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Brain Plasticity Through the Life Span: Learning to Learn and Action Video Games
Open accessAbstract
The ability of the human brain to learn is exceptional. Yet, learning is typically quite specific to the exact task used during training, a limiting factor for practical applications such as rehabilitation, workforce training, or education. The possibility of identifying training regimens that have a broad enough impact to transfer to a variety of tasks is thus highly appealing. This work reviews how complex training environments such as action video game play may actually foster brain plasticity and learning. This enhanced learning capacity, termed learning to learn, is considered in light of its computational requirements and putative neural mechanisms.
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“A wealth of evidence indicates that performance on almost any cognitive activity, including basic low-level visual discrimination of perceptual features, shows gradual improvement with training (for review see Bavelier et al., 2012).”
From Working memory training involves learning new skills (2019) · cites this paper as Bavelier et al., 2012
In the citing paper
“Interestingly, this form of generalization may call upon the same executive mechanisms of “learning to learn” that we discussed the previous section: to generalize effectively the brain must know how to generalize, i.e., compare the relevant aspects of the different task structures (Bavelier et al., 2012).”
From Attention, learning, and the value of information (2012) · cites this paper as Bavelier et al., 2012
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Metadata
- Authors
- Bavelier, D., Green, C. S., Pouget, A., & Schrater, P.
- Affiliations
- Department of Psychology and Education Sciences, University of Geneva, 1211 Geneva 4, Switzerland
- University of Wisconsin–Madison (US)
- University of Geneva (CH)
- University of Minnesota (US)
- Venue
- Annual Review of Neuroscience
- Publisher
- Annual Reviews
- Published
- 2012-07-21
- Volume
- 35
- Issue
- 1
- Pages
- 391-416
- ISSN
- 0147-006X (ISSN-L)
- 1545-4126
- OpenAlex
- W2120804125
- Cited by
- 541
- References
- 148
- Citations by year
- 2026: 34 · 2025: 32 · 2024: 31 · 2023: 35 · 2022: 38 · 2021: 43 · 2020: 54 · 2019: 39 · 2018: 35 · 2017: 42 · 2016: 41 · 2015: 45 · 2014: 35 · 2013: 31 · 2012: 4
- FWCI
- 2.91
- Type
- journal article
- Language
- en
- Open access
- Licence
- cc-by
- Locations
- Annual Review of Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
- Funders
- National Institute on Drug Abuse (DA-0346785)
- National Eye Institute (EY-016880, EY-0285, R01EY016880)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neuroscience, Education and Cognitive Function
- Life Sciences › Neuroscience › Developmental Neuroscience › Neurogenesis and neuroplasticity mechanisms
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Functions and Memory
- Keywords
- neuroplasticity, meta-learning, action video games, transfer of learning, computational models of learning, neural mechanisms of learning, lifespan development, rehabilitation training, employee training
- MeSH terms
- Algorithms, Brain, Humans, Human Development, Learning, Neuronal Plasticity, Psychomotor Performance, Transfer, Psychology, Neural Networks, Computer, Video Games
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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