Structural selection in implicit learning of artificial grammars

In the contextual cueing paradigm, Endo and Takeda (in Percept Psychophys 66:293–302, 2004) provided evidence that implicit learning involves selection of the aspect of a structure that is most useful to one’s task. The present study attempted to replicate this finding in artificial grammar learning to investigate whether or not implicit learning commonly involves such a selection. Participants in Experiment 1 were presented with an induction task that could be facilitated by several characteristics of the exemplars. For some participants, those characteristics included a perfectly predictive feature. The results suggested that the aspect of the structure that was most useful to the induction task was selected and learned implicitly. Experiment 2 provided evidence that, although salience affected participants’ awareness of the perfectly predictive feature, selection for implicit learning was mainly based on usefulness

Reynolds number effect on the velocity increment skewness in isotropic turbulence

Second and third order longitudinal structure functions and wavenumber spectra of isotropic turbulence are computed using the EDQNM model and compared to results of the multifractal formalism. At the highest Reynolds number available in windtunnel experiments, $R_\lambda=2500$, both the multifractal model and EDQNM give power-law corrections to the inertial range scaling of the velocity increment skewness. For EDQNM, this correction is a finite Reynolds number effect, whereas for the multifractal formalism it is an intermittency correction that persists at any high Reynolds number. Furthermore, the two approaches yield realistic behavior of second and third order statistics of the velocity fluctuations in the dissipative and near-dissipative ranges. Similarities and differences are highlighted, in particular the Reynolds number dependence

Brain Drain: The Mere Presence of One’s Own Smartphone Reduces Available Cognitive Capacity

Our smartphones enable—and encourage—constant connection to information, entertainment, and each other. They put the world at our fingertips, and rarely leave our sides. Although these devices have immense potential to improve welfare, their persistent presence may come at a cognitive cost. In this research, we test the “brain drain” hypothesis that the mere presence of one’s own smartphone may occupy limited-capacity cognitive resources, thereby leaving fewer resources available for other tasks and undercutting cognitive performance. Results from two experiments indicate that even when people are successful at maintaining sustained attention—as when avoiding the temptation to check their phones—the mere presence of these devices reduces available cognitive capacity. Moreover, these cognitive costs are highest for those highest in smartphone dependence. We conclude by discussing the practical implications of this smartphone-induced brain drain for consumer decision-making and consumer welfare.Marketin

User Experience Evaluation in BCI: Filling the Gap

Brain-computer interface (BCI) systems can improve the user experience (UX) when used in entertainment technologies. Improved UX can enhance user acceptance, improve quality of life and also increase the system performance of a BCI system. Therefore, the evaluation of UX is essential in BCI research. However, BCI systems are generally evaluated according to the system aspect only so there is no methodology to evaluate UX in BCI systems. This paper gives an overview of such methods from the human-computer interaction field and discusses their possible uses in BCI research

Space-time discontinuous Galerkin method for the compressible Navier-Stokes equations on deforming meshes

An overview is given of a space-time discontinuous Galerkin finite element method for the compressible Navier-Stokes equations. This method is well suited for problems with moving (free) boundaries which require the use of deforming elements. In addition, due to the local discretization, the space-time discontinuous Galerkin method is well suited for mesh adaptation and parallel computing. The algorithm is demonstrated with computations of the unsteady \ud ow field about a delta wing and a NACA0012 airfoil in rapid pitch up motion

Direct evidence for the magnetic ordering of Nd ions in NdFeAsO by high resolution inelastic neutron scattering

We investigated the low energy excitations in the parent compound NdFeAsO of the Fe-pnictide superconductor in the $\mu$eV range by a back scattering neutron spectrometer. The energy scans on a powder NdFeAsO sample revealed inelastic peaks at E = 1.600 $\pm 0.003 \mu$eV at T = 0.055 K on both energy gain and energy loss sides. The inelastic peaks move gradually towards lower energy with increasing temperature and finally merge with the elastic peak at about 6 K. We interpret the inelastic peaks to be due to the transition between hyperfine-split nuclear level of the $^{143}$Nd and $^{145}$Nd isotopes with spin $I = 7/2$. The hyperfine field is produced by the ordering of the electronic magnetic moment of Nd at low temperature and thus the present investigation gives direct evidence of the ordering of the Nd magnetic sublattice of NdFeAsO at low temperature

Concept and considerations of a medical device:the active noise cancelling incubator

Background: An increasingly 24/7 connected and urbanised world has created a silent pandemic of noise-induced hearing loss. Ensuring survival to children born (extremely) preterm is crucial. The incubator is a closed medical device, modifying the internal climate, and thus providing an environment for the child, as safe, warm, and comfortable as possible. While sound outside the incubator is managed and has decreased over the years, managing the noise inside the incubator is still a challenge.Method: Using active noise cancelling in an incubator will eliminate unwanted sounds (i.e., from the respirator and heating) inside the incubator, and by adding sophisticated algorithms, normal human speech, neonatal intensive care unit music-based therapeutic interventions, and natural sounds will be sustained for the child in the pod. Applying different methods such as active noise cancelling, motion capture, sonological engineering. and sophisticated machine learning algorithms will be implemented in the development of the incubator. Projected Results: A controlled and active sound environment in and around the incubator can in turn promote the wellbeing, neural development, and speech development of the child and minimise distress caused by unwanted noises. While developing the hardware and software pose individual challenges, it is about the system design and aspects contributing to it. On the one hand, it is crucial to measure the auditory range and frequencies in the incubator, as well as the predictable sounds that will have to be played back into the environment. On the other, there are many technical issues that have to be addressed when it comes to algorithms, datasets, delay, microphone technology, transducers, convergence, tracking, impulse control and noise rejection, noise mitigation stability, detection, polarity, and performance.Conclusion: Solving a complex problem like this, however, requires a de-disciplinary approach, where each discipline will realise its own shortcomings and boundaries, and in turn will allow for innovations and new avenues. Technical developments used for building the active noise cancellation-incubator have the potential to contribute to improved care solutions for patients, both infants and adults. Code available at: 10.3389/fped.2023.1187815.</p