Effects of visual blur and contrast on spatial and temporal precision in manual interception

The visual system is said to be especially sensitive towards spatial but lesser so towards temporal information. To test this, in two experiments, we systematically reduced the acuity and contrast of a visual stimulus and examined the impact on spatial and temporal precision (and accuracy) in a manual interception task. In Experiment 1, we blurred a virtual, to-be-intercepted moving circle (ball). Participants were asked to indicate (i.e., finger tap) on a touchscreen where and when the virtual ball crossed a ground line. As a measure of spatial and temporal accuracy and precision, we analyzed the constant and variable errors, respectively. With increasing blur, the spatial and temporal variable error, as well as the spatial constant error increased, while the temporal constant error decreased. Because in the first experiment, blur was potentially confounded with contrast, in Experiment 2, we re-ran the experiment with one difference: instead of blur, we included five levels of contrast matched to the blur levels. We found no systematic effects of contrast. Our findings confirm that blurring vision decreases spatial precision and accuracy and that the effects were not mediated by concomitant changes in contrast. However, blurring vision also affected temporal precision and accuracy, thereby questioning the generalizability of the theoretical predictions to the applied interception task.

A measurement error Rao–Yu model for regional prevalence estimation over time using uncertain data obtained from dependent survey estimates

The assessment of prevalence on regional levels is an important element of public health reporting. Since regional prevalence is rarely collected in registers, corresponding figures are often estimated via small area estimation using suitable health data. However, such data are frequently subject to uncertainty as values have been estimated from surveys. In that case, the method for prevalence estimation must explicitly account for data uncertainty to allow for reliable results. This can be achieved via measurement error models that introduce distribution assumptions on the noisy data. However, these methods usually require target and explanatory variable errors to be independent. This does not hold when data for both have been estimated from the same survey, which is sometimes the case in official statistics. If not accounted for, prevalence estimates can be severely biased. We propose a new measurement error model for regional prevalence estimation that is suitable for settings where target and explanatory variable errors are dependent. We derive empirical best predictors and demonstrate mean-squared error estimation. A maximum likelihood approach for model parameter estimation is presented. Simulation experiments are conducted to prove the effectiveness of the method. An application to regional hypertension prevalence estimation in Germany is provided.

Differentiation of the Strength of Back Muscle Contraction Under Fatigue: Does Force Feedback Play a Role?

Objective: This study investigates the ability of subjects to differentiate the strength of back muscle contraction with and without feedback information on force produced under fatigue and nonfatigue conditions. Design: Controlled laboratory study. Setting: Research laboratory environment. Participants: A group of 52 healthy young men participated in the study. Intervention: Subjects self-estimated 50% of the maximal voluntary isometric contraction of back muscles either on their own volition or on the basis of information about the actual force, before and after the Sørensen fatigue test. Main Outcome Measures: The force was measured by means of the FiTRO Back Dynamometer. Results: The self-estimated 50% maximal voluntary isometric contraction was significantly higher than the one calculated from maximal voluntary isometric contraction during 10 trials in 2 repeated sessions (8.3% and 10.0%, P < .05). However, when feedback on the force produced was provided, significantly higher values were observed during an initial trial in both sessions (8.5%, P = .04 and 12.1%, P = .01). Subjects were able to estimate the target force during the following trials. Fatigue induced a decrease in peak force (7.7%, P = .04), whereas the ability to regulate the prescribed force was not compromised. Constant error was lower with than without force feedback during both measurements (2.15% and 6.85%; 3.06% and 8.56%). However, constant and variable errors were greater under fatigue than nonfatigue conditions (8.43% and 5.55%; 0.41% and 0.37%). Similarly, root mean square error decreased with force feedback (from 6.88% to 3.48% and from 8.74% to 5.09%) and increased under fatigue (from 5.87% to 8.67%). Conclusions: These findings indicate that force feedback plays a role in the differentiation of the strength of back muscle contraction, regardless of fatigue. It contributes to a more precise regulation of force produced during voluntary isometric contraction of back muscles. This promising method awaits further experimentation to be applied for individuals with low back pain.

Moving sliding mode controller for overhead cranes suffering from matched and unmatched disturbances

In this paper, a novel time-varying gain extended state observer (ESO)-based moving sliding mode control method is proposed for anti-sway and positioning control of two-dimensional underactuated overhead cranes. The designed moving sliding mode surface can adjust its slope in real time according to the state variable errors; in addition, a dynamic exponential term is added into the moving sliding mode surface so as to drive any initial state variable errors into the sliding surface rapidly, and thereby the robustness of crane systems is improved. Then, a chattering-free reaching law is designed to realize fast convergence of the system state errors, and the input is modelled as a saturated one due to the fact the motor torque is bounded and the control law and adaptive updating law of switching gain are derived in the sense of Lyapunov function, so the stability can be guaranteed even under the input saturation. Moreover, to suppress the matched and unmatched disturbance occurring in crane dynamic systems, a time-varying gain ESO is constructed to estimate the lumped disturbance, then the estimated value is used for feedforward compensation to establish the controller. Finally, the simulation results confirm the effectiveness of the proposed controller.

Considerations on Parameter and State Estimation with Ensemble Data Assimilation Methods – A Case Study with a Nonlinear Oscillator

&lt;p&gt;This study investigates the effect of erroneous parameter values for state and parameter estimation using data assimilation. The numerical model chosen for this study solves the van der Pol equation, a second-order differential equation that can be used to simulate oscillatory processes, such as earthquakes. In the model, discrepancies in the parameter values can have a significant influence on the forecasted states of the model, which is even more significant if its behaviour is highly nonlinear. When observations of the state variables are assimilated to update the parameters along with the state variables, this improves the quality of the state forecasts. The results suggest that corrections in the model parameter not only recover the actual parameter values but also reduce state-variable errors after a certain time period. However, data assimilation that updates the state variables but not the parameter can lead to erroneous estimates as well as forecasts of the oscillation. Since the study is performed on a simplified nonlinear model framework, the consequences of these results for data assimilation in more realistic models remains to be investigated.&lt;/p&gt;

Variance components affecting the repeatability of the alternating cover test

In within-subject and within-examiner repeated measures designs, measures of heterophoria with the manual prism cover test achieve standard deviations between 0.5 and 0.8 deg. We addressed the question how this total noise is composed of variable errors related to the examiner (measurement noise), to the size of the heterophoria (heterophoria noise), and to the availability of sensory vergence cues (stimulus noise). We developed an automated alternating cover test (based on a combination of VOG and shutter glasses) which minimizes stimulus noise and has a defined measurement noise (sd=0.06 deg). In a within-subject design, 19 measures were taken within 1.5 min and multiple such blocks were repeated either across days or across 45 min. Blocks were separated by periods of binocular viewing. The standard deviation of the heterophoria across blocks from different days or from the same day (sd=0.33 deg) was 6 times larger than expected based on the standard deviation within the block. The results show that about 42% of the inter-block variance with the manual prism cover test was related to variability of the heterophoria and not to measurement noise or stimulus noise. The heterophoria noise across blocks was predominantly induced during the inter-mediate binocular viewing periods.

The Mechanics of Treatment-effect Estimate Bias for Nonexperimental Data

To measure “treatment” effects, social science researchers typically rely on nonexperimental data. In education, school and teacher effects on students are often measured through value-added models (VAMs) that are not fully understood. We propose a framework that relates to the education production function in its most flexible form and connects with the basic VAMs without using untenable assumptions. We illustrate how, due to measurement error (ME), cross-group imbalances created by nonrandom group assignment cause correlations that drive the models’ treatment-effect estimate bias. We derive formulas to calculate bias and rank the models and show that no model is better in all situations. The framework and formulas’ workings are verified and illustrated via simulation. We also evaluate the performance of latent variable/errors-in-variables models that handle ME and study the role of extra covariates including lags of the outcome.

Spatial bias in estimating the position of visual and proprioceptive targets

When people match an unseen hand to a visual or proprioceptive target, they make both variable and systematic (bias) errors. Variance is a well-established factor in behavior, but the origin and implications of bias, and its connection to variance, are poorly understood. Eighty healthy adults matched their unseen right index finger to proprioceptive (left index finger) and visual targets with no performance feedback. We asked whether matching bias was related to target modality and to the magnitude or spatial properties of matching variance. Bias errors were affected by target modality, with subjects estimating visual and proprioceptive targets 20 mm apart. We found three pieces of evidence to suggest a connection between bias and variable errors: 1) for most subjects, the target modality that yielded greater spatial bias was also estimated with greater variance; 2) magnitudes of matching bias and variance were somewhat correlated for each target modality ( R = 0.24 and 0.29); and 3) bias direction was closely related to the angle of the major axis of the confidence ellipse ( R = 0.60 and 0.63). However, whereas variance was significantly correlated with visuo-proprioceptive weighting as predicted by multisensory integration theory ( R = −0.29 and 0.27 for visual and proprioceptive variance, respectively), bias was not. In a second session, subjects improved their matching variance, but not bias, for both target modalities, indicating a difference in stability. Taken together, these results suggest bias and variance are related only in some respects, which should be considered in the study of multisensory behavior. NEW & NOTEWORTHY People matching visual or proprioceptive targets make both variable and systematic (bias) errors. Multisensory integration is thought to minimize variance, but if the less variable modality has more bias, behavioral accuracy will decrease. Our data set suggests this is unusual. However, although bias and variable errors were spatially related, they differed in both stability and correlation with multisensory weighting. This suggests the bias-variance relationship is not straightforward, and both should be considered in multisensory behavior.

Quantification of uncertainties in OCO-2 measurements of XCO₂: simulations and linear error analysis

We present an analysis of uncertainties in global measurements of the column averaged dry-air mole fraction of CO2 (XCO2) by the NASA Orbiting Carbon Observatory-2 (OCO-2). The analysis is based on our best estimates for uncertainties in the OCO-2 operational algorithm and its inputs, and uses simulated spectra calculated for the actual flight and sounding geometry, with measured atmospheric analyses. The simulations are calculated for land nadir and ocean glint observations. We include errors in measurement, smoothing, interference, and forward model parameters. All types of error are combined to estimate the uncertainty in XCO2 from single soundings, before any attempt at bias correction has been made. From these results we also estimate the "variable error" which differs between soundings, to infer the error in the difference of XCO2 between any two soundings. The most important error sources are aerosol interference, spectroscopy, and instrument calibration. Aerosol is the largest source of variable error. Spectroscopy and calibration, although they are themselves fixed error sources, also produce important variable errors in XCO2. Net variable errors are usually < 1 ppm over ocean and ∼ 0.5–2.0 ppm over land. The total error due to all sources is ∼ 1.5–3.5 ppm over land and ∼ 1.5–2.5 ppm over ocean.

Quantification of Uncertainties in OCO-2 Measurements of XCO₂: Simulations and Linear Error Analysis

We present an analysis of uncertainties in global measurements of the column averaged dry-air mole fraction of CO2 ('XCO2') by the NASA Orbiting Carbon Observatory-2, ('OCO-2'). The analysis is based on our best estimates for uncertainties in the OCO-2 operational algorithm and its inputs, and uses simulated spectra calculated for the actual flight and sounding geometry, with measured atmospheric analyses. The simulations are calculated for land nadir and ocean glint observations. We include errors in measurement, smoothing, interference, and forward model parameters. All types of error are combined to estimate the uncertainty in XCO2 from single soundings, before any attempt at bias correction has been made. From these results we also estimate the 'variable error' which differs between soundings, to infer the error in the difference of XCO2 between any two soundings. The most important error sources are aerosol interference, spectroscopy, and instrument calibration. Aerosol is the largest source of variable error. Variable errors are usually