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Temporal prediction (TP) influences our perception and cognition. The cerebellum could mediate this multi-level ability in a context-dependent manner. We tested whether a modulation of the cerebellar neural activity, induced by transcranial Direct Current Stimulation (tDCS), changed the TP ability according to the temporal features of the context and the duration of target interval. Fifteen healthy participants received anodal, cathodal, and sham tDCS (15 min × 2 mA intensity) over the right cerebellar hemisphere during a TP task. We recorded reaction times (RTs) to a target during the task in two contextual conditions of temporal anticipation: rhythmic (i.e., interstimulus intervals (ISIs) were constant) and single-interval condition (i.e., the estimation of the timing of the target was based on the prior exposure of the train of stimuli). Two ISIs durations were explored: 600 ms (short trials) and 900 ms (long trials). Cathodal tDCS improved the performance during the TP task (shorter RTs) specifically in the rhythmic condition only for the short trials and in the single-interval condition only for the long trials. Our results suggest that the inhibition of cerebellar activity induced a different improvement in the TP ability according to the temporal features of the context. In the rhythmic context, the cerebellum could integrate the temporal estimation with the anticipatory motor responses critically for the short target interval. In the single-interval context, for the long trials, the cerebellum could play a main role in integrating representation of time interval in memory with the elapsed time providing an accurate temporal prediction. [ABSTRACT FROM AUTHOR]

Representation learning based on temporal knowledge graphs (TKGs) has attracted widespread interest, and temporal knowledge graph embedding (TKGE) expresses time entity and relation tokens and exhibit strong dynamics. Despite the significance of the dynamics and the persistent updates in TKGs, most studies have been devoted to static knowledge graphs. Moreover, previous temporal works ignored the semantic hierarchies observed in knowledge modelling cases, which are common in real-world applications. Inaccurate semantic expressions caused by incomplete projections might not capture complex topological structures very well. To solve this problem, a novel hierarchicaltime-surfaceembedding (HTSE) model is proposed for the representation learning of entities, relations and time. Specifically, a unified relation-oriented hierarchical space aims to distinguish relations at different semantic levels of a hierarchy, and entities can naturally reflect the corresponding hierarchy. Then, a time surface aims to enhance the temporal characteristics, and quadruples are learned through exponential mapping and tangent planes in the time surface. According to extensive experiments, HTSE can achieve remarkable performance on five benchmark datasets, outperforming baseline models for time scope prediction, temporal link prediction and hierarchical relation embedding tasks.Furthermore, the qualitative analysis is used to demonstrate the explainable strategy for hierarchical embeddings and their significance in TKGs. [ABSTRACT FROM AUTHOR]

Interpersonal coordination of movements often involves precise synchronization of action timing, particularly in expert domains such as ensemble music performance. According to the adaptation and anticipation model (ADAM) of sensorimotor synchronization, precise yet flexible interpersonal coordination is supported by reactive error correction mechanisms and anticipatory mechanisms that exploit systematic patterns in stimulus timing to plan future actions. Here, we provide a tutorial introduction to the computational architecture of ADAM and present a series of single- and dual-virtual agent simulations that examine the model parameters that produce ideal synchronization performance in different tempo conditions. In the single-agent simulations, a virtual agent synchronized responses to steady tempo sequence or a sequence containing gradual tempo changes. Parameters controlling basic reactive error (phase) correction were sufficient for producing ideal synchronization performance at the steady tempo, whereas parameters controlling anticipatory mechanisms were necessary for ideal performance with a tempo-changing sequence. In the dual-agent simulations, two interacting virtual agents produced temporal sequences from either congruent or incongruent internal performance templates specifying a steady tempo or tempo changes. Ideal performance was achieved with reactive error correction alone when both agents implemented the same performance template (either steady tempo or tempo change). In contrast, anticipatory mechanisms played a key role when one agent implemented a steady tempo template and the other agent implemented a tempo change template. These findings have implications for understanding the interplay between reactive and anticipatory mechanisms when agents possess compatible versus incompatible representations of task goals during human–human and human–machine interaction. [ABSTRACT FROM AUTHOR]

In sensor networks, the periodically aggregated data often exhibit high temporal coherency. Huge energy consumption incurred in transmitting these redundant information results in network disconnection thereby leading to service disruption. In order to effectively manage the energy consumption in concurrent data gathering rounds, temporal data prediction model is proposed. The proposed model provides near accurate predictions that successfully restricts redundant transmissions. The communication energy conserved owing to successful predictions helps to increase the number of data cycles considerably. In addition, an energy prediction-based cluster head rotation algorithm is also presented for load balancing within clusters. Experimental outcomes show that the proposed prediction model significantly improves energy conservation by providing successful predictions per data gathering cycle. Results reveal lower magnitude of prediction error as compared to certain existing prediction methods. [ABSTRACT FROM AUTHOR]

The attentional blink (AB) refers to a deficit in reporting the second of two sequentially presented targets when they are separated by less than 500 ms. Two decades of research has suggested that the AB is a robust phenomenon that is likely attributable to a fundamental limit in sequential object processing. This assumption, however, has recently been undermined by a demonstration that the AB can be eliminated after only a few hundred training trials (Choi, Chang, Shibata, Sasaki, & Watanabe in Proceedings of the National Academy of Sciences 109:12242-12247, ). In the present work, we examined whether this training benefited performance directly, by eliminating processing limitations as claimed, or indirectly, by creating expectations about when targets would appear. Consistent with the latter option, when temporal expectations were reduced, training-related improvements declined significantly. This suggests that whereas training may ameliorate the AB indirectly, the processing limits evidenced in the AB cannot be directly eliminated by brief exposure to the task. [ABSTRACT FROM AUTHOR]

Musical ensemble performance is a form of joint action that requires highly precise yet flexible interpersonal action coordination. To maintain synchrony during expressive passages that contain tempo variations, musicians presumably anticipate the sounds that will be produced by their co-performers. Our previous studies revealed that individuals differ in their ability to predict upcoming event timing when finger tapping in synchrony with tempo-changing pacing signals (i.e., the degree to which inter-tap intervals match vs. lag behind inter-onset intervals in the pacing signal varies between individuals). The current study examines the influence of these individual differences on synchronization performance in a dyadic tapping task. In addition, the stability of individual prediction tendencies across time is tested. Individuals with high or low prediction tendencies were invited to participate in two experimental sessions. In both sessions, participants were asked (1) to tap alone with a tempo-changing pacing signal and (2) to tap synchronously in dyads comprising individuals with similar or different prediction tendencies. Results indicated that individual differences in prediction tendencies were stable over several months and played a significant role in dyadic synchronization. Dyads composed of two high-predicting individuals tapped with higher accuracy and less variability than low-predicting dyads, while mixed dyads were intermediate. Prediction tendencies explained variance in dyadic synchronization performance over and above individual synchronization ability. These findings suggest that individual differences in temporal prediction ability may potentially mediate the interaction of cognitive, motor, and social processes underlying musical joint action. [ABSTRACT FROM AUTHOR]

We all have a sense of time. Yet, there are no sensory receptors specifically dedicated for perceiving time. It is an almost uniquely intangible sensation: we cannot see time in the way that we see color, shape, or even location. So how is time represented in the brain? We explore the neural substrates of metrical representations of time such as duration estimation (explicit timing) or temporal expectation (implicit timing). Basal ganglia (BG), supplementary motor area, cerebellum, and prefrontal cortex have all been linked to the explicit estimation of duration. However, each region may have a functionally discrete role and will be differentially implicated depending upon task context. Among these, the dorsal striatum of the BG and, more specifically, its ascending nigrostriatal dopaminergic pathway seems to be the most crucial of these regions, as shown by converging functional neuroimaging, neuropsychological, and psychopharmacological investigations in humans, as well as lesion and pharmacological studies in animals. Moreover, neuronal firing rates in both striatal and interconnected frontal areas vary as a function of duration, suggesting a neurophysiological mechanism for the representation of time in the brain, with the excitatory-inhibitory balance of interactions among distinct subtypes of striatal neuron serving to fine-tune temporal accuracy and precision. [ABSTRACT FROM AUTHOR]