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Moving in synchrony to external rhythmic stimuli is an elementary function that humans regularly engage in. It is termed “sensorimotor synchronization” and it is governed by two main parameters, the period and the phase of the movement with respect to the external rhythm. There has been an extensive body of research on the characteristics of these parameters, primarily once the movement synchronization has reached a steady-state level. Particular interest has been shown about how these parameters are corrected when there are deviations for the steady-state level. However, little is known about the initial “tuning-in” interval, when one aligns the movement to the external rhythm from rest. The current work investigates this “tuning-in” period for each of the four limbs and makes various novel contributions in the understanding of sensorimotor synchronization. The results suggest that phase and period alignment appear to be separate processes. Phase alignment involves limb-specific somatosensory memory in the order of minutes while period alignment has very limited memory usage. Phase alignment is the primary task but then the brain switches to period alignment where it spends most its resources. In overall this work suggests a central, cognitive role of period alignment and a peripheral, sensorimotor role of phase alignment.
The neural mechanisms that unfold when humans form a large group defined by an overarching context, such as audiences in theater or sports, are largely unknown and unexplored. This is mainly due to the lack of availability of a scalable system that can record the brain activity from a significantly large portion of such an audience simultaneously. Although the technology for such a system has been readily available for a long time, the high cost as well as the large overhead in human resources and logistic planning have prohibited the development of such a system. However, during the recent years reduction in technology costs and size have led to the emergence of low-cost, consumer-oriented EEG systems, developed primarily for recreational use. Here by combining such a low-cost EEG system with other off-the-shelve hardware and tailor-made software, we develop in the lab and test in a cinema such a scalable EEG hyper-scanning system. The system has a robust and stable performance and achieves accurate unambiguous alignment of the recorded data of the different EEG headsets. These characteristics combined with small preparation time and low-cost make it an ideal candidate for recording large portions of audiences.
Research on psychopathy has so far been largely limited to the investigation of high-level processes, such as emotion perception and regulation. In the present work, we investigate whether psychopathy has an effect on the estimation of fundamental physical parameters, which are computed in the brain during early stages of sensory processing. We employed a simple task in which participants had to estimate their interpersonal distance from a moving avatar and stop it at a given distance. The face expression of the avatars were positive, negative, or neutral. Participants carried out the task online on their home computers. We measured the psychopathy level via a self-report questionnaire. Regardless of the degree of psychopathy, the facial expression of the avatars showed no effect on distance estimation. Our results show that individuals with a high degree of psychopathy underestimate distance of approaching avatars significantly less (let the avatar approach them significantly closer) than did participants with a lesser degree of psychopathy. Moreover, participants who scored high in Self-Centered Impulsivity underestimate the distance to approaching avatars significantly less (let the avatar approach closer) than participants with a low score. Distance estimation is considered an automatic process performed at early stages of visual processing. Therefore, our results imply that psychopathy affects basic early sensory processes, such as feature extraction, in the visual cortex.
Temporal anticipation is a fundamental process underlying complex neural functions such as associative learning, decision-making, and motor-preparation. Here we study event anticipation in its simplest form in human participants using magnetoencephalography. We distributed events in time according to different probability density functions and presented the stimuli separately in two different sensory modalities. We found that the temporal dynamics in right parietal cortex correlate with reaction times to anticipated events. Specifically, after an event occurred, event probability was represented in right parietal activity, hinting at a functional role of event-related potential component P300 in temporal expectancy. The results are consistent across both visual and auditory modalities. The right parietal cortex seems to play a central role in the processing of event probability density. Overall, this work contributes to the understanding of the neural processes involved in the anticipation of events in time.
Neural signatures of temporal anticipation in human cortex represent event probability density
(2024)
Temporal prediction is a fundamental function of neural systems. Recent advances suggest that humans anticipate future events by calculating probability density functions, rather than hazard rates. However, direct neural evidence for this mechanism is lacking. We recorded neural activity using magnetoencephalography as participants anticipated auditory and visual events distributed in time. We show that temporal anticipation, measured as reaction times, approximates the event probability density function, but not hazard rate. Temporal anticipation manifests as spatiotemporally patterned activity in three anatomically and functionally distinct parieto-temporal and sensorimotor cortical areas. In both audition and vision, each of these areas revealed a marked neural signature of anticipation: Prior to sensory cues, activity in a specific frequency band of neural oscillations, spanning alpha and beta ranges, encodes the event probability density function. Strikingly, these neural signals predicted reaction times to imminent sensory cues. These results show that supra-modal representations of probability density across cortex underlie the anticipation of future events.
