Brain Plasticity: Learning, Rehabilitation, and Adaptive Problem Solving
Updated: Aug 22
Dr. Elena R. Marconi¹, Dr. Samuel T. Hwang², Prof. Isabelle K. Fournier³
¹ Department of Systems Neuroscience and Learning, European Institute for Brain Science
² Center for Neurorehabilitation and Cognitive Engineering, Pacific Institute of Biomedical Technology
³ Laboratory of Neural Adaptation and Cognition, Institut des Neurosciences Avancées
[Disclaimer: This is a sample academic article. All author names, affiliations, and institutional details are fictional and have been created solely for illustrative and educational purposes.]
Abstract
Neuroplasticity is the capacity of the nervous system to modify its functional organization, synaptic strength, structural connectivity, and patterns of neural activity in response to experience, learning, environmental demands, injury, and disease. Rather than representing a process in which the adult brain is indiscriminately “rewired,” plasticity occurs through multiple mechanisms operating across different spatial and temporal scales, including long-term potentiation and depression, changes in dendritic spines, modification of inhibitory and excitatory circuits, altered myelination, sensory and motor cortical remapping, and reorganization of distributed neural networks. Learning a new motor or cognitive skill can progressively modify the neural systems involved in task performance, while repeated practice can change both behavioral efficiency and measurable patterns of brain activity and structure. Importantly, substantial plasticity persists throughout adulthood, although its magnitude, mechanisms, and constraints change with age and differ across brain regions. Following stroke or other neurological injury, rehabilitation can exploit experience-dependent plasticity to improve function through intensive, task-specific, and appropriately timed practice, but recovery should not be interpreted as the brain simply creating an unrestricted set of alternative pathways around damaged tissue. Neuroplasticity is similarly relevant to cognitive flexibility and problem solving because expertise, exposure to varied tasks, strategy switching, and the acquisition of new conceptual representations can alter how information is processed; however, direct claims that generic “brain-training” exercises substantially increase general intelligence or creativity require caution because improvements frequently remain specific to the trained tasks. Physical exercise, sleep, environmental enrichment, learning, and social interaction can influence mechanisms associated with neural plasticity, whereas chronic stress, sleep disruption, neurological disease, and other adverse conditions can interfere with adaptive neural change. Emerging technologies—including non-invasive brain stimulation, neurofeedback, brain–computer interfaces, robotic rehabilitation, virtual reality, and machine-learning-assisted adaptive training—are increasingly being investigated as methods for guiding or measuring plasticity, particularly in rehabilitation. Particular attention is given to synaptic plasticity, motor learning, cortical remapping, adult plasticity, stroke recovery, cognitive training, creativity and adaptive problem solving, physical exercise, aging, neurotechnology, and the distinction between adaptive and maladaptive plasticity. Brain plasticity is therefore best understood not as unlimited biological self-reprogramming, but as a constrained process of experience → neural activity → synaptic and network adaptation → behavioral change → further experience, through which learning and rehabilitation progressively reshape the functional organization of the nervous system.
1. Introduction
Neuroplasticity describes the capacity of the nervous system to alter synaptic efficacy, cellular structure, network connectivity, and functional organization in response to activity and experience. One of the earliest experimental foundations was the demonstration of long-lasting potentiation of synaptic transmission in the hippocampus following patterned stimulation, establishing that previous neural activity can persistently modify the effectiveness of subsequent synaptic communication [10.1113/jphysiol.1973.sp010273]. At larger scales, human neuroimaging has shown that acquiring unfamiliar motor skills can be associated with measurable changes in gray-matter structure [10.1038/427311a] and white-matter organization [10.1038/nn.2412], while animal imaging has demonstrated rapid formation and selective stabilization of dendritic spines during motor learning [10.1038/nature08389]. Plasticity is therefore not a single mechanism and does not mean that arbitrary regions of the brain can freely assume any function; it is constrained by anatomy, developmental history, molecular state, injury location, behavioral relevance, timing, and the patterns of neural activity generated by experience [10.1146/annurev.neuro.27.070203.144216]. The popular expression “rewiring the brain” captures only part of this process because learning may involve changes in synaptic strength without generating new long-range pathways, while structural remodeling can occur at synapses, dendrites, axons, myelin, and network levels. Understanding neuroplasticity consequently requires distinguishing genuine biological adaptation from simplified claims that general cognitive performance can be increased merely by “making neurons fire more.”
2. Results and Discussion
Neuroplasticity operates over timescales extending from milliseconds to years and across spatial scales ranging from individual synapses to distributed cortical networks. Short-term changes in synaptic efficacy can influence information processing almost immediately, whereas repeated experience can stabilize selected synapses, alter dendritic-spine turnover, modify cortical representations, reorganize white-matter microstructure, and change how large-scale networks participate in a task [10.1113/jphysiol.1973.sp010273; 10.1038/nature08389; 10.1038/nn.2412]. Behavioral improvement therefore emerges from multiple interacting mechanisms rather than from a universal “plasticity switch,” and the biological changes induced by practice depend strongly on what is practiced, how intensively it is practiced, and whether the training produces meaningful behavioral demands.
2.1. Synaptic Plasticity: Changing the Strength of Communication
Synapses are not fixed communication junctions but can alter their effectiveness according to patterns of pre- and postsynaptic activity. Long-term potentiation provides a canonical example in which particular stimulation patterns produce a persistent increase in synaptic response, while complementary mechanisms of synaptic depression reduce transmission under other activity patterns [10.1113/jphysiol.1973.sp010273]. Such changes can involve receptor trafficking, intracellular signaling, alterations in neurotransmitter release, and ultimately structural modifications of synaptic connections. It is therefore misleading to describe brain improvement simply as “increasing synapse firing”: neural function depends on precisely regulated excitation, inhibition, timing, and network coordination rather than maximal firing rates. Excessive neuronal activity can impair information processing or become pathological, whereas useful learning is associated with selective modification of specific circuits.
2.2. Learning Can Physically Reshape Synaptic Architecture
Modern in-vivo microscopy has shown that learning can be accompanied by structural changes at individual neuronal connections. In mice trained on a new forelimb motor task, dendritic spines formed rapidly in the relevant motor cortex, and a subset of these new spines was selectively stabilized over extended periods, while different motor experiences recruited partly distinct populations of newly stabilized connections [10.1038/nature08389]. These observations provide a concrete cellular mechanism through which repeated experience can leave persistent physical traces in neuronal circuitry. The finding does not imply that every memory is stored in a single new synapse, but it supports the broader concept that long-term behavioral learning can be accompanied by selective remodeling of the structural substrate through which neurons communicate.
2.3. Learning a Skill Changes Distributed Brain Networks
Motor learning involves multiple stages, including rapid improvement during initial practice, slower consolidation, increased automaticity, and long-term retention, each associated with changes across motor cortex, premotor areas, cerebellum, basal ganglia, and related networks [10.1016/j.neuron.2011.10.008]. Early learning often requires substantial conscious attention and error correction, whereas later performance can become faster and less cognitively demanding as task representations are refined. Neuroplasticity therefore does not merely create additional neural activity; efficient learning can also reduce unnecessary activity by making processing more specialized and coordinated. Expertise represents an optimization of network organization rather than simply a larger quantity of neural activation.
2.4. Structural Plasticity Is Detectable in the Adult Human Brain
Human neuroimaging provides evidence that adult learning can be accompanied by measurable structural changes. Adults who learned juggling showed transient alterations in gray-matter regions associated with processing and coordinating visual motion, demonstrating that detectable brain-structure changes can occur after relatively short periods of acquiring a novel skill [10.1038/427311a]. A related diffusion-imaging experiment found localized changes in white-matter microstructural measures after juggling training, indicating that experience-dependent adaptation is not restricted to gray matter [10.1038/nn.2412]. Such imaging measurements do not identify a single cellular mechanism directly — changes in MRI-derived quantities can reflect multiple biological processes, but they strongly contradict the historical view of the healthy adult brain as anatomically static.
2.5. Adult Plasticity Persists, but Age Changes Its Conditions
Neuroplastic capacity decreases in some respects with aging but does not disappear. Older adults trained to perform juggling showed training-associated gray-matter changes, providing experimental evidence that structural adaptation remains possible in later life [10.1523/JNEUROSCI.0742-08.2008]. Aging nevertheless affects neurotransmission, vascular function, sensory acuity, sleep, white-matter integrity, processing speed, and many other biological factors that influence how efficiently new skills are acquired. The aging brain may additionally recruit alternative networks or broader patterns of activity to compensate for declining efficiency in specialized systems, a concept incorporated into models of neurocognitive scaffolding [10.1146/annurev.psych.59.103006.093656]. The scientifically defensible message is therefore not that age is irrelevant, but that meaningful experience-dependent plasticity remains possible throughout much of the lifespan.
2.6. Practice Matters Because Plasticity Is Use-Dependent
Repeated practice strengthens task-relevant representations partly because neural circuits are repeatedly engaged under behaviorally meaningful conditions. Motor-learning research shows that repetition alone is not sufficient: error signals, attention, reward, task difficulty, feedback, and successful performance all influence which neural patterns are reinforced [10.1016/j.neuron.2011.10.008]. Effective learning therefore generally requires practice that remains challenging enough to generate adaptation while being achievable enough to provide informative feedback. Once performance becomes completely automatic, repeating an unchanged task may produce diminishing adaptation because the nervous system is no longer required to solve a new computational problem. Progressive difficulty and variation can consequently be valuable when they remain relevant to the target skill.
2.7. Sleep Contributes to Consolidation of Newly Learned Skills
Learning does not end when practice stops because memory consolidation continues after initial acquisition. Experiments on motor-sequence learning have shown that sleep can produce additional performance improvements beyond those obtained during waking practice, supporting the role of sleep-dependent processes in stabilizing and refining newly learned skills [10.1016/S0896-6273(02)00746-8]. Sleep influences synaptic regulation, memory reactivation, network dynamics, hormonal state, and metabolic processes, making it biologically implausible to treat cognitive training independently of recovery. For practical learning, sustained sleep deprivation can therefore undermine the neural conditions necessary for effective consolidation even when training intensity remains high.
2.8. Physical Exercise Can Influence Brain Structure and Cognition
Aerobic exercise can influence brain function through cardiovascular, metabolic, neurotrophic, and vascular mechanisms rather than by acting as a generic “brain booster.” In a randomized trial involving older adults, aerobic exercise was associated with an increase in hippocampal volume and improvements in spatial memory, accompanied by changes in circulating brain-derived neurotrophic factor [10.1073/pnas.1015950108]. These results provide evidence that physical activity can influence neural systems relevant to cognition, particularly in aging, although effects vary according to exercise type, intensity, health status, duration, and the cognitive outcome examined. Exercise should therefore be considered one contributor to an environment supportive of plasticity rather than a direct substitute for practicing the cognitive or motor function one intends to improve.
2.9. Stroke Demonstrates Both the Power and Limits of Neuroplasticity
Stroke can damage specialized cortical and subcortical networks abruptly, after which recovery depends on a mixture of spontaneous biological processes, compensation, relearning, and activity-dependent reorganization. Animal studies demonstrated that rehabilitative training following cortical injury can reorganize motor representations in surviving cortex and improve skilled movement [10.1126/science.272.5269.1791]. Human rehabilitation similarly benefits from repeated, meaningful use of impaired functions, but the phrase “the brain simply reroutes around the damaged area” is an oversimplification because recovery depends strongly on lesion size, anatomical location, surviving pathways, timing, and the ability of residual circuits to support the required computation. Plasticity can optimize what remains available; it cannot necessarily reconstruct every lost neural system.
2.10. Task-Specific Rehabilitation Provides Stronger Evidence Than Generic Brain Exercise
One of the clearest principles of neurorehabilitation is that improvement tends to be strongest for functions that are repeatedly and specifically practiced. Constraint-induced movement therapy, which encourages intensive use of an affected upper limb while restricting compensatory use of the less-impaired limb, produced sustained functional improvements in selected stroke survivors in a large multicenter clinical trial [10.1001/jama.296.17.2095]. Such results are consistent with use-dependent plasticity but also demonstrate why rehabilitation must remain individualized: an intervention appropriate for a patient with useful residual voluntary movement may not be suitable for someone with a different lesion or level of impairment. Neuroplasticity provides the biological substrate for rehabilitation, but clinical outcome depends on applying appropriate training to the remaining neural and physical capacities.
2.11. Brain–Computer Interfaces Can Couple Intent Directly to Rehabilitation
Brain–computer interfaces provide a particularly interesting rehabilitation strategy because neural signals associated with attempted movement can be detected and linked to external feedback or robotic devices even when voluntary movement is limited. A randomized controlled study in patients with chronic stroke showed that a brain–machine-interface intervention coupled to movement feedback could improve motor function, demonstrating that contingency between neural intention and sensory feedback can potentially be used to reinforce relevant sensorimotor circuits [10.1002/ana.23879]. The concept is biologically attractive because the brain receives immediate evidence that a task-related neural pattern has produced a meaningful consequence. However, brain–computer interfaces remain technically demanding, outcomes vary among individuals, and they should be understood as rehabilitation tools rather than technologies capable of unrestricted neural rewiring.
2.12. Non-Invasive Brain Stimulation May Modulate Plasticity but Is Not a Stand-Alone Solution
Transcranial magnetic stimulation and transcranial direct-current stimulation can modify cortical excitability and have therefore been investigated as adjuncts to neurological rehabilitation [10.1016/S1474-4422(06)70525-7]. The underlying strategy is generally to influence the excitability or balance of neural networks while the patient performs relevant training, thereby creating conditions that may favor experience-dependent adaptation. Effects depend strongly on stimulation location, timing, intensity, individual anatomy, baseline brain state, and the rehabilitation task itself, and results across clinical studies have not been uniformly positive. Brain stimulation should therefore not be portrayed as a direct method for “activating neuroplasticity”; its most plausible role is as a controlled modifier of plastic processes that must still be shaped through behavior.
2.13. Virtual Reality and Robotics Expand the Rehabilitation Environment
Virtual reality and robotic rehabilitation systems can provide large numbers of reproducible training trials, precisely measured performance feedback, adjustable task difficulty, and environments that would be difficult to construct physically. Reviews of virtual-reality interventions after stroke indicate that VR-based approaches can improve aspects of upper-limb function and activities of daily living in some contexts, although evidence quality and comparative advantage over equivalent conventional therapy vary among studies [10.1002/14651858.CD008349.pub4]. The principal neuroplastic value of these systems is not the technology itself but their ability to increase controlled, motivating, task-specific practice. A sophisticated virtual environment with insufficient meaningful repetition is unlikely to outperform a simpler intervention that consistently engages the relevant neural and behavioral functions.
2.14. Brain Training Improves Trained Tasks More Reliably Than General Intelligence
Claims that generic computerized exercises broadly “rewire the brain” and produce large improvements in intelligence, reasoning, or everyday functioning are considerably less secure than evidence for task-specific learning. A large online study involving more than 11,000 participants found substantial improvement on practiced cognitive exercises but little evidence that these gains transferred to untrained cognitive tasks [10.1038/nature09042]. A broad review of the brain-training literature similarly concluded that evidence for far transfer to general cognitive abilities remains limited and substantially weaker than evidence for improvement on trained or closely related tasks [10.1177/1529100616661983]. Neuroplasticity therefore does not guarantee generalized enhancement: because adaptation is often specific to the neural computations repeatedly demanded by training, becoming highly skilled at one cognitive exercise may primarily make the individual better at that exercise.
2.15. Creativity Is a Network Phenomenon, Not a Single “Creative Center”
Inventive problem solving requires the generation of candidate ideas, retrieval and recombination of knowledge, evaluation of constraints, and selection among alternatives, processes distributed across multiple interacting brain systems rather than localized to one anatomical “creativity center.” Functional-connectivity research has shown that individual creative ability can be predicted partly from patterns of interaction among default-mode, executive-control, and salience-related networks, suggesting that creative cognition depends on cooperation among systems often associated with spontaneous idea generation and deliberate evaluation [10.1073/pnas.1713532115]. This does not demonstrate that generic neuroplasticity exercises automatically increase creativity. A more defensible strategy is to expand the repertoire of knowledge and problem representations through learning, deliberately encounter diverse domains, practice alternative solution generation, and repeatedly evaluate ideas against real constraints, thereby giving adaptive neural systems richer material from which novel combinations can emerge.
2.16. Inventive Problem Solving Requires Both Learning and Unlearning
Expertise can enhance problem solving because accumulated knowledge allows rapid recognition of important structures, yet familiar solution patterns can also constrain thinking when previously successful strategies are applied automatically to new problems. Neuroplasticity permits new task representations to become established through repeated alternative experience, but replacing an old strategy is not equivalent to erasing its neural representation. Instead, competing responses may be inhibited, contextualized, or made less likely while new strategies are strengthened. Effective inventive training should therefore alternate between acquiring domain knowledge and deliberately challenging established assumptions, because creativity depends on possessing a sufficiently rich mental model while remaining able to restructure that model when existing approaches fail.
2.17. Stress Can Drive Plasticity in Maladaptive Directions
Plasticity is intrinsically neither beneficial nor harmful; it simply describes the nervous system's capacity to change. Chronic stress can alter dendritic structure, neuroendocrine regulation, hippocampal and prefrontal function, and behavioral responses, demonstrating that repeated experience can reorganize neural systems in directions that impair rather than enhance performance [10.1152/physrev.00041.2006]. Persistent pain, addiction, pathological fear, and certain movement disorders similarly involve durable learning-like changes within neural circuits. The goal of rehabilitation and education is therefore not to maximize plasticity indiscriminately but to guide plasticity toward adaptive representations while limiting reinforcement of maladaptive ones.
2.18. Environment and Social Experience Matter Because Learning Is Contextual
Enriched physical and social environments can expose the nervous system to greater sensory diversity, motor complexity, novelty, and cognitive demand, all of which increase the range of neural circuits engaged during behavior. However, the common phrase “a stimulating environment grows more connections” is too simplistic because adaptation can involve formation, elimination, stabilization, weakening, and reweighting of connections simultaneously. Efficient networks frequently become more selective rather than simply denser. Educational environments designed around neuroplasticity should therefore emphasize sustained attention, meaningful challenges, retrieval, feedback, progressively increasing difficulty, and opportunities to apply knowledge in different contexts rather than relying on superficial novelty or constant stimulation.
2.19. Neurodegenerative Disease Places Strong Constraints on Plasticity
Plastic mechanisms remain relevant in neurodegenerative disorders, but they operate within nervous systems undergoing progressive cellular and network damage. Compensatory recruitment can temporarily support function, and cognitive or physical training may help individuals use residual capacities more effectively, but plasticity does not imply that experience can reverse the primary molecular pathology of diseases such as Alzheimer's or Parkinson's disease. Research on aging suggests that compensatory scaffolding can help maintain performance despite neural deterioration [10.1146/annurev.psych.59.103006.093656], yet compensation has finite capacity. It is therefore important to distinguish rehabilitation and adaptation from disease-modifying treatment when communicating the potential of neuroplasticity.
2.20. Artificial Intelligence Can Make Neurorehabilitation More Adaptive
Machine-learning systems can potentially analyze movement trajectories, electromyographic activity, neuroimaging, electrophysiology, and longitudinal performance to adjust rehabilitation difficulty or identify patterns that predict response to training. In robotic or brain–computer-interface rehabilitation, algorithms can estimate patient intent and modify assistance in real time, producing a closed loop of neural activity → behavioral attempt → measurement → algorithmic adaptation → sensory feedback → further neural activity [10.1002/ana.23879]. The scientific opportunity lies in personalization: instead of providing an identical number and difficulty of trials to every patient, adaptive systems could continuously tune training according to measured performance. Such systems nevertheless require clinical validation and transparent safety constraints because optimization of a digital performance metric does not automatically guarantee meaningful neurological recovery.
3. Conclusion and Outlook
Brain plasticity provides the biological foundation for learning, adaptation, and substantial components of neurological rehabilitation, but its scientific meaning is considerably more precise than the popular idea that the brain can be arbitrarily “rewired.” Synaptic strength can persistently change following activity [10.1113/jphysiol.1973.sp010273], motor learning can rapidly generate and stabilize new synaptic structures [10.1038/nature08389], and acquisition of unfamiliar skills can produce measurable changes in adult gray and white matter [10.1038/427311a; 10.1038/nn.2412]. Plasticity persists into older age [10.1523/JNEUROSCI.0742-08.2008], contributes to rehabilitation following stroke [10.1126/science.272.5269.1791], and can be shaped through task-specific training, brain–computer interfaces, physical activity, and potentially selected neurotechnological interventions [10.1001/jama.296.17.2095; 10.1002/ana.23879; 10.1073/pnas.1015950108]. Yet plasticity is strongly constrained and task dependent: computerized brain training frequently improves the exercises being practiced without producing broad transfer to unrelated cognitive abilities [10.1038/nature09042; 10.1177/1529100616661983], while stress and disease can drive or constrain neural adaptation in undesirable directions [10.1152/physrev.00041.2006]. For inventive problem solving, the most scientifically defensible implication is therefore not that a particular exercise can “switch on creativity,” but that sustained acquisition of diverse knowledge, deliberate practice, strategy switching, feedback, adequate recovery, and repeated exposure to unfamiliar problems progressively expand and reorganize the neural repertoire available for future reasoning. Neuroplasticity is best represented as experience → selective neural activity → synaptic and structural adaptation → altered network dynamics → behavioral learning → new experience, a continuing feedback process through which the nervous system adapts to the problems it is repeatedly required to solve.
References
Bliss, T. V. P., & Lømo, T. (1973). Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. The Journal of Physiology, 232, 331–356. [10.1113/jphysiol.1973.sp010273]
Pascual-Leone, A., Amedi, A., Fregni, F., & Merabet, L. B. (2005). The plastic human brain cortex. Annual Review of Neuroscience, 28, 377–401. [10.1146/annurev.neuro.27.070203.144216]
Draganski, B., Gaser, C., Busch, V., Schuierer, G., Bogdahn, U., & May, A. (2004). Changes in grey matter induced by training. Nature, 427, 311–312. [10.1038/427311a]
Boyke, J., Driemeyer, J., Gaser, C., Büchel, C., & May, A. (2008). Training-induced brain structure changes in the elderly. Journal of Neuroscience, 28, 7031–7035. [10.1523/JNEUROSCI.0742-08.2008]
Scholz, J., Klein, M. C., Behrens, T. E. J., & Johansen-Berg, H. (2009). Training induces changes in white-matter architecture. Nature Neuroscience, 12, 1370–1371. [10.1038/nn.2412]
Xu, T., Yu, X., Perlik, A. J., et al. (2009). Rapid formation and selective stabilization of synapses for enduring motor memories. Nature, 462, 915–919. [10.1038/nature08389]
Dayan, E., & Cohen, L. G. (2011). Neuroplasticity subserving motor skill learning. Neuron, 72, 443–454. [10.1016/j.neuron.2011.10.008]
Walker, M. P., Brakefield, T., Morgan, A., Hobson, J. A., & Stickgold, R. (2002). Practice with sleep makes perfect: Sleep-dependent motor skill learning. Neuron, 35, 205–211. [10.1016/S0896-6273(02)00746-8]
Erickson, K. I., Voss, M. W., Prakash, R. S., et al. (2011). Exercise training increases size of hippocampus and improves memory. Proceedings of the National Academy of Sciences USA, 108, 3017–3022. [10.1073/pnas.1015950108]
Nudo, R. J., Wise, B. M., SiFuentes, F., & Milliken, G. W. (1996). Neural substrates for the effects of rehabilitative training on motor recovery after ischemic infarct. Science, 272, 1791–1794. [10.1126/science.272.5269.1791]
Wolf, S. L., Winstein, C. J., Miller, J. P., et al. (2006). Effect of constraint-induced movement therapy on upper extremity function 3 to 9 months after stroke. JAMA, 296, 2095–2104. [10.1001/jama.296.17.2095]
Hummel, F. C., & Cohen, L. G. (2006). Non-invasive brain stimulation: A new strategy to improve neurorehabilitation after stroke? The Lancet Neurology, 5, 708–712. [10.1016/S1474-4422(06)70525-7]
Ramos-Murguialday, A., Broetz, D., Rea, M., et al. (2013). Brain–machine interface in chronic stroke rehabilitation: A controlled study. Annals of Neurology, 74, 100–108. [10.1002/ana.23879]
Laver, K. E., Lange, B., George, S., Deutsch, J. E., Saposnik, G., & Crotty, M. (2017). Virtual reality for stroke rehabilitation. Cochrane Database of Systematic Reviews. [10.1002/14651858.CD008349.pub4]
Owen, A. M., Hampshire, A., Grahn, J. A., et al. (2010). Putting brain training to the test. Nature, 465, 775–778. [10.1038/nature09042]
Simons, D. J., Boot, W. R., Charness, N., et al. (2016). Do “brain-training” programs work? Psychological Science in the Public Interest, 17, 103–186. [10.1177/1529100616661983]
Beaty, R. E., Kenett, Y. N., Christensen, A. P., et al. (2018). Robust prediction of individual creative ability from brain functional connectivity. Proceedings of the National Academy of Sciences USA, 115, 1087–1092. [10.1073/pnas.1713532115]
Park, D. C., & Reuter-Lorenz, P. (2009). The adaptive brain: Aging and neurocognitive scaffolding. Annual Review of Psychology, 60, 173–196. [10.1146/annurev.psych.59.103006.093656]
McEwen, B. S. (2007). Physiology and neurobiology of stress and adaptation: Central role of the brain. Physiological Reviews, 87, 873–904. [10.1152/physrev.00041.2006]
Comments