Alpha-band (8-14 Hz) oscillatory EEG activity was examined with high-density scalp electrical recording during the cue-stimulus interval of an endogenous spatial cueing paradigm. In different blocks, cued spatial locations (left or right) were in either the upper or lower visual field, and attended stimuli were either oriented Ts or moving dots. Distractor stimuli were equally likely in the uncued hemifield. Sustained focal increases of alpha-band activity were seen over occipital cortex contralateral to the direction of the to-be-ignored location (ipsilateral to the cued direction of attention) before onset of the to-be-attended stimulus. The focus of alpha-band activity also moved depending on whether cued locations were in the upper or lower field. Results are consistent with active gating of uncued spatial locations.

Previous studies have suggested the relation of particular frequency bands such as theta (4 -8 Hz), alpha (8 -14 Hz), beta (14 -30 Hz), or gamma (Ͼ30 Hz) to cognitive functions. However, there has been controversy over which bands are specifically related to attention. We used the attention network test to separate three anatomically defined brain networks that carry out the functions of alerting, orienting, and executive control of attention. High-density scalp electrical recording was performed to record synchronous oscillatory activity and power spectrum analyses based on functional magnetic resonance imaging constrained dipole modeling were conducted for each attentional network. We found that each attentional network has a distinct set of oscillations related to its activity. The alerting network showed a specific decrease in theta-, alpha-, and beta-band activity 200 -450 ms after a warning signal. The orienting network showed an increase in gamma-band activity at ϳ200 ms after a spatial cue, indicating the location of a target. The executive control network revealed a complex pattern when a target was surrounded with incongruent flankers compared with congruent flankers. There was an early (Ͻ400 ms) increase in gamma-band activity, a later (Ͼ400 ms) decrease in beta-and low gamma-band activity after the target onset, and a decrease of all frequency bands before response followed by an increase after the response. These data demonstrate that attention is not related to any single frequency band but that each network has a distinct oscillatory activity and time course.

Prevailing theories of implicit or unaware learning propose a developmental invariance model, with implicit function maturing early in infancy or childhood despite prolonged improvements in explicit or intentional learning and memory systems across childhood. Neuroimaging studies of adult visuomotor sequence learning have associated fronto-striatal brain regions with implicit learning of spatial sequences. Given evidence of continued development in these brain regions during childhood, we compare implicit sequence learning in adults and 7- to 11-year-old children to examine potential developmental differences in the recruitment of fronto-striatal circuitry during implicit learning. Participants performed a standard serial reaction time task. Stimuli alternately followed a fixed 10-step sequence of locations or were presented in a pseudorandom order of locations. Adults outperformed children, achieving a significantly larger sequence learning effect and showing learning more quickly than children. Age-related differences in activity were observed in the premotor cortex, putamen, hippocampus, inferotemporal cortex, and parietal cortex. We observed differential recruitment of cortical and subcortical motor systems between groups, presumably reflecting age differences in motor response execution. Adults showed greater hippocampal activity for sequence trials, whereas children demonstrated greater signal during random trials. Activity in the right caudate correlated significantly with behavioral measures of implicit learning for both age groups, although adults showed greater signal change than children overall, as would be expected given developmental differences in sequence learning magnitude. These results challenge the idea of developmental invariance in implicit learning and instead support a view of parallel developments in implicit and explicit learning systems.

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