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Cited by 137EEG and Brain-Computer InterfacesBehavioral and Psychological StudiesNeural and Behavioral Psychology Studies

What learning theories can teach us in designing neurofeedback treatments

Open access
Strehl, Ute·2014·Frontiers in Human Neuroscience
DOI 10.3389/fnhum.2014.00894PMC PMC4222234
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Abstract

Popular definitions of neurofeedback point out that neurofeedback is a process of operant conditioning which leads to self-regulation of brain activity. Self-regulation of brain activity is considered to be a skill. The aim of this paper is to clarify that not only operant conditioning plays a role in the acquisition of this skill. In order to design the learning process additional references have to be derived from classical conditioning, two-process-theory and in particular from skill learning and research into motivational aspects. The impact of learning by trial and error, cueing of behavior, feedback, reinforcement, and knowledge of results as well as transfer of self-regulation skills into everyday life will be analyzed in this paper. In addition to these learning theory basics this paper tries to summarize the knowledge about acquisition of self-regulation from neurofeedback studies with a main emphasis on clinical populations. As a conclusion it is hypothesized that learning to self-regulate has to be offered in a psychotherapeutic, i.e., behavior therapy framework.

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Crossref · 10.3389/fnhum.2014.00894

Cites

53 of 53 references verified0 citation errors · 0 not checkable

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  • No explicit strategies or instructions were given because implicit strategies have been shown to produce better outcomes.

    “Notably, no explicit strategies or instructions were given as, with few exceptions [204], implicit strategies have been shown to produce better outcomes [205–210].”

    From Infraslow closed-loop brain training for anxiety and depression (ISAD): a protocol for a randomized, double-blind, sham-controlled pilot trial in adult females with internalizing disorders (2022) · cites this paper as [206]

    In “Automation: practice makes perfect” (page unavailable), Strehl recommends not prescribing strategies and reports better improvement or within-session learning among healthy participants who used no specific strategy or received no guidance. But the same passage reports no overall performance difference in the guidance comparison, so it does not establish generally better outcomes from implicit strategies. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Manual, rather than automated, thresholding was chosen because it has been reported to lead to better EEG-learning.

    “Manual, rather than automated, thresholding was chosen as it has been reported to lead to better EEG-learning [125, 173, 206, 208].”

    From Infraslow closed-loop brain training for anxiety and depression (ISAD): a protocol for a randomized, double-blind, sham-controlled pilot trial in adult females with internalizing disorders (2022) · cites this paper as [206]

    In “Shaping and the question of threshold regulation” (page unavailable), Strehl criticizes automatic threshold regulation as a questionable way to reward performance. But the same section says this criticism partly applies to manual adjustment too and calls for more research; it does not report better EEG learning with manual thresholding. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Cites

Flagged first, then by citations

Research works printed but not linked: 5

  1. Blume F. (2012). Neurofeedbacktraining bei Kindern mit einer Aufmerksamkeitsdefizit-/Hyperaktivitätsstörung (ADHS): Eine Untersuchung der Trainingsverläufe mit dem Versuch der Klassifikation von Lernern und Nicht-Lernern. Thesis published in Tübingen: Eberhard-Karls-Universität Tübingen.
  2. Fitts P. M. (1964). “ Perceptual-motor skill learning,” in Categories of Human Learning, ed Melton A. W. (New York: Academic Press), 243– 285.
  3. Kisil A. (1992). Rückmeldung und Modifikation Langsamer Hirnrindenpotentiale. Dissertation Tübingen: Eberhard-Karls-Universität Tübingen.
  4. Kisil A. Birbaumer N. (1992). Biofeedback langsamer Hirnpotentiale. Z. Exp. Angew. Psychol. 39, 216– 228. 1413918
  5. Neumann N. (2001). Gehirn-Computer-Schnittstelle: Einflussfaktoren der Selbstregulation Langsamer Kortikaler Hirnpotentiale. Dissertation Tübingen: Schwäbische Verlagsgesellschaft.

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Authors
Strehl, U.
Affiliations
University of Tübingen (DE)
Corresponding author
Strehl, Ute
Venue
Frontiers in Human Neuroscience
Publisher
Frontiers Media SA
Published
2014-11-06
Volume
8
Pages
894-894
ISSN
1662-5161 (ISSN-L)
DOI
10.3389/fnhum.2014.00894
PMC
PMC4222234
OpenAlex
W2038534511
Cited by
137
References
53
Citations by year
2026: 10 · 2025: 7 · 2024: 10 · 2023: 9 · 2022: 12 · 2021: 13 · 2020: 14 · 2019: 16 · 2018: 15 · 2017: 13 · 2016: 9 · 2015: 7 · 2013: 1
FWCI
5.39
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Frontiers in Human Neuroscience (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Deutsche Forschungsgemeinschaft (2503/4-2, STR 597/7-1, 597/7-1)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Social Sciences › Psychology › Developmental and Educational Psychology › Behavioral and Psychological Studies
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
Keywords
neurofeedback, learning theory, operant conditioning, classical conditioning, two-process theory, skill acquisition, self-regulation of brain activity, motivation, trial-and-error learning, feedback, reinforcement, knowledge of results, behavior therapy, clinical populations

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