Frequency-specific oscillations and phase-coupling of neuronal populations have been proposed as an essential mechanism for the coordination of activity between brain areas during cognitive tasks. To provide an effective substrate for cognitive function, we reasoned that ongoing functional brain networks should also be able to reorganise and coordinate in a similar manner. To test this hypothesis, we use a novel method for identifying repeating patterns of large-scale phase-coupling network dynamics, and show that resting networks in magnetoencephalography are well characterised by visits to shortlived transient brain states, with spatially distinct power and phase-coupling in specific frequency bands. Brain states were identified for sensory, motor networks and higher-order cognitive networks; the latter include a posterior higher-order cognitive network in the alpha range (8-12Hz) and an anterior higher-order cognitive network in the delta/theta range (1-7Hz). Both higher-order cognitive networks exhibit especially high power and coherence, and contain brain areas corresponding to posterior and anterior subdivisions of the default mode network. Our results show that large-scale cortical phase-coupling networks operate in very specific frequency bands, possibly reflecting functional specialisation at different intrinsic timescales. 2015). However, the evidence for frequency specific phase-coupling in spontaneous activity at timescales associated with fast cognition is limited.Here, we propose that cortical activity at rest can be described by transient, intermittently reoccurring events in which large-scale networks activate with distinct spectral and phasecoupling features. To identify the possible presence of these events, we use a new analysis approach based on the Hidden Markov Model (HMM; Rabiner, 1989). For the first time, this allows for the identification of brain-wide networks (or brain states) characterised by specific patterns of power and phase-coupling connectivity, which, crucially, are spectrallyresolved (i.e. power and phase-coupling are defined as a function of frequency). These patterns are also temporally-resolved, meaning that the method provides a probabilistic estimation of when the different networks are active (see Fig. 1a). Notably, applying this approach to resting MEG recordings of healthy human subjects revealed the distinct temporal and spectral properties of anterior versus posterior regions of the default mode network. The joint description of the spectral, temporal and spatial properties of ongoing neuronal activity provides new insight into the large-scale circuit organization of the brain (Woolrich and Stephan, 2013).

This chapter takes attention into the fourth dimension by considering research that explores how predictive information in the temporal structure of events can contribute to optimizing perception. The authors review behavioural and neural findings from three lines of investigation in which the temporal regularity and predictability of events are manipulated through rhythms, hazard functions, and cues. The findings highlight the fundamental role temporal expectations play in shaping several aspects of performance, from early perceptual analysis to motor preparation. They also reveal modulation of neural activity by temporal expectations all across the brain. General principles of how temporal expectations are generated and bias information processing are still emerging. The picture so far suggests that there may be multiple sources of temporal expectation, which can bias multiple stages of stimulus analysis depending on the stages of information processing that are critical for task performance. Neural oscillations are likely to provide an important medium through which the anticipated timing of events can regulate neuronal excitability.

Flexible behavior requires guidance not only by sensations that are available immediately but also by relevant mental contents carried forward through working memory. Therefore, selective-attention functions that modulate the contents of working memory to guide behavior (inside-out) are just as important as those operating on sensory signals to generate internal contents (outside-in). We review the burgeoning literature on selective attention in the inside-out direction and underscore its functional, flexible, and future-focused nature. We discuss in turn the purpose (why), targets (what), sources (when), and mechanisms (how) of selective attention inside working memory, using visual working memory as a model. We show how the study of internal selective attention brings new insights concerning the core cognitive processes of attention and working memory and how considering selective attention and working memory together paves the way for a rich and integrated understanding of how mind serves behavior. Expected final online publication date for the Annual Review of Psychology, Volume 74 is January 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.