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Evidence accumulates that the cerebellum's role in the brain is not restricted to motor functions. Rather, cerebellar activity seems to be crucial for a variety of tasks that rely on precise event timing and prediction. Due to its complex structure and importance in communication, human speech requires a particularly precise and predictive coordination of neural processes to be successfully comprehended. Recent studies proposed that the cerebellum is indeed a major contributor to speech processing, but how this contribution is achieved mechanistically remains poorly understood. The current study aimed to reveal a mechanism underlying cortico-cerebellar coordination and demonstrate its speech-specificity. In a reanalysis of magnetoencephalography data, we found that activity in the cerebellum aligned to rhythmic sequences of noise-vocoded speech, irrespective of its intelligibility. We then tested whether these "entrained" responses persist, and how they interact with other brain regions, when a rhythmic stimulus stopped and temporal predictions had to be updated. We found that only intelligible speech produced sustained rhythmic responses in the cerebellum. During this "entrainment echo," but not during rhythmic speech itself, cerebellar activity was coupled with that in the left inferior frontal gyrus, and specifically at rates corresponding to the preceding stimulus rhythm. This finding represents evidence for specific cerebellum-driven temporal predictions in speech processing and their relay to cortical regions. [ABSTRACT FROM AUTHOR]

The purpose of the present study was to examine whether spatial or temporal prediction of the tactile stimulus contributes to tactile sensitivity. To investigate the effect of spatial prediction on tactile sensitivity, electrical stimuli were provided for the digit nerve in one of five fingers, and advanced notice of the stimulating finger was provided before the stimulus in some trials but not in others. There was no significant effect of spatial prediction on the intensity at the perceptual threshold of the digit nerve stimulus. This indicates that spatial prediction of the tactile stimulus does not influence tactile sensitivity. To examine the effect of temporal prediction, an auditory warning cue was provided 0, 1, or 10 s before the electrical stimulus to the digit nerve. The stimulus intensity at the perceptual threshold in the trials with the 1 s warning cue was lower than those with the 0 s warning cue. This indicates that temporal prediction enhances tactile sensitivity. The stimulus intensity at the perceptual threshold in the trials with the 1 s warning cue was lower than those with the 10 s warning cue. This means that the contribution of temporal prediction to the tactile sensitivity is greater as the warning cue is closer to the time of the stimulus. This finding may be explained by a defense mechanism activated when humans predict that a tactile stimulus is coming soon. [ABSTRACT FROM AUTHOR]

Landslide disasters frequently result in significant casualties and property losses, underscoring the critical importance of research on landslide displacement prediction. This paper introduces an approach combining improved empirical mode decomposition (ICEEMDAN) and singular entropy-enhanced singular spectrum analysis (SSA) to predict landslide displacement using a time series short-duration memory network (LSTM). Initially, ICEEMDAN decomposes the landslide displacement time series into trend and periodic terms. SSA is then employed to denoise these components before fitting the trend term with LSTM. Pearson correlation analysis is utilized to identify characteristic factors within the LSTM model, followed by predictions using a multivariate LSTM model. The empirical results from the Baijiabao landslide in the Three Gorges Reservoir area demonstrate that the joint ICEEMDAN-SSA approach, when combined with LSTM modeling, outperforms the separate applications of SSA and ICEEMDAN, as well as other models such as RNN and SVM. Specifically, the ICEEMDAN-SSA-LSTM model achieves an RMSE of 6.472 mm and an MAE of 4.992 mm, which are considerably lower than those of the RNN model (19.945 mm and 15.343 mm, respectively) and the SVM model (16.584 mm and 11.748 mm, respectively). Additionally, the R2 value for the ICEEMDAN-SSA-LSTM model is 97.5%, significantly higher than the RNN model's 72.3% and the SVM model's 92.8%. By summing the predictions of the trend and periodic terms, the cumulative displacement prediction is obtained, indicating the superior accuracy of the ICEEMDAN-SSA-LSTM model. This model provides a new benchmark for precise landslide displacement prediction and contributes valuable insights to related research. [ABSTRACT FROM AUTHOR]

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]

Music, like spoken language, is often characterized by hierarchically organized structure. Previous experiments have shown neural tracking of notes and beats, but little work touches on the more abstract question: how does the brain establish high-level musical structures in real time? We presented Bach chorales to participants (20 females and 9 males) undergoing electroencephalogram (EEG) recording to investigate how the brain tracks musical phrases. We removed the main temporal cues to phrasal structures, so that listeners could only rely on harmonic information to parse a continuous musical stream. Phrasal structures were disrupted by locally or globally reversing the harmonic progression, so that our observations on the original music could be controlled and compared. We first replicated the findings on neural tracking of musical notes and beats, substantiating the positive correlation between musical training and neural tracking. Critically, we discovered a neural signature in the frequency range ~0.1 Hz (modulations of EEG power) that reliably tracks musical phrasal structure. Next, we developed an approach to quantify the phrasal phase precession of the EEG power, revealing that phrase tracking is indeed an operation of active segmentation involving predictive processes. We demonstrate that the brain establishes complex musical structures online over long timescales (>5 s) and actively segments continuous music streams in a manner comparable to language processing. These two neural signatures, phrase tracking and phrasal phase precession, provide new conceptual and technical tools to study the processes underpinning high-level structure building using noninvasive recording techniques. [ABSTRACT FROM AUTHOR]

Predicting the timing of incoming information allows brain to optimize information processing in dynamic environments. However, the effects of temporal predictions on tactile perception are not well established. In this study, two experiments were conducted to determine how temporal predictions interact with conditional probabilities in tactile perceptual processing. In Experiment 1, we explored the range of the interval between preceding ready cues and imperative targets in which temporal prediction effects can be observed. This prediction effect was observed for intervals of 500 and 1,000 ms. In Experiment 2, we investigated the benefits of temporal predictions on tactile perception while manipulating the conditional probability (setting the stimulus onset earlier or later than the predicted moment in short and long intervals). Our results revealed that this effect became stronger as the probability of the stimulus at the predicted time point increased under short-interval conditions. Together, our results show that the difficulty of transferring processing resources increases in temporally dynamic environments, suggesting a greater subjective cost associated with maladaptive responses to temporally uncertain events. [ABSTRACT FROM AUTHOR]

Definitions of syncopation share two characteristics: the presence of a meter or analogous hierarchical rhythmic structure and a displacement or contradiction of that structure. These attributes are translated in terms of a Bayesian theory of syncopation, where the syncopation of a rhythm is inferred based on a hierarchical structure that is, in turn, learned from the ongoing musical stimulus. Several experiments tested its simplest possible implementation, with equally weighted priors associated with different meters and independence of auditory events, which can be decomposed into two terms representing note density and deviation from a metric hierarchy. A computational simulation demonstrated that extant measures of syncopation fall into two distinct factors analogous to the terms in the simple Bayesian model. Next, a series of behavioral experiments found that perceived syncopation is significantly related to both terms, offering support for the general Bayesian construction of syncopation. However, we also found that the prior expectations associated with different metric structures are not equal across meters and that there is an interaction between density and hierarchical deviation, implying that auditory events are not independent from each other. Together, these findings provide evidence that syncopation is a manifestation of a form of temporal expectation that can be directly represented in Bayesian terms and offer a complementary, feature‐driven approach to recent Bayesian models of temporal prediction. [ABSTRACT FROM AUTHOR]

Rhythmic entrainment echoes—rhythmic brain responses that outlast rhythmic stimulation—can demonstrate endogenous neural oscillations entrained by the stimulus rhythm. Here, we tested for such echoes in auditory perception. Participants detected a pure tone target, presented at a variable delay after another pure tone that was rhythmically modulated in amplitude. In four experiments involving 154 human (female and male) participants, we tested (1) which stimulus rate produces the strongest entrainment echo and, inspired by the tonotopical organization of the auditory system and findings in nonhuman primates, (2) whether these are organized according to sound frequency. We found the strongest entrainment echoes after 6 and 8 Hz stimulation, respectively. The best moments for target detection (in phase or antiphase with the preceding rhythm) depended on whether sound frequencies of entraining and target stimuli matched, which is in line with a tonotopical organization. However, for the same experimental condition, best moments were not always consistent across experiments. We provide a speculative explanation for these differences that relies on the notion that neural entrainment and repetition-related adaptation might exercise competing opposite influences on perception. Together, we find rhythmic echoes in auditory perception that seem more complex than those predicted from initial theories of neural entrainment. [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]

This paper proposes an algorithm for transmitting and reconstructing the estimated point cloud by temporally estimating a dynamic point cloud sequence. When a non-rigid 3D point cloud sequence (PCS) is input, the sequence is divided into groups of point cloud frames (PCFs), and a key PCF is selected. The 3D skeleton is predicted through 3D pose estimation, and the motion of the skeleton is estimated by analyzing the joints and bones of the 3D skeleton. For the deformation of the non-rigid human PC, the 3D PC model is transformed into a mesh model, and the key PCF is rigged using the 3D skeleton. After deforming the key PCF into the target PCF utilizing the motion vector of the estimated skeleton, the residual PC between the motion compensation PCF and the target PCF is generated. If there is a key PCF, the motion vector of the target PCF, and a residual PC, the target PCF can be reconstructed. Just as compression is performed using pixel correlation between frames in a 2D video, this paper compresses 3D PCFs by estimating the non-rigid 3D motion of a 3D object in a 3D PC. The proposed algorithm can be regarded as an extension of the 2D motion estimation of a rigid local region in a 2D plane to the 3D motion estimation of a non-rigid object (human) in 3D space. Experimental results show that the proposed method can successfully compress 3D PC sequences. If it is used together with a PC compression technique such as MPEG PCC (point cloud compression) in the future, a system with high compression efficiency may be configured. [ABSTRACT FROM AUTHOR]

Both the cerebellum and basal ganglia are involved in rhythm processing, but their specific roles remain unclear. During rhythm perception, these areas may be processing purely sensory information, or they may be involved in motor preparation, as periodic stimuli often induce synchronized movements. Previous studies have shown that neurons in the cerebellar dentate nucleus and the caudate nucleus exhibit periodic activity when the animals prepare to respond to the random omission of regularly repeated visual stimuli. To detect stimulus omission, the animals need to learn the stimulus tempo and predict the timing of the next stimulus. The present study demonstrates that neuronal activity in the cerebellum is modulated by the location of the repeated stimulus and that in the striatum (STR) by the direction of planned movement. However, in both brain regions, neuronal activity during movement and the effect of electrical stimulation immediately before stimulus omission were largely dependent on the direction of movement. These results suggest that, during rhythm processing, the cerebellum is involved in multiple stages from sensory prediction to motor control, while the STR consistently plays a role in motor preparation. Thus, internalized rhythms without movement are maintained as periodic neuronal activity, with the cerebellum and STR preferring sensory and motor representations, respectively. [ABSTRACT FROM AUTHOR]

For the prediction of landslide-generated waves, previous studies have developed numerous empirical equations to express the maximums of wave characteristics as functions of slide parameters upon impact. In this study, we built the temporal relationship between the wave characteristics and slide features. We gave specific insights into impulse waves generated by snow avalanches and mimicked them using a buoyant material called Carbopol whose density is close to that of water. Using the particle image velocimetry (PIV) technique, the slide's temporal velocity field and thickness, as well as the temporal free water surface fluctuation, were determined experimentally. Using a statistical method denoted as panel data analysis, we quantified the temporal wave amplitude from the time series data of the thickness and depth-averaged velocity of the sliding mass at the shoreline. Then, the slide's temporal thickness and velocity at the shoreline were estimated from the parameters of the stationary slide at the initial position, based on the viscoplastic theory. Combining the panel data analysis and the viscoplastic theory, the temporal wave amplitudes were estimated from the initial slide parameters. In the end, we validated the proposed theoretical–statistical combined predictive method with the support of experimental data. [ABSTRACT FROM AUTHOR]

Data-driven Remaining Useful Life (RUL) prediction is one of the core technologies of Prognostics and Health Management (PHM). Committed to improving the accuracy of RUL prediction for aero-engines, this paper proposes a model that is entirely based on the attention mechanism. The attention model is divided into the multi-head self-attention and timing feature enhancement attention models. The multi-head self-attention model employs scaled dot-product attention to extract dependencies between time series; the timing feature enhancement attention model is used to accelerate and enhance the feature selection process. This paper utilises Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) turbofan engine simulation data obtained from NASA Ames' Prognostics Center of Excellence and compares the proposed algorithm to other models. The experiments conducted validate the superiority of our model's approach. [ABSTRACT FROM AUTHOR]

One hypothesis for why humans enjoy musical rhythms relates to their prediction of when each beat should occur. The ability to predict the timing of an event is important from an evolutionary perspective. Therefore, our brains have evolved internal mechanisms for processing the progression of time. However, due to inherent noise in neural signals, this prediction is not always accurate. Theoretical considerations of optimal estimates suggest the occurrence of certain systematic errors made by the brain when estimating the timing of beats in rhythms. Here, we tested psychophysically whether these systematic errors exist and if so, how they depend on stimulus parameters. Our experimental data revealed two main types of systematic errors. First, observers perceived the time of the last beat of a rhythmic pattern as happening earlier than actual when the inter-beat interval was short. Second, the perceived time of the last beat was later than the actual when the inter-beat interval was long. The magnitude of these systematic errors fell as the number of beats increased. However, with many beats, the errors due to long inter-beat intervals became more apparent. We propose a Bayesian model for these systematic errors. The model fits these data well, allowing us to offer possible explanations for how these errors occurred. For instance, neural processes possibly contributing to the errors include noisy and temporally asymmetric impulse responses, priors preferring certain time intervals, and better-early-than-late loss functions. We finish this article with brief discussions of both the implications of systematic errors for the appreciation of rhythm and the possible compensation by the brain's motor system during a musical performance. [ABSTRACT FROM AUTHOR]

A regular rhythmic stimulation increases people's ability to anticipate future events in time and to move their body in space. Temporal concepts are usually prescribed to spatial locations through a past-behind and future-ahead mapping. In this study, we tested the hypothesis that a regular rhythmic stimulation could promote the forward-body (i.e., toward the future) projections in the peri-personal space. In a Visual Approach/Avoidance by the Self Task (VAAST), participants (N = 24) observed a visual scene on the screen (i.e., a music studio with a metronome in the middle). They were exposed to 3 s of auditory isochronous or non-isochronous rhythms, after which they were asked to make as quickly as possible a perceptual judgment on the visual scene (i.e., whether the metronome pendulum was pointing to the right or left). The responses could trigger a forward or backward visual flow, i.e., approaching or moving them away from the scene. Results showed a significant interaction between the rhythmic stimulation and the movement projections (p < 0.001): participants were faster for responses triggering forward-body projections (but not backward-body projections) after the exposure to isochronous (but not non-isochronous) rhythm. By highlighting the strong link between isochronous rhythms and forward-body projections, these findings support the idea that temporal predictions driven by a regular auditory stimulation are grounded in a perception-action system integrating temporal and spatial information. [ABSTRACT FROM AUTHOR]

Temporal prediction ability is vital for movement synchronization with external rhythmic stimuli (sensorimotor synchronization); however, little is known regarding individual variations in temporal prediction ability and its neural correlates. We determined the underlying neural correlates of temporal prediction and individual variations during auditory-motor synchronization. We hypothesized that the non-primary motor cortices, such as the premotor cortex and supplementary motor area, are the key brain regions that correlate individual variations in prediction ability. Functional magnetic resonance imaging (7T) was performed for 18 healthy volunteers who tapped to 3 types of auditory metronome beats: isochronous, tempo change, and random. The prediction ability was evaluated using prediction/tracking ratios that were computed based on cross-correlations between tap timing and pacing events. Participants with a higher prediction/tracking ratio (i.e. stronger predictive tendency) tapped to metronome beats more accurately and precisely. The prediction/tracking ratio was positively correlated with the activity in the bilateral dorsal premotor cortex (PMd), suggesting that the bilateral PMd explains the individual variation in prediction ability. These results indicate that the PMd is involved in generating a model for temporal prediction of auditory rhythm patterns and its activity would reflect model accuracy, which is critical for accurate and precise sensorimotor synchronization. [ABSTRACT FROM AUTHOR]

Prior research on the relation between empathy and burnout in social professions appears to be inconsistent. Whereas some studies have claimed that empathy functions as a buffer against burnout, others have argued that empathy contributes to the development of burnout. One explanation for this disparity may be methodological as most studies have relied on cross-sectional single-measure designs. To examine their cross-sectional and longitudinal associations, the present study analyzed cross-lagged data of several measures of empathy and burnout. Data from two longitudinal studies with three to twelve months between measurements were analyzed. The samples contained 172 nursing students and 186 nurses, respectively. Empathy was assessed using the Interpersonal Reactivity Index, the Toronto Empathy Questionnaire, the Geneva Emotion Recognition Test, and the Social Mindfulness Test Paradigm. Burnout was assessed using the Maslach Burnout Inventory, the Irritation Scale, and the Copenhagen Burnout Inventory. Results showed significant cross-sectional correlations between the questionnaire measures of empathy and burnout. However, there was insufficient support for the cross-lagged associations. Associations between empathy and burnout may be attributed to the methodological limitations of cross-sectional designs. However, the influence of potential moderators is discussed as a promising direction of future research. [ABSTRACT FROM AUTHOR]

Self-generated auditory input is perceived less loudly than the same sounds generated externally. The existence of this phenomenon, called Sensory Attenuation (SA), has been studied for decades and is often explained by motor-based forward models. Recent developments in the research of SA, however, challenge these models. We review the current state of knowledge regarding theoretical implications about the significance of Sensory Attenuation and its role in human behavior and functioning. Focusing on behavioral and electrophysiological results in the auditory domain, we provide an overview of the characteristics and limitations of existing SA paradigms and highlight the problem of isolating SA from other predictive mechanisms. Finally, we explore different hypotheses attempting to explain heterogeneous empirical findings, and the impact of the Predictive Coding Framework in this research area. [ABSTRACT FROM AUTHOR]

Planning forest management relies on predicting insect outbreaks such as mountain pine beetle, particularly in the intermediate‐term future, e.g., 5‐year. Machine‐learning algorithms are potential solutions to this challenging problem due to their many successes across a variety of prediction tasks. However, there are many subtle challenges in applying them: identifying the best learning models and the best subset of available covariates (including time lags) and properly evaluating the models to avoid misleading performance‐measures. We systematically address these issues in predicting the chance of a mountain pine beetle outbreak in the Cypress Hills area and seek models with the best performance at predicting future 1‐, 3‐, 5‐ and 7‐year infestations. We train nine machine‐learning models, including two generalized boosted regression trees (GBM) that predict future 1‐ and 3‐year infestations with 92% and 88% AUC, and two novel mixed models that predict future 5‐ and 7‐year infestations with 86% and 84% AUC, respectively. We also consider forming the train and test datasets by splitting the original dataset randomly rather than using the appropriate year‐based approach and show that this may obtain models that score high on the test dataset but low in practice, resulting in inaccurate performance evaluations. For example, a k‐nearest neighbor model with the actual performance of 68% AUC, scores the misleadingly high 78% on a test dataset obtained from a random split, but the more accurate 66% on a year‐based split. We then investigate how the prediction accuracy varies with respect to the provided history length of the covariates and find that neural network and naive Bayes, predict more accurately as history‐length increases, particularly for future 1‐ and 3‐year predictions, and roughly the same holds with GBM. Our approach is applicable to other invasive species. The resulting predictors can be used in planning forest and pest management and planning sampling locations in field studies. [ABSTRACT FROM AUTHOR]

Prediction of periodic event timing is an important function for everyday activities, while the exact neural mechanism remains unclear. Previous studies in nonhuman primates have demonstrated that neurons in the cerebellar dentate nucleus and those in the caudate nucleus exhibit periodic firing modulation when the animals attempt to detect a single omission of isochronous repetitive audiovisual stimuli. To understand how these subcortical signals are sent and processed through the thalamocortical pathways, we examined single-neuron activities in the central thalamus of two macaque monkeys (one female and one male). We found that three types of neurons responded to each stimulus in the sequence in the absence of movements. Reactive-type neurons showed sensory adaptation and gradually waned the transient response to each stimulus. Predictive-type neurons steadily increased the magnitude of the suppressive response, similar to neurons previously reported in the cerebellum. Switch-type neurons initially showed a transient response, but after several cycles, the direction of firing modulation reversed and the activity decreased for each repetitive stimulus. The time course of Switch-type activity was well explained by the weighted sum of activities of the other types of neurons. Furthermore, for only Switch-type neurons the activity just before stimulus omission significantly correlated with behavioral latency, indicating that this type of neuron may carry a more advanced signal in the system detecting stimulus omission. These results suggest that the central thalamus may transmit integrated signals to the cerebral cortex for temporal information processing, which are necessary to accurately predict rhythmic event timing. [ABSTRACT FROM AUTHOR]

WE EXPLORE THE EFFECTS OF TRAINED MUSICAL movements on sensorimotor interactions in order to clarify the interpretation of previously observed expertise differences. Pianists and non-pianists listened to an auditory sequence and identified whether the final event occurred in time with the sequence. In half the trials participants listened without moving, and in half they synchronized keystrokes while listening. Pianists and non-pianists were better able to identify the timing of the final tone after synchronizing keystrokes compared to listening only. Curiously, this effect of movement did not differ between pianists and non-pianists despite substantial training differences with respect to finger movements. We also found few group differences in the ability to align keystrokes with events in the auditory sequence; however, movements were less variable (lower coefficient of variation) in pianists compared to nonpianists. Consistent with the idea that the benefits of synchronization on rhythm perception are constrained by motor effector kinematics, this work helps clarify previous findings in this paradigm. We discuss these outcomes in light of training and the kinematics involved in pianist keystrokes compared to musicians synchronizing movements in other studies. We also overview how these differences across motor effector synchronization and training must be accounted for in models of perception and action. [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]

The transmission and storage of weather radar products will be an important problem for future weather radar applications. The aim of this work is to provide a solution for real-time transmission of weather radar data and efficient data storage. By upgrading the capabilities of radar, the amount of data that can be processed continues to increase. Weather radar compression is necessary to reduce the amount of data for transmission and archiving. The characteristics of weather radar data are not considered in general-purpose compression programs. The sparsity and data redundancy of weather radar data are analyzed. A lossless compression of weather radar data based on prediction coding is presented, which is called spatial and temporal prediction compression (STPC). The spatial and temporal correlations in weather radar data are utilized to improve the compression ratio. A specific prediction scheme for weather radar data is given, while the residual data and motion vectors are used to replace the original values for entropy coding. After this, the Level-II product from CINRAD SA is used to evaluate STPC. Experimental results show that the STPC achieves a better performance than the general-purpose compression programs, with the STPC yield being approximately 26% better than the next best approach. [ABSTRACT FROM AUTHOR]

Recent advances in neuroscience have brought a great focus on how the auditory cortex tracks speech at certain time scales corresponding to pre-lexical speech units in order to achieve comprehension. In particular, it has been claimed that it is the syllabic rhythm to which slow neural oscillations in the auditory cortex entrain in order to chunk the speech stream into smaller informational units. However, the terms “syllable” and “rhythm” have been treated quite loosely in the current literature. We revisit classic approaches to show that both concepts do not necessarily have an acoustic or phonetic counterpart, which could be directly extracted by neural processes. We would like to suggest that the syllabic rhythm could emerge at the intersection of acoustic–phonetic and motor knowledge of speech. We furthermore propose that nesting of cortical oscillations might be the key mechanism to understand the timing constraints that lead to the emergence of the syllable. [ABSTRACT FROM PUBLISHER]

Music can be thought of as a dynamic path over time. In most cases, the rhythmic structure of this path, such as specific sequences of strong and weak beats or recurring patterns, allows us to predict what and particularly when sounds are going to happen. Without this ability we would not be able to entrain body movements to music, like we do when we dance. By combining EEG and behavioral measures, the current study provides evidence illustrating the importance of ongoing neural oscillations at beat-related frequencies--i.e., neural entrainment--for tracking and predicting musical rhythms. Participants (13 musicians and 13 non-musicians) listened to drum rhythms that switched from a quadruple rhythm to a 3-over-4 polyrhythm. After a silent period of ~2-3 s, participants had to decide whether a target stimulus was presented on time with the triple beat of the polyrhythm, too early, or too late. Results showed that neural oscillations reflected the rhythmic structure of both the simple quadruple rhythm and the more complex polyrhythm with no differences between musicians and non-musicians. During silent periods, the observation of time-frequency plots and more commonly used frequency spectra analyses suggest that beat-related neural oscillations were more pronounced in musicians compared to non-musicians. Neural oscillations during silent periods are not driven by an external input and therefore are thought to reflect top-down controlled endogenous neural entrainment. The functional relevance of endogenous neural entrainment was demonstrated by a positive correlation between the amplitude of task-relevant neural oscillations during silent periods and the number of correctly identified target stimuli. In sum, our findings add to the evidence supporting the neural resonance theory of pulse and meter. Furthermore, they indicate that beat-related topdown controlled neural oscillations can exist without external stimulation and suggest that those endogenous oscillations are strengthened by musical expertise. Finally, this study shows that the analysis of neural oscillations can be a useful tool to assess how we perceive and process complex auditory stimuli such as polyrhythms. [ABSTRACT FROM AUTHOR]

Moving to a speech rhythm can enhance verbal processing in the listener by increasing temporal expectancies (Falk and Dalla Bella, 2016). Here we tested whether this hypothesis holds for prosodically diverse languages such as German (a lexical stress-language) and French (a non-stress language). Moreover, we examined the relation between motor performance and the benefits for verbal processing as a function of language. Sixty-four participants, 32 German and 32 French native speakers detected subtle word changes in accented positions in metrically structured sentences to which they previously tapped with their index finger. Before each sentence, they were cued by a metronome to tap either congruently (i.e., to accented syllables) or incongruently (i.e., to non-accented parts) to the following speech stimulus. Both French and German speakers detected words better when cued to tap congruently compared to incongruent tapping. Detection performance was predicted by participants' motor performance in the non-verbal cueing phase. Moreover, tapping rate while participants tapped to speech predicted detection differently for the two language groups, in particular in the incongruent tapping condition. We discuss our findings in light of the rhythmic differences of both languages and with respect to recent theories of expectancy-driven and multisensory speech processing. [ABSTRACT FROM AUTHOR]

The present study aimed to investigate whether or not the evaluative processing of action feedback can be modulated by temporal prediction. For this purpose, we examined the effects of the predictability of the timing of action feedback on an ERP effect that indexed the evaluative processing of action feedback, that is, an ERP effect that has been interpreted as a feedback-related negativity (FRN) elicited by 'bad' action feedback or a reward positivity (RewP) elicited by 'good' action feedback. In two types of experimental blocks, the participants performed a gambling task in which they chose one of two cards and received an action feedback that indicated monetary gain or loss. In fixed blocks, the time interval between the participant's choice and the onset of the action feedback was fixed at 0, 500, or 1,000 ms in separate blocks; thus, the timing of action feedback was predictable. In mixed blocks, the time interval was randomly chosen from the same three intervals with equal probability; thus, the timing was less predictable. The results showed that the FRN/RewP was smaller in mixed than fixed blocks for the 0-ms interval trial, whereas there was no difference between the two block types for the 500-ms and 1,000-ms interval trials. Interestingly, the smaller FRN/RewP was due to the modulation of gain ERPs rather than loss ERPs. These results suggest that temporal prediction can modulate the evaluative processing of action feedback, and particularly good feedback, such as that which indicates monetary gain. [ABSTRACT FROM AUTHOR]

Knowing when an event is likely to occur allows attentional resources to be oriented toward that moment in time, enhancing processing of the event. We previously found that children (mean age 11 years) are unable to use endogenous temporal cues to orient attention in time, despite being able to use endogenous spatial cues (arrows) to orient attention in space. Arrow cues, however, may have proved beneficial by engaging exogenous (automatic), as well as endogenous (voluntary), orienting mechanisms. We therefore conducted two studies in which the exogenous properties of visual temporal cues were increased, to examine whether this helped children orient their attention in time. In the first study, the location of an imperative target was predicted by the direction of a left or right spatial arrow cue while its onset was predicted by the relative duration of a short or long temporal cue. To minimize the influence of rhythmic entrainment in the temporal condition, the foreperiod (500 ms/1100 ms) was deliberately chosen so as not to precisely match the duration of the temporal cue (100 ms/400 ms). Targets appeared either at cued locations/onset times (valid trials) or at unexpected locations/onset times (invalid trials). Adults' response times were significantly slower for invalid versus valid trials, in both spatial and temporal domains. Despite being slowed by invalid spatial cues, children (mean age 10.7 years) were unperturbed by invalid temporal cues, suggesting that these duration-based temporal cues did not help them orient attention in time. In the second study, we enhanced the exogenous properties of temporal cues further, by presenting multiple temporal cues in an isochronous (rhythmic) sequence. Again, to minimize automatic entrainment, target onset did not match the isochronous interval. Children (mean age 11.4 years), as well as adults, were now significantly slowed by invalid cues in both the temporal and spatial dimension. The sequential, as opposed to single, presentation of temporal cues therefore helped children to orient their attention in time. We suggest that the exogenous properties of sequential presentation provide a temporal scaffold that supports the additional attentional and mnemonic requirements of temporal, as compared to spatial, processing. [ABSTRACT FROM AUTHOR]

Action update, substituting a prepotent behavior with a new action, allows the organism to counteract surprising environmental demands. However, action update fails when the organism is uncertain about when to release the substituting behavior, when it faces temporal uncertainty. Predictive coding states that accurate perception demands minimization of precise prediction errors. Activity of the right anterior insula (rAI) is associated with temporal uncertainty. Therefore, we hypothesize that temporal uncertainty during action update would cause the AI to decrease the sensitivity to ascending prediction errors. Moreover, action update requires response inhibition which recruits the frontostriatal indirect pathway associated with motor control. Therefore, we also hypothesize that temporal estimation errors modulate frontostriatal connections. To test these hypotheses, we collected fMRI data when participants performed an action-update paradigm within the context of temporal estimation. We fit dynamic causal models to the imaging data. Competing models comprised the inferior occipital gyrus (IOG), right supramarginal gyrus (rSMG), rAI, right presupplementary motor area (rPreSMA), and the right striatum (rSTR). The winning model showed that temporal uncertainty drove activity into the rAI and decreased insular sensitivity to ascending prediction errors, as shown by weak connectivity strength of rSMG→rAI connections. Moreover, temporal estimation errors weakened rPreSMA→rSTR connections and also modulated rAI→rSTR connections, causing the disruption of action update. Results provide information about the neurophysiological implementation of the so-called horse race model of action control. We suggest that, contrary to what might be believed, unsuccessful action update could be a homeostatic process that represents a Bayes optimal encoding of uncertainty. [ABSTRACT FROM AUTHOR]

The updating of prospective internal models is necessary to accurately predict future observations. Uncertainty-driven internal model updating has been studied using a variety of perceptual paradigms, and have revealed engagement of frontal and parietal areas. In a distinct literature, studies on temporal expectations have also characterized a time-perception network, which relies on temporal orienting of attention. However, the updating of prospective internal models is highly dependent on temporal attention, since temporal attention must be reoriented according to the current environmental demands. In this study, we used functional magnetic resonance imaging (fMRI) to evaluate to what extend the continuous manipulation of temporal prediction would recruit update-related areas and the time-perception network areas. We developed an exogenous temporal task that combines rhythm cueing and time-to-contact principles to generate implicit temporal expectation. Two patterns of motion were created: periodic (simple harmonic oscillation) and non-periodic (harmonic oscillation with variable acceleration). We found that non-periodic motion engaged the exogenous temporal orienting network, which includes the ventral premotor and inferior parietal cortices, and the cerebellum, as well as the presupplementary motor area, which has previously been implicated in internal model updating, and the motion-sensitive area MT+. Interestingly, we found a right-hemisphere preponderance suggesting the engagement of explicit timing mechanisms. We also show that the periodic motion condition, when compared to the non-periodic motion, activated a particular subset of the default-mode network (DMN) midline areas, including the left dorsomedial prefrontal cortex (DMPFC), anterior cingulate cortex (ACC), and bilateral posterior cingulate cortex/precuneus (PCC/PC). It suggests that the DMN plays a role in processing contextually expected information and supports recent evidence that the DMN may reflect the validation of prospective internal models and predictive control. Taken together, our findings suggest that continuous manipulation of temporal predictions engages representations of temporal prediction as well as task-independent updating of internal models. [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]

Video compression systems use prediction to reduce redundancies present in video sequences along the temporal and spatial dimensions. Standard video coding systems use either temporal or spatial prediction on a per block basis. If temporal prediction is used, spatial information is ignored. If spatial prediction is used, temporal information is ignored. This may be a computationally efficient approach, but it does not effectively combine temporal and spatial information. In this letter, we provide a framework where available temporal and spatial information can be combined effectively to perform joint spatial and temporal prediction in video coding. Experimental results obtained from one sample realization of this framework show its potential. [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 requires temporally precise interpersonal action coordination. To play in synchrony, ensemble musicians presumably rely on anticipatory mechanisms that enable them to predict the timing of sounds produced by co-performers. Previous studies have shown that individuals differ in their ability to predict upcoming tempo changes in paced finger-tapping tasks (indexed by cross-correlations between tap timing and pacing events) and that the degree of such prediction influences the accuracy of sensorimotor synchronization (SMS) and interpersonal coordination in dyadic tapping tasks. The current functional magnetic resonance imaging study investigated the neural correlates of auditory temporal predictions during SMS in a within-subject design. Hemodynamic responses were recorded from 18 musicians while they tapped in synchrony with auditory sequences containing gradual tempo changes under conditions of varying cognitive load (achieved by a simultaneous visual n-back working-memory task comprising three levels of difficulty: observation only, 1-back, and 2-back object comparisons). Prediction ability during SMS decreased with increasing cognitive load. Results of a parametric analysis revealed that the generation of auditory temporal predictions during SMS recruits (1) a distributed network of cortico-cerebellar motor-related brain areas (left dorsal premotor and motor cortex, right lateral cerebellum, SMA proper and bilateral inferior parietal cortex) and (2) medial cortical areas (medial prefrontal cortex, posterior cingulate cortex). While the first network is presumably involved in basic sensory prediction, sensorimotor integration, motor timing, and temporal adaptation, activation in the second set of areas may be related to higher-level social-cognitive processes elicited during action coordination with auditory signals that resemble music performed by human agents. [ABSTRACT FROM AUTHOR]

This paper examines the role of mental imagery in music performance. Self-reports by musicians, and various other sources of anecdotal evidence, suggest that covert auditory, motor, and/or visual imagery facilitate multiple aspects of music performance. The cognitive and motor mechanisms that underlie such imagery include working memory, action simulation, and internal models. Together these mechanisms support the generation of anticipatory images that enable thorough action planning and movement execution that is characterized by efficiency, temporal precision, and biomechanical economy. In ensemble performance, anticipatory imagery may facilitate interpersonal coordination by enhancing online predictions about others' action timing. Overlap in brain regions subserving auditory imagery and temporal prediction is consistent with this view. It is concluded that individual differences in anticipatory imagery may be a source of variation in expressive performance excellence and the quality of ensemble cohesion. Engaging in effortful musical imagery is therefore justified when artistic perfection is the goal. [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]

Predicting changes in software entities (e.g. source files) that are more likely to change can help in the efficient allocation of the project resources. A powerful change prediction tool can improve maintenance and evolution tasks in software projects in terms of cost and time factors. The vast majority of research works have focused on determining "where" the most change-prone entities are, and "how" the change will be propagated through a system. This article suggests that knowing "when" changes are likely to happen can also provide another consideration for managers and developers to plan their maintenance activities more efficiently. To address this issue, a Neural Network-based Temporal Change Prediction (NNTCP) framework is proposed. This novel framework indicates "where" the changes are likely to happen (i.e. hot spots), and then adds the time dimension to predict "when" it may occur. In proving this concept, the NNTCP framework is applied in two large-scale open source software projects, Mozilla and Eclipse. The results obtained indicate NNTCP can predict the occurrence of several future revisions with reasonable performance. [ABSTRACT FROM AUTHOR]