Details
| Original language | English |
|---|---|
| Pages (from-to) | 607-629 |
| Number of pages | 23 |
| Journal | Reviews in the neurosciences |
| Volume | 33 |
| Issue number | 6 |
| Early online date | 7 Feb 2022 |
| Publication status | Published - 26 Aug 2022 |
Abstract
The field of neurofeedback training (NFT) has seen growing interest and an expansion of scope, resulting in a steadily increasing number of publications addressing different aspects of NFT. This development has been accompanied by a debate about the underlying mechanisms and expected outcomes. Recent developments in the understanding of psychophysiological regulation have cast doubt on the validity of control systems theory, the principal framework traditionally used to characterize NFT. The present article reviews the theoretical and empirical aspects of NFT and proposes a predictive framework based on the concept of allostasis. Specifically, we conceptualize NFT as an adaptation to changing contingencies. In an allostasis four-stage model, NFT involves (a) perceiving relations between demands and set-points, (b) learning to apply collected patterns (experience) to predict future output, (c) determining efficient set-points, and (d) adapting brain activity to the desired ("set") state. This model also identifies boundaries for what changes can be expected from a neurofeedback intervention and outlines a time frame for such changes to occur.
Keywords
- allostasis framework, neurofeedback training, psychophysiological regulation, self-regulation
ASJC Scopus subject areas
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Reviews in the neurosciences, Vol. 33, No. 6, 26.08.2022, p. 607-629.
Research output: Contribution to journal › Review article › Research › peer review
}
TY - JOUR
T1 - Neurofeedback and neural self-regulation
T2 - A new perspective based on allostasis
AU - Mirifar, Arash
AU - Keil, Andreas
AU - Ehrlenspiel, Felix
N1 - Funding Information: Research funding: AM and FE received no financial support for the research, authorship, and/or publication of this article. AK was supported by grant R01MH112558, by the National Institute of Mental Health.
PY - 2022/8/26
Y1 - 2022/8/26
N2 - The field of neurofeedback training (NFT) has seen growing interest and an expansion of scope, resulting in a steadily increasing number of publications addressing different aspects of NFT. This development has been accompanied by a debate about the underlying mechanisms and expected outcomes. Recent developments in the understanding of psychophysiological regulation have cast doubt on the validity of control systems theory, the principal framework traditionally used to characterize NFT. The present article reviews the theoretical and empirical aspects of NFT and proposes a predictive framework based on the concept of allostasis. Specifically, we conceptualize NFT as an adaptation to changing contingencies. In an allostasis four-stage model, NFT involves (a) perceiving relations between demands and set-points, (b) learning to apply collected patterns (experience) to predict future output, (c) determining efficient set-points, and (d) adapting brain activity to the desired ("set") state. This model also identifies boundaries for what changes can be expected from a neurofeedback intervention and outlines a time frame for such changes to occur.
AB - The field of neurofeedback training (NFT) has seen growing interest and an expansion of scope, resulting in a steadily increasing number of publications addressing different aspects of NFT. This development has been accompanied by a debate about the underlying mechanisms and expected outcomes. Recent developments in the understanding of psychophysiological regulation have cast doubt on the validity of control systems theory, the principal framework traditionally used to characterize NFT. The present article reviews the theoretical and empirical aspects of NFT and proposes a predictive framework based on the concept of allostasis. Specifically, we conceptualize NFT as an adaptation to changing contingencies. In an allostasis four-stage model, NFT involves (a) perceiving relations between demands and set-points, (b) learning to apply collected patterns (experience) to predict future output, (c) determining efficient set-points, and (d) adapting brain activity to the desired ("set") state. This model also identifies boundaries for what changes can be expected from a neurofeedback intervention and outlines a time frame for such changes to occur.
KW - allostasis framework
KW - neurofeedback training
KW - psychophysiological regulation
KW - self-regulation
UR - http://www.scopus.com/inward/record.url?scp=85124630107&partnerID=8YFLogxK
U2 - 10.1515/revneuro-2021-0133
DO - 10.1515/revneuro-2021-0133
M3 - Review article
C2 - 35122709
AN - SCOPUS:85124630107
VL - 33
SP - 607
EP - 629
JO - Reviews in the neurosciences
JF - Reviews in the neurosciences
SN - 0334-1763
IS - 6
ER -