not, leading to smaller effect sizes for neurophysiology when these factors were controlled.
- The study suggests that neurophysiology is a valid measure for assessing learning and calls for further research to explore the complex interactions between learning, neurophysiology, behavior, and contextual factors.
Respiratory Biofeedback Does Not Facilitate Lowering Arousal in Meditation Through Virtual Reality.
Appl Psychophysiol Biofeedback
March 2019
The current study examined the effectiveness of respiratory biofeedback in lowering subjective and objective arousal after stress. Participants were presented with a meditation session in virtual reality while subjective and objective arousal were measured, the latter measured through ECG and EEG. Three conditions were used: (a) a respiratory biofeedback condition, in which visual feedback was paired to breathing; (b) a control feedback placebo condition, in which visual feedback was not paired to breathing; and (c) a control no-feedback condition, in which no visual feedback was used.
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