Blind insight: metacognitive discrimination despite chance task performance

Blindsight and other examples of unconscious knowledge and perception demonstrate dissociations between judgment accuracy and metacognition: Studies reveal that participants’ judgment accuracy can be above chance while their confidence ratings fail to discriminate right from wrong answers. Here, we demonstrated the opposite dissociation: a reliable relationship between confidence and judgment accuracy (demonstrating metacognition) despite judgment accuracy being no better than chance. We evaluated the judgments of 450 participants who completed an AGL task. For each trial, participants decided whether a stimulus conformed to a given set of rules and rated their confidence in that judgment. We identified participants who performed at chance on the discrimination task, utilizing a subset of their responses, and then assessed the accuracy and the confidence-accuracy relationship of their remaining responses. Analyses revealed above-chance metacognition among participants who did not exhibit decision accuracy. This important new phenomenon, which we term blind insight, poses critical challenges to prevailing models of metacognition grounded in signal detection theory

Automated parameters for troubled-cell indicators using outlier detection

In Vuik and Ryan (2014) we studied the use of troubled-cell indicators for discontinuity detection in nonlinear hyperbolic partial differential equations and introduced a new multiwavelet technique to detect troubled cells. We found that these methods perform well as long as a suitable, problem-dependent parameter is chosen. This parameter is used in a threshold which decides whether or not to detect an element as a troubled cell. Until now, these parameters could not be chosen automatically. The choice of the parameter has impact on the approximation: it determines the strictness of the troubled-cell indicator. An inappropriate choice of the parameter will result in detection (and limiting) of too few or too many elements. The optimal parameter is chosen such that the minimal number of troubled cells is detected and the resulting approximation is free of spurious oscillations. In this paper we will see that for each troubled-cell indicator the sudden increase or decrease of the indicator value with respect to the neighboring values is important for detection. Indication basically reduces to detecting the outliers of a vector (one dimension) or matrix (two dimensions). This is done using Tukey's boxplot approach to detect which coefficients in a vector are straying far beyond others (Tukey, 1977). We provide an algorithm that can be applied to various troubled-cell indication variables. Using this technique the problem-dependent parameter that the original indicator requires is no longer necessary as the parameter will be chosen automatically

Compact strain-sensitive flexible photonic crystals for sensors

A promising fabrication route to produce absorbing flexible photonic crystals is presented, which exploits self-assembly during the shear processing of multi-shelled polymer spheres. When absorbing material is incorporated in the interstitial space surrounding high-refractive-index spheres, a dramatic enhancement in the transmission edge on the short-wavelength side of the band gap is observed. This effect originates from the shifting optical field spatial distribution as the incident wavelength is tuned around the band gap, and results in a contrast up to 100 times better than similar but nonabsorbing photonic crystals. An order-of-magnitude improvement in strain sensitivity is shown, suggesting the use of these thin films in photonic sensors

Multi-dimensional filtering: Reducing the dimension through rotation

Over the past few decades there has been a strong effort towards the development of Smoothness-Increasing Accuracy-Conserving (SIAC) filters for Discontinuous Galerkin (DG) methods, designed to increase the smoothness and improve the convergence rate of the DG solution through this post-processor. These advantages can be exploited during flow visualization, for example by applying the SIAC filter to the DG data before streamline computations [Steffan et al., IEEE-TVCG 14(3): 680-692]. However, introducing these filters in engineering applications can be challenging since a tensor product filter grows in support size as the field dimension increases, becoming computationally expensive. As an alternative, [Walfisch et al., JOMP 38(2);164-184] proposed a univariate filter implemented along the streamline curves. Until now, this technique remained a numerical experiment. In this paper we introduce the line SIAC filter and explore how the orientation, structure and filter size affect the order of accuracy and global errors. We present theoretical error estimates showing how line filtering preserves the properties of traditional tensor product filtering, including smoothness and improvement in the convergence rate. Furthermore, numerical experiments are included, exhibiting how these filters achieve the same accuracy at significantly lower computational costs, becoming an attractive tool for the scientific visualization community

[OII] emitters in the GOODS field at z~1.85: a homogeneous measure of evolving star formation

We present the results of a deep, near-infrared, narrow band imaging survey at a central wavelength of 1.062 microns (FWHM=0.01 microns) in the GOODS-South field using the ESO VLT instrument, HAWK-I. The data are used to carry out the highest redshift search for [OII]3727 emission line galaxies to date. The images reach an emission line flux limit (5 sigma) of 1.5 x 10^-17 erg cm^-2 s^-1, additionally making the survey the deepest of its kind at high redshift. In this paper we identify a sample of [OII]3727 emission line objects at redshift z~1.85 in a co-moving volume of ~4100 Mpc^3. Objects are selected using an observed equivalent width (EW_obs) threshold of EW_obs = 50 angstroms. The sample is used to derive the space density and constrain the luminosity function of [OII] emitters at z=1.85. We find that the space density of objects with observed [OII] luminosities in the range log(L_[OII]) > 41.74 erg s^-1 is log(rho)=-2.45+/-0.14 Mpc^-3, a factor of 2 greater than the observed space density of [OII] emitters reported at z~1.4. After accounting for completeness and assuming an internal extinction correction of A_Halpha=1 mag (equivalent to A_[OII]=1.87), we report a star formation rate density of rho* ~0.38+/-0.06 Msun yr^-1 Mpc^-3. We independently derive the dust extinction of the sample using 24 micron fluxes and find a mean extinction of A_[OII]=0.98+/-0.11 magnitudes (A_Halpha=0.52). This is significantly lower than the A_Halpha=1 (A[OII]=1.86) mag value widely used in the literature. Finally we incorporate this improved extinction correction into the star formation rate density measurement and report rho*~0.24+/-0.06 Msun yr^-1 Mpc^-3.Comment: 11 pages, 10 figures, accepted for publication in MNRA

The spectrum of radial, orbital and gluonic excitations of charmonium

We present results for the charmonium spectrum from $N_f=2$ dynamical QCD simulations on $12^3\times 80$ anisotropic lattices. Using all-to-all propagators we determine the ground and excited states of S, P and D waves and hybrids. We also evaluate the disconnected (OZI suppressed) contribution to the $\eta_c$ and $J/\Psi$Comment: 6 pages, 3 figures, Presented at 24th International Symposium on Lattice Field Theory (Lattice 2006), Tucson, Arizona, 23-28 Jul 200

What do we know about rail suicide incidents?: analysis of 257 fatalities on the rail network in Great Britain

There are over two hundred and fifty suicides on the railway in Great Britain (GB) each year. Descriptive statistics are compiled, producing national and international data. The industry know how many and, to a limited extent, where these fatalities occur. There is little in-depth analysis of events. Therefore, there are gaps in knowledge of these fatalities and this is a weakness when considering the best approaches to prevention. This paper reports on the analysis of data on 257 suicide events at or near to 51 stations on three rail routes in Great Britain over a 20 year period. The analysis uses data from the industry Safety Management System (SMIS) database and produces simple descriptive statistics on a range of variables, including comparisons across the three rail routes. Additional data from staff and route based documentation have been used to verify, supplement and interpret information in the database. Examples of patterns of immediate and precursor behaviours during incidents have been presented, illustrating the potential to explore both common and anomalous behaviours during events. The findings demonstrate the type of content that can be explored within the industry data and through use of other data that are available within the industry. Commentary is provided on the strengths and weaknesses of the data and how findings from the analysis can be used to improve future data collection and prevention of incidents