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      Motor origin of temporal predictions in auditory attention

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          Abstract

          <p id="d12969764e153">How the motor system participates in auditory perception is unknown. In a magnetoencephalography experiment involving auditory temporal attention, we show that the left sensorimotor cortex encodes temporal predictions, which drive the precise temporal anticipation of forthcoming sensory inputs. This encoding is associated with bursts of beta (18–24 Hz) neural oscillations that are directed toward auditory regions. Our data also show that the production of overt movements improves the quality of temporal predictions and augments auditory task performance. These behavioral changes are associated with increased signaling of temporal predictions in right-lateralized frontoparietal associative regions. This study points at a covert form of auditory active sensing, and emphasizes the fundamental role of motor brain areas and actual motor behavior in sensory processing. </p><p class="first" id="d12969764e156">In behavior, action and perception are inherently interdependent. However, the actual mechanistic contributions of the motor system to sensory processing are unknown. We present neurophysiological evidence that the motor system is involved in predictive timing, a brain function that aligns temporal fluctuations of attention with the timing of events in a task-relevant stream, thus facilitating sensory selection and optimizing behavior. In a magnetoencephalography experiment involving auditory temporal attention, participants had to disentangle two streams of sound on the unique basis of endogenous temporal cues. We show that temporal predictions are encoded by interdependent delta and beta neural oscillations originating from the left sensorimotor cortex, and directed toward auditory regions. We also found that overt rhythmic movements improved the quality of temporal predictions and sharpened the temporal selection of relevant auditory information. This latter behavioral and functional benefit was associated with increased signaling of temporal predictions in right-lateralized frontoparietal associative regions. In sum, this study points at a covert form of auditory active sensing. Our results emphasize the key role of motor brain areas in providing contextual temporal information to sensory regions, driving perceptual and behavioral selection. </p>

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          Most cited references35

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          What makes us tick? Functional and neural mechanisms of interval timing.

          Time is a fundamental dimension of life. It is crucial for decisions about quantity, speed of movement and rate of return, as well as for motor control in walking, speech, playing or appreciating music, and participating in sports. Traditionally, the way in which time is perceived, represented and estimated has been explained using a pacemaker-accumulator model that is not only straightforward, but also surprisingly powerful in explaining behavioural and biological data. However, recent advances have challenged this traditional view. It is now proposed that the brain represents time in a distributed manner and tells the time by detecting the coincidental activation of different neural populations.
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            Measuring phase synchrony in brain signals

            This article presents, for the first time, a practical method for the direct quantification of frequency‐specific synchronization (i.e., transient phase‐locking) between two neuroelectric signals. The motivation for its development is to be able to examine the role of neural synchronies as a putative mechanism for long‐range neural integration during cognitive tasks. The method, called phase‐locking statistics (PLS), measures the significance of the phase covariance between two signals with a reasonable time‐resolution (<100 ms). Unlike the more traditional method of spectral coherence, PLS separates the phase and amplitude components and can be directly interpreted in the framework of neural integration. To validate synchrony values against background fluctuations, PLS uses surrogate data and thus makes no a priori assumptions on the nature of the experimental data. We also apply PLS to investigate intracortical recordings from an epileptic patient performing a visual discrimination task. We find large‐scale synchronies in the gamma band (45 Hz), e.g., between hippocampus and frontal gyrus, and local synchronies, within a limbic region, a few cm apart. We argue that whereas long‐scale effects do reflect cognitive processing, short‐scale synchronies are likely to be due to volume conduction. We discuss ways to separate such conduction effects from true signal synchrony. Hum Brain Mapping 8:194–208, 1999. © 1999 Wiley‐Liss, Inc.
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              Sensorimotor synchronization: a review of recent research (2006-2012).

              Sensorimotor synchronization (SMS) is the coordination of rhythmic movement with an external rhythm, ranging from finger tapping in time with a metronome to musical ensemble performance. An earlier review (Repp, 2005) covered tapping studies; two additional reviews (Repp, 2006a, b) focused on music performance and on rate limits of SMS, respectively. The present article supplements and extends these earlier reviews by surveying more recent research in what appears to be a burgeoning field. The article comprises four parts, dealing with (1) conventional tapping studies, (2) other forms of moving in synchrony with external rhythms (including dance and nonhuman animals' synchronization abilities), (3) interpersonal synchronization (including musical ensemble performance), and (4) the neuroscience of SMS. It is evident that much new knowledge about SMS has been acquired in the last 7 years.

                Author and article information

                Journal
                Proceedings of the National Academy of Sciences
                Proc Natl Acad Sci USA
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                October 17 2017
                October 17 2017
                : 114
                : 42
                : E8913-E8921
                Article
                10.1073/pnas.1705373114
                5651745
                28973923
                2454f485-cb6f-4ece-a57f-9a74f6f3c0b7
                © 2017

                http://www.pnas.org/site/misc/userlicense.xhtml

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