Common cortical areas have different neural mechanisms for covert and overt visual pursuits

Although humans can direct their attention to visual targets with or without eye movements, it remains unclear how different brain mechanisms control visual attention and eye movements together and/or separately. Here, we measured MEG and fMRI data during covert/overt visual pursuit tasks and estimated cortical currents using our previously developed extra-dipole, hierarchical Bayesian method. Then, we predicted the time series of target positions and velocities from the estimated cortical currents of each task using a sparse machine-learning algorithm. The predicted target positions/velocities had high temporal correlations with actual visual target kinetics. Additionally, we investigated the generalization ability of predictive models among three conditions: control, covert, and overt pursuit tasks. When training and testing data were the same tasks, the largest reconstructed accuracies were overt, followed by covert and control, in that order. When training and testing data were selected from different tasks, accuracies were in reverse order. These results are well explained by the assumption that predictive models consist of combinations of three computational brain functions: visual information-processing, maintenance of attention, and eye-movement control. Our results indicate that separate subsets of neurons in the same cortical regions control visual attention and eye movements differently.

Visual search under physical effort is faster but more vulnerable to distractor interference

Cognition and action are often intertwined in everyday life. It is thus pivotal to understand how cognitive processes operate with concurrent actions. The present study aims to assess how simple physical effort operationalized as isometric muscle contractions affects visual attention and inhibitory control. In a dual-task paradigm, participants performed a singleton search task and a handgrip task concurrently. In the search task, the target was a shape singleton among distractors with a homogeneous but different shape. A salient-but-irrelevant distractor with a unique color (i.e., color singleton) appeared on half of the trials (Singleton distractor present condition), and its presence often captures spatial attention. Critically, the visual search task was performed by the participants with concurrent hand grip exertion, at 5% or 40% of their maximum strength (low vs. high physical load), on a hand dynamometer. We found that visual search under physical effort is faster, but more vulnerable to distractor interference, potentially due to arousal and reduced inhibitory control, respectively. The two effects further manifest in different aspects of RT distributions that can be captured by different components of the ex-Gaussian model using hierarchical Bayesian method. Together, these results provide behavioral evidence and a novel model for two dissociable cognitive mechanisms underlying the effects of simple muscle exertion on the ongoing visual search process on a moment-by-moment basis.

Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models

The aim of this paper is to forecast monthly crude oil price with a hierarchical shrinkage approach, which utilizes not only LASSO for predictor selection, but a hierarchical Bayesian method to determine whether constant coefficient (CC) or time-varying parameter (TVP) predictive regression should be employed in each out-of-sample forecasting step. This newly developed method has the advantages of both model shrinkage and automatic switch between CC and TVP forecasting models; thus, this may produce more accurate predictions of crude oil prices. The empirical results show that this hierarchical shrinkage model can outperform many commonly used forecasting benchmark methods, such as AR, unobserved components stochastic volatility (UCSV), and multivariate regression models in forecasting crude oil price on various forecasting horizons.

Spatially resolved dust-to-gas mass ratios in nearby galaxies

We analyse the dust-to-gas mass ratio (DGR) in nearby galaxies on kiloparsec scales. We focus on their dependence on metallicity and the CO-to-H2 conversion factor, αco. We use a sample of 25 nearby galaxies from SINGS and combine our data with CO (2-1) and H I observations from the HERACLES and THINGS surveys. We implement a Hierarchical Bayesian method to derive the dust mass via fitting the infrared data from 100 to 500 μm with a single modified blackbody. We find that the DGR-metallicity relation follows a power law and we study its strong dependency on the conversion factor αco. Our results indicate a strong connection between interstellar dust and gas. The resolved DGR-metallicity relation cannot be represented with a single power law. The scatter in this relation shows the strong impact of several processes that take place in every galaxy.

A hierarchical bayesian dust SED model and its application to the nearby universe

In this paper, I review several dust evolution studies based on the DustPedia nearby galaxy sample. I first present the dust spectral energy distribution model, implementing a hierarchical Bayesian method, that we have developed. I then discuss the dust evolution trends we have derived among (integrated) and within (resolved) galaxies. In particular, we show that the trend of dust-to-gas ratio with metallicity is clearly non-linear, indicating the need for grain growth in the interstellar medium. Our trend is closer to the one derived with damped Lyα systems than what was suggested by previous studies. We finally demonstrate the universal processing of small amorphous carbon grains by stellar photons.

A hybrid model of internal pitting corrosion degradation under changing operational conditions for pipeline integrity management

This article proposes a new framework to estimate the degradation level in oil and gas pipelines corroded by internal pitting when operational conditions change over time. Despite the fact that the operational conditions of a pipeline change at various times, this change has not been addressed in the current available pipeline corrosion degradation models. In this framework, a hierarchical Bayesian method and augmented particle filtering are used for data fusion to address this issue. This framework is applied on a case study and the results are compared with the estimations of a state of the art pitting corrosion degradation model.