Internal representations of temporal statistics and feedback calibrate motor-sensory interval timing.

Humans have been shown to adapt to the temporal statistics of timing tasks so as to optimize the accuracy of their responses, in agreement with the predictions of Bayesian integration. This suggests that they build an internal representation of both the experimentally imposed distribution of time intervals (the prior) and of the error (the loss function). The responses of a Bayesian ideal observer depend crucially on these internal representations, which have only been previously studied for simple distributions. To study the nature of these representations we asked subjects to reproduce time intervals drawn from underlying temporal distributions of varying complexity, from uniform to highly skewed or bimodal while also varying the error mapping that determined the performance feedback. Interval reproduction times were affected by both the distribution and feedback, in good agreement with a performance-optimizing Bayesian observer and actor model. Bayesian model comparison highlighted that subjects were integrating the provided feedback and represented the experimental distribution with a smoothed approximation. A nonparametric reconstruction of the subjective priors from the data shows that they are generally in agreement with the true distributions up to third-order moments, but with systematically heavier tails. In particular, higher-order statistical features (kurtosis, multimodality) seem much harder to acquire. Our findings suggest that humans have only minor constraints on learning lower-order statistical properties of unimodal (including peaked and skewed) distributions of time intervals under the guidance of corrective feedback, and that their behavior is well explained by Bayesian decision theory.

Temporal difference models describe higher-order learning in humans.

The ability to use environmental stimuli to predict impending harm is critical for survival. Such predictions should be available as early as they are reliable. In pavlovian conditioning, chains of successively earlier predictors are studied in terms of higher-order relationships, and have inspired computational theories such as temporal difference learning. However, there is at present no adequate neurobiological account of how this learning occurs. Here, in a functional magnetic resonance imaging (fMRI) study of higher-order aversive conditioning, we describe a key computational strategy that humans use to learn predictions about pain. We show that neural activity in the ventral striatum and the anterior insula displays a marked correspondence to the signals for sequential learning predicted by temporal difference models. This result reveals a flexible aversive learning process ideally suited to the changing and uncertain nature of real-world environments. Taken with existing data on reward learning, our results suggest a critical role for the ventral striatum in integrating complex appetitive and aversive predictions to coordinate behaviour.

The internal structure of igniting turbulent sprays as revealed by complex chemistry DNS

A parametric study of spark ignition in a uniform monodisperse turbulent spray is performed with complex chemistry three-dimensional Direct Numerical Simulations in order to improve the understanding of the structure of the ignition kernel. The heat produced by the kernel increases with the amount of fuel evaporated inside the spark volume. Moreover, the heat sink by evaporation is initially higher than the heat release and can have a negative effect on ignition. With the sprays investigated, heat release occurs over a large range of mixture fractions, being high within the nominal flammability limits and finite but low below the lean flammability limit. The burning of very lean regions is attributed to the diffusion of heat and species from regions of high heat release, and from the spark, to lean regions. Two modes of spray ignition are reported. With a relatively dilute spray, nominally flammable material exists only near the droplets. Reaction zones are created locally near the droplets and have a non-premixed character. They spread from droplet to droplet through a very lean interdroplet spacing. With a dense spray, the hot spark region is rich due to substantial evaporation but the cold region remains lean. In between, a large surface of flammable material is generated by evaporation. Ignition occurs there and a large reaction zone propagates from the rich burned region to the cold lean region. This flame is wrinkled due to the stratified mixture fraction field and evaporative cooling. In the dilute spray, the reaction front curvature pdf contains high values associated with single droplet combustion, while in the dense spray, the curvature is lower and closer to the curvature associated with gaseous fuel ignition kernels. © 2011 The Combustion Institute.

Semi-implicit second order finite volume jet stream computations

Semi-implicit, second order temporal and spatial finite volume computations of the flow in a differentially heated rotating annulus are presented. For the regime considered, three cyclones and anticyclones separated by a relatively fast moving jet of fluid or "jet stream" are predicted. Two second order methods are compared with, first order spatial predictions, and experimental measurements. Velocity vector plots are used to illustrate the predicted flow structure. Computations made using second order central differences are shown to agree best with experimental measurements, and to be stable for integrations over long time periods (> 1000s). No periodic smoothing is required to prevent divergence.