Understanding Test Takers' Choices in a Self-Adapted Test: A Hidden Markov Modeling of Process Data

With the rise of more interactive assessments, such as simulation- and game-based assessment, process data are available to learn about students' cognitive processes as well as motivational aspects. Since process data can be complicated due to interdependencies in time, our traditional psychometric models may not necessarily fit, and we need to look for additional ways to analyze such data. In this study, we draw process data from a study on self-adapted test under different goal conditions (Arieli-Attali, 2016) and use hidden Markov models to learn about test takers' choice making behavior. Self-adapted test is designed to allow test takers to choose the level of difficulty of the items they receive. The data includes test results from two conditions of goal orientation (performance goal and learning goal), as well as confidence ratings on each question. We show that using HMM we can learn about transition probabilities from one state to another as dependent on the goal orientation, the accumulated score and accumulated confidence, and the interactions therein. The implications of such insights are discussed

The polytropic approximation and X-ray scaling relations: constraints on gas and dark matter profiles for galaxy groups and clusters

We constrain gas and dark matter (DM) parameters of galaxy groups and clusters, by comparing X-ray scaling relations to theoretical expectations, obtained assuming that the gas is in hydrostatic equilibrium with the DM and follows a polytropic relation. We vary four parameters: the gas polytropic index Gamma, its temperature at large radii T_xi, the DM logarithmic slope at large radii zeta and its concentration c_vir. When comparing the model to the observed mass-temperature (M-T) relation of local clusters, our results are independent of both T_xi and c_vir. We thus obtain constraints on Gamma, by fixing the DM profile, and on zeta, by fixing the gas profile. For an NFW DM profile, we find that 6/5<Gamma<13/10, which is consistent with numerical simulations and observations of individual clusters. Taking 6/5<Gamma<13/10 allows the DM profile to be slightly steeper than the NFW profile at large radii. Upon including local groups, we constrain the mass-dependence of Gamma and the value of T_xi. Interestingly, with Gamma=6/5 and zeta=-3, we reproduce the observed steepening/breaking of the M-T relation at low M, if 10^6 K<T_xi<10^7 K, consistent with simulations and observations of the warm-hot intergalactic medium. When extrapolated to high redshift z, the model with a constant Gamma reproduces the expected self-similar behaviour. We also account for the observed, non-self-similar relations provided by some high-z clusters, as they provide constraints on the evolution of Gamma. Comparing our model to the observed luminosity-temperature relation, we discriminate between different M-c_vir relations: a weak dependence of c_vir on M is currently preferred by data. This simple theoretical model accounts for much of the complexity of recent, improved X-ray scaling relations, provided that we allow for a mild dependence of Gamma on M or for T_xi consistent with intercluster values. [abridged]Comment: 20 pages, 18 figures, 2 tables. Accepted for publication in MNRAS, with minor changes. Accepted version plus two typos corrected. Abstract abridged for astro-ph submissio

Weak pairwise correlations imply strongly correlated network states in a neural population

Biological networks have so many possible states that exhaustive sampling is impossible. Successful analysis thus depends on simplifying hypotheses, but experiments on many systems hint that complicated, higher order interactions among large groups of elements play an important role. In the vertebrate retina, we show that weak correlations between pairs of neurons coexist with strongly collective behavior in the responses of ten or more neurons. Surprisingly, we find that this collective behavior is described quantitatively by models that capture the observed pairwise correlations but assume no higher order interactions. These maximum entropy models are equivalent to Ising models, and predict that larger networks are completely dominated by correlation effects. This suggests that the neural code has associative or error-correcting properties, and we provide preliminary evidence for such behavior. As a first test for the generality of these ideas, we show that similar results are obtained from networks of cultured cortical neurons.Comment: Full account of work presented at the conference on Computational and Systems Neuroscience (COSYNE), 17-20 March 2005, in Salt Lake City, Utah (http://cosyne.org

Neural Network Mechanisms Underlying Stimulus Driven Variability Reduction

It is well established that the variability of the neural activity across trials, as measured by the Fano factor, is elevated. This fact poses limits on information encoding by the neural activity. However, a series of recent neurophysiological experiments have changed this traditional view. Single cell recordings across a variety of species, brain areas, brain states and stimulus conditions demonstrate a remarkable reduction of the neural variability when an external stimulation is applied and when attention is allocated towards a stimulus within a neuron's receptive field, suggesting an enhancement of information encoding. Using an heterogeneously connected neural network model whose dynamics exhibits multiple attractors, we demonstrate here how this variability reduction can arise from a network effect. In the spontaneous state, we show that the high degree of neural variability is mainly due to fluctuation-driven excursions from attractor to attractor. This occurs when, in the parameter space, the network working point is around the bifurcation allowing multistable attractors. The application of an external excitatory drive by stimulation or attention stabilizes one specific attractor, eliminating in this way the transitions between the different attractors and resulting in a net decrease in neural variability over trials. Importantly, non-responsive neurons also exhibit a reduction of variability. Finally, this reduced variability is found to arise from an increased regularity of the neural spike trains. In conclusion, these results suggest that the variability reduction under stimulation and attention is a property of neural circuits

An Algebraic Theory for Data Linkage

There are countless sources of data available to governments, companies, and citizens, which can be combined for good or evil. We analyse the concepts of combining data from common sources and linking data from different sources. We model the data and its information content to be found in a single source by an ordered partial monoid, and the transfer of information between sources by different types of morphisms. To capture the linkage between a family of sources, we use a form of Grothendieck construction to create an ordered partial monoid that brings together the global data of the family in a single structure. We apply our approach to database theory and axiomatic structures in approximate reasoning. Thus, ordered partial monoids provide a foundation for the algebraic study for information gathering in its most primitive form

In vivo monitoring of neuronal loss in traumatic brain injury: a microdialysis study

Traumatic brain injury causes diffuse axonal injury and loss of cortical neurons. These features are well recognized histologically, but their in vivo monitoring remains challenging. In vivo cortical microdialysis samples the extracellular fluid adjacent to neurons and axons. Here, we describe a novel neuronal proteolytic pathway and demonstrate the exclusive neuro-axonal expression of Pavlov’s enterokinase. Enterokinase is membrane bound and cleaves the neurofilament heavy chain at positions 476 and 986. Using a 100 kDa microdialysis cut-off membrane the two proteolytic breakdown products, extracellular fluid neurofilament heavy chains NfH476−986 and NfH476−1026, can be quantified with a relative recovery of 20%. In a prospective clinical in vivo study, we included 10 patients with traumatic brain injury with a median Glasgow Coma Score of 9, providing 640 cortical extracellular fluid samples for longitudinal data analysis. Following high-velocity impact traumatic brain injury, microdialysate extracellular fluid neurofilament heavy chain levels were significantly higher (6.18 ± 2.94 ng/ml) and detectable for longer (>4 days) compared with traumatic brain injury secondary to falls (0.84 ± 1.77 ng/ml, <2 days). During the initial 16 h following traumatic brain injury, strong correlations were found between extracellular fluid neurofilament heavy chain levels and physiological parameters (systemic blood pressure, anaerobic cerebral metabolism, excessive brain tissue oxygenation, elevated brain temperature). Finally, extracellular fluid neurofilament heavy chain levels were of prognostic value, predicting mortality with an odds ratio of 7.68 (confidence interval 2.15–27.46, P = 0.001). In conclusion, this study describes the discovery of Pavlov’s enterokinase in the human brain, a novel neuronal proteolytic pathway that gives rise to specific protein biomarkers (NfH476−986 and NfH476−1026) applicable to in vivo monitoring of diffuse axonal injury and neuronal loss in traumatic brain injury

A Realistic Validation Study of a New Nitrogen Multiple-Breath Washout System

Background For reliable assessment of ventilation inhomogeneity, multiple-breath washout (MBW) systems should be realistically validated. We describe a new lung model for in vitro validation under physiological conditions and the assessment of a new nitrogen (N2)MBW system. Methods The N2MBW setup indirectly measures the N2 fraction (FN2) from main-stream carbon dioxide (CO2) and side-stream oxygen (O2) signals: FN2 = 1−FO2−FCO2−FArgon. For in vitro N2MBW, a double chamber plastic lung model was filled with water, heated to 37°C, and ventilated at various lung volumes, respiratory rates, and FCO2. In vivo N2MBW was undertaken in triplets on two occasions in 30 healthy adults. Primary N2MBW outcome was functional residual capacity (FRC). We assessed in vitro error (√[difference]2) between measured and model FRC (100–4174 mL), and error between tests of in vivo FRC, lung clearance index (LCI), and normalized phase III slope indices (Sacin and Scond). Results The model generated 145 FRCs under BTPS conditions and various breathing patterns. Mean (SD) error was 2.3 (1.7)%. In 500 to 4174 mL FRCs, 121 (98%) of FRCs were within 5%. In 100 to 400 mL FRCs, the error was better than 7%. In vivo FRC error between tests was 10.1 (8.2)%. LCI was the most reproducible ventilation inhomogeneity index. Conclusion The lung model generates lung volumes under the conditions encountered during clinical MBW testing and enables realistic validation of MBW systems. The new N2MBW system reliably measures lung volumes and delivers reproducible LCI values