Effects of stimulus and task structure on temporal perceptual learning

Our ability to discriminate temporal intervals can be improved with practice. This learning is generally thought to reflect an enhancement in the representation of a trained interval, which leads to interval-specific improvements in temporal discrimination. In the present study, we asked whether temporal learning is further constrained by context-specific factors dictated through the trained stimulus and task structure. Two groups of participants were trained using a single-interval auditory discrimination task over 5 days. Training intervals were either one of eight predetermined values (FI group), or random from trial to trial (RI group). Before and after the training period, we measured discrimination performance using an untrained two-interval temporal comparison task. Our results revealed a selective improvement in the FI group, but not the RI group. However, this learning did not generalize between the trained and untrained tasks. These results highlight the sensitivity of TPL to stimulus and task structure, suggesting that mechanisms of temporal learning rely on processes beyond changes in interval representation.

Context-specific control over the neural dynamics of temporal attention by the human cerebellum

Physiological methods have identified a number of signatures of temporal prediction, a core component of attention. While the underlying neural dynamics have been linked to activity within cortico-striatal networks, recent work has shown that the behavioral benefits of temporal prediction rely on the cerebellum. Here, we examine the involvement of the human cerebellum in the generation and/or temporal adjustment of anticipatory neural dynamics, measuring scalp electroencephalography in individuals with cerebellar degeneration. When the temporal prediction relied on an interval representation, duration-dependent adjustments were impaired in the cerebellar group compared to matched controls. This impairment was evident in ramping activity, beta-band power, and phase locking of delta-band activity. These same neural adjustments were preserved when the prediction relied on a rhythmic stream. Thus, the cerebellum has a context-specific causal role in the adjustment of anticipatory neural dynamics of temporal prediction, providing the requisite modulation to optimize behavior.

Context-specific control of the neural dynamics of temporal attention by the human cerebellum

SummaryPhysiological methods have identified a number of signatures of temporal prediction, a core component of attention. While the underlying neural dynamics have been linked to activity within cortico-striatal networks, recent work has shown that the behavioral benefits of temporal prediction causally rely on the cerebellum. Here we examine the involvement of the human cerebellum in the generation and/or temporal adjustment of anticipatory neural dynamics, measuring scalp electroencephalography in individuals with cerebellar degeneration. When the temporal prediction relied on an interval representation, duration-dependent adjustments were impaired in the cerebellar group compared to matched controls. This impairment was evident in ramping activity, beta-band power, and phase locking of delta-band activity. Remarkably, these same neural adjustments were preserved when the prediction relied on a rhythmic stream. Thus, the cerebellum has a context-specific causal role in the adjustment of anticipatory neural dynamics of temporal prediction, providing the requisite modulation to optimize behavior.

Probability Bounds Analysis Applied to the Sandia Verification and Validation Challenge Problem

Our approach to the Sandia Verification and Validation Challenge Problem is to use probability bounds analysis (PBA) based on probabilistic representation for aleatory uncertainties and interval representation for (most) epistemic uncertainties. The nondeterministic model predictions thus take the form of p-boxes, or bounding cumulative distribution functions (CDFs) that contain all possible families of CDFs that could exist within the uncertainty bounds. The scarcity of experimental data provides little support for treatment of all uncertain inputs as purely aleatory uncertainties and also precludes significant calibration of the models. We instead seek to estimate the model form uncertainty at conditions where the experimental data are available, then extrapolate this uncertainty to conditions where no data exist. The modified area validation metric (MAVM) is employed to estimate the model form uncertainty which is important because the model involves significant simplifications (both geometric and physical nature) of the true system. The results of verification and validation processes are treated as additional interval-based uncertainties applied to the nondeterministic model predictions based on which the failure prediction is made. Based on the method employed, we estimate the probability of failure to be as large as 0.0034, concluding that the tanks are unsafe.

Temporal Data Management and Processing with Column Oriented NoSQL Databases

This article introduces how temporal data can be maintained and processed by utilizing Column-oriented NoSQL databases (CoNoSQLDBs). Although each column in a CoNoSQLDB can store multiple data versions with their corresponded timestamps, its implicit temporal interval representation can cause wrong or misleading results during temporal query processing. In consequence, the original table representation supported by CoNoSQLDBs is not suitable for storing temporal data. To maintain the temporal data in the CoNoSQLDB tables, two alternative table representations can be adopted, namely, explicit history representation (EHR) and tuple time-stamping representation (TTR) in which each tuple (data version) has an explicit temporal interval. For processing TTR, the temporal relational algebra is extended to TTRO operator model with minor modifications. For processing EHR, a novel temporal operator model called CTO is proposed. Both TTRO and CTO contain eight temporal data operators, namely, Union, Difference, Intersection, Project, Filter, Cartesian product, Theta-Join and Group by with a set of aggregation functions, such as SUM, AVG, MAX and etc. Moreover, the authors implement each temporal operator by utilizing MapReduce framework to indicate which temporal operator model is more suitable for temporal data processing in the context of CoNoSQLDBs.