Adaptation and learning: Characteristic time scales of performance dynamics

Newell, Karl M.; Mayer‐Kress, Gottfried; Hong, S. Lee; Liu, Yeou Teh

doi:10.1016/j.humov.2009.07.001

Cited by 59 publications

(69 citation statements)

References 45 publications

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“…This segregation of connectivity structure manifested consistently over the scale of days, hours, and minutes, suggesting that community structure provides a generalizable framework to study the evolution of temporally distinct phenomena (12). However, it is also notable that connectivity at the shortest temporal scale displayed higher variability, perhaps reflecting the necessity for dynamic modulation of human brain function over relatively short intervals during learning (19). In light of historically strict definitions of cognitive modules as completely encapsulated structures (38), it is important to emphasize that the modules that we have uncovered remain integrated with one another by a complex pattern of weak interconnections.…”

Section: Discussionmentioning

confidence: 90%

“…We investigated network organization over multiple temporal scales-over days, hours, and minutes -during motor learning (18,19) (Fig. 1B).…”

Section: Resultsmentioning

confidence: 99%

“…More specifically, based on several studies relating the neural basis of modularity to the development of skilled movements (15-17), we expected that functional brain networks derived from acquisition of a simple motor skill would display modular structure over the variety of temporal scales associated with learning (18). We also hypothesized that modular structure would change dynamically during learning (4,19), and that characteristics of such dynamics would be associated with learning success.We tested these predictions using fMRI, an indirect measure of local neuronal activity (20), in healthy adult subjects during the acquisition of a simple motor learning skill composed of visually cued finger sequences. We derived low frequency (0.06-0.12 Hz) functional networks from the fMRI data by computing the temporal correlation between activity in each pair of brain regions to construct weighted graphs or whole-brain functional networks (21-23) (Fig.…”

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confidence: 99%

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confidence: 99%

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Dynamic reconfiguration of human brain networks during learning

Bassett

Wymbs

Porter

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

1,531

1,765

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Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes-flexibility and selection-must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we investigate the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.complex network | time-dependent network | fMRI | motor learning | community structure T he brain is a complex system, composed of many interacting parts, which dynamically adapts to a continually changing environment over multiple temporal scales. Over relatively short temporal scales, rapid adaptation and continuous evolution of those interactions or connections form the neurophysiological basis for behavioral adaptation or learning. At small spatial scales, stable neurophysiological signatures of learning have been best demonstrated in animal systems at the level of individual synapses between neurons (1-3). At a larger spatial scale, it is also well-known that specific regional changes in brain activity and effective connectivity accompany many forms of learning in humans-including the acquisition of motor skills (4, 5).Learning-associated adaptability is thought to stem from the principle of cortical modularity (6). Modular, or nearly decomposable (7), structures are aggregates of small subsystems (modules) that can perform specific functions without perturbing the remainder of the system. Such structure provides a combination of compartmentalization and redundancy, which reduces the interdependence of components, enhances robustness, and facilitates behavioral adaptation (8, 9). Modular organization also confers evolvability on a system by reducing constraints on change (8,(10)(11)(12). Indeed, a putative relationship between modularity and adaptability in the context of human neuroscience has recently been posited (13,14). To date, however, the existence of modularity in large-scale cortical connectivity during learning has not been tested directly.Based on the aforementioned theoretical and empirical grounds, we hypothesized that the principle of modularity would characterize the fundamental organiz...

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Section: Discussionmentioning

confidence: 90%

“…We investigated network organization over multiple temporal scales-over days, hours, and minutes -during motor learning (18,19) (Fig. 1B).…”

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Dynamic reconfiguration of human brain networks during learning

Bassett

Wymbs

Porter

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

1,531

1,765

View full text Add to dashboard Cite

show abstract

“…The persistent changes that characterize learning are relatively slow, occur over a single practice session, after several days, months or years [2] and improve performance within and even between practice sessions [5].…”

Section: Introductionmentioning

confidence: 99%