Observed Response Time Research Articles

The Long and Winding Road to Real-Life Experiments: Remote Assessment of Executive Functions with Computerized Games-Results from 8 Years of Naturalistic Interventions.

Mate Marote is an open-access cognitive training software aimed at children between 4 and 8 years old. It consists of a set of computerized games specifically tailored to train and evaluate Executive Functions (EF), a class of processes critical for purposeful, goal-directed behavior, including working memory, planning, flexibility, and inhibitory control. Since 2008, several studies were performed with this software at children's own schools in interventions supervised in-person by cognitive scientists. After 2015, we incorporated naturalistic, yet controlled, interventions with children's own teachers' help. The platform includes a battery of standardized tests, disguised as games, to assess children's EF. The main question that emerges is whether the results, obtained with these traditional tasks but conducted without the presence of researchers, are comparable to those widely reported in the literature, that were obtained in more supervised settings. In this study, we were able to replicate the expected difficulty and age effects in at least one of the analyzed dependent variables of each employed test. We also report important discrepancies between the expected and the observed response time patterns, specifically for time-constrained tasks. We hereby discuss the benefits and setbacks of a new possible strategy for this type of assessment in naturalistic settings. We conclude that this battery of established EF tasks adapted for its remote usage is appropriate to measure the expected mental processes in naturalistic settings, enriching opportunities to upscale cognitive training interventions at schools. These types of tools can constitute a concerted strategy to bring together educational neuroscience research and real-life practice.

Development and Evaluation of a MQ-5 Sensor-Based Condition Monitoring System for In-Situ Pipeline Leak Detection

The condition monitoring system for an in-situ pipeline is an innovative concept that uses MQ-5 sensors to detect fuel leaks in pipelines and relay concentration data to a receiver station. The essence of the monitoring process is to ensure the safety and security of engineering properties and lives. The project addresses the crucial requirement for rapid detection of fuel leaks to avoid environmental problems, economic losses, and safety risks connected with pipeline circulation. The goal is to create a leak detection monitoring system. The project required the design and execution of four transmitter stations, each responsible for detecting fuel leaks at various places along a small-scale pipeline and transmitting concentration data to a central receiver station. The system's response time, a critical performance parameter for this project, was measured by timing how long it took for an alert to arrive at the receiving station after the transmitter detected a leak. This time was measured at three distances between the transmitter and receiver stations: 1m, 2m, and 3m. Multiple measurements were taken at each distance, and the average response time was computed. The results showed that as the distance between the stations increased, so did the reaction time. The average response time at 1m was 3.37 seconds, whereas, at 2m and 3m, it was 3.856 seconds and 4.198 seconds, respectively. A t-test inference was performed to check that there was a distinct difference between the response times for each distance, and a significant difference was detected. The built system effectively demonstrated its ability to detect fuel leaks, and the observed response times offered useful information about the system's performance at various distances. This technology demonstrates the potential to improve pipeline transportation safety and efficiency by enabling early identification of fuel leaks.

Non-parametric mixture modeling of cognitive psychological data: A new method to disentangle hidden strategies

In a wide variety of cognitive domains, participants have access to several alternative strategies to perform a particular task and, on each trial, one specific strategy is selected and executed. Determining how many strategies are used by a participant as well as their identification at a trial level is a challenging problem for researchers. In the current paper, we propose a new method – the non-parametric mixture model – to efficiently disentangle hidden strategies in cognitive psychological data, based on observed response times. The developed method derived from standard hidden Markov modeling. Importantly, we used a model-free approach where a particular shape of a response time distribution does not need to be assumed. This has the considerable advantage of avoiding potentially unreliable results when an inappropriate response time distribution is assumed. Through three simulation studies and two applications to real data, we repeatedly demonstrated that the non-parametric mixture model is able to reliably recover hidden strategies present in the data as well as to accurately estimate the number of concurrent strategies. The results also showed that this new method is more efficient than a standard parametric approach. The non-parametric mixture model is therefore a useful statistical tool for strategy identification that can be applied in many areas of cognitive psychology. To this end, practical guidelines are provided for researchers wishing to apply the non-parametric mixture models on their own data set.

Comparing the Observable Response Times of ACC and CACC Systems

This paper analyzes trajectory observations from vehicles driving in platoon formation and they are equipped with Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC) systems; aiming to quantify response delays. When a preceding vehicle induces a perturbation, the delay until the reaction of the following vehicle, often quoted as observable response time, can have negative implication to the traffic flow and other factors such as energy consumption, stability and safety. Quantifying such delays can help towards more realistic traffic simulation modeling. The analysis is performed based on empirical observations from three well-known experimental campaigns in the literature with data from ACC-driven and CACC-driven vehicle platoons. Three state-of-the-art techniques were implemented to provide quantitative results for the observed response times. The benefits and downsides of each technique are discussed as well. The results show that ACC systems do not exhibit a significant improvement compared to human drivers, yet, it can be concluded that the additional vehicle-to-vehicle communication incorporated in the CACC systems allows for a substantially higher traffic flow and possibly other benefits.

Highly sensitive graphene oxide leaf wetness sensor for disease supervision on medicinal plants

Plant disease prediction plays a pivotal role to abate the crop loss. For this purpose, early disease prediction models have been explored, where information about leaf wetness duration (LWD) is one of the important factors. The leaf wetness duration is measured with the help of leaf wetness sensors (LWS). Here, the LWS is fabricated on the polyamide flexible substrate where graphene oxide (GO) is used as the sensing film to detect the water molecules on the leaf canopy. Fabricated GO LWS has been tested under laboratory conditions, we exposed the entire sensing film with water molecule and we observed that it offers response of about 45000 % with respect to the air. Subsequently, observed response time of the fabricated sensor is around 400 s with recovery time of about 100 s. Further, the fabricated sensor shows only 2 % change in the response when exposed to the temperature ranging from 20 0C to 65 0C. Under field conditions, to explore the efficacy of the fabricated LWS, we benchmarked the LWD measured using the GO LWS with commercial LWS (Phytos 31). We have deployed the fabricated GO LWS along with Phytos 31 on the Tulsi (Ocimum tenuiflorum) medical plant. The on-field testing of the GO LWS indicates that maximum difference in LWD value using fabricated GO LWS and Phytos 31 is around ± 30 min.

Hidden Markov Models of Evidence Accumulation in Speeded Decision Tasks

Speeded decision tasks are usually modeled within the evidence accumulation framework, enabling inferences on latent cognitive parameters, and capturing dependencies between the observed response times and accuracy. An example is the speed-accuracy trade-off, where people sacrifice speed for accuracy (or vice versa). Different views on this phenomenon lead to the idea that participants may not be able to control this trade-off on a continuum, but rather switch between distinct states (Dutilh et al., Cognitive Science 35(2):211–250, 2010). Hidden Markov models are used to account for switching between distinct states. However, combining evidence accumulation models with a hidden Markov structure is a challenging problem, as evidence accumulation models typically come with identification and computational issues that make them challenging on their own. Thus, an integration of hidden Markov models with evidence accumulation models has still remained elusive, even though such models would allow researchers to capture potential dependencies between response times and accuracy within the states, while concomitantly capturing different behavioral modes during cognitive processing. This article presents a model that uses an evidence accumulation model as part of a hidden Markov structure. This model is considered as a proof of principle that evidence accumulation models can be combined with Markov switching models. As such, the article considers a very simple case of a simplified Linear Ballistic Accumulation. An extensive simulation study was conducted to validate the model’s implementation according to principles of robust Bayesian workflow. Example reanalysis of data from Dutilh et al. (Cognitive Science 35(2):211–250, 2010) demonstrates the application of the new model. The article concludes with limitations and future extensions or alternatives to the model and its application.

A Multilevel Mixture IRT Framework for Modeling Response Times as Predictors or Indicators of Response Engagement in IRT Models.

Disengaged item responses pose a threat to the validity of the results provided by large-scale assessments. Several procedures for identifying disengaged responses on the basis of observed response times have been suggested, and item response theory (IRT) models for response engagement have been proposed. We outline that response time-based procedures for classifying response engagement and IRT models for response engagement are based on common ideas, and we propose the distinction between independent and dependent latent class IRT models. In all IRT models considered, response engagement is represented by an item-level latent class variable, but the models assume that response times either reflect or predict engagement. We summarize existing IRT models that belong to each group and extend them to increase their flexibility. Furthermore, we propose a flexible multilevel mixture IRT framework in which all IRT models can be estimated by means of marginal maximum likelihood. The framework is based on the widespread Mplus software, thereby making the procedure accessible to a broad audience. The procedures are illustrated on the basis of publicly available large-scale data. Our results show that the different IRT models for response engagement provided slightly different adjustments of item parameters of individuals’ proficiency estimates relative to a conventional IRT model.

IoT Enabled, Leaf Wetness Sensor on the Flexible Substrates for In-Situ Plant Disease Management

Early plant disease detection and providing the control measures have become highly desirable to improve crop yield. Leaf wetness duration (LWD) is one of the essential parameters related to the development of fungal disease on the leaf canopy. To measured LWD, the leaf wetness sensor (LWS) is widely used. Commercially available LWS are made on printed circuit board (PCB) technology, which has an operational issue during field deployment such as weight of the sensor, contact resistance between the sensor and the leaves, form factor and most importantly, affordability. To mitigate the issues associated with the commercially available LWS, in this work, we have fabricated the in-house IoT-enabled and affordable electronic leaf wetness sensor on the flexible substrates, which is used for integrated plant disease management. Fabricated LWS comprises the interdigitated electrodes (IDEs) on the polyimide flexible substrate. The lab measurement results indicate that fabricated LWS on the flexible substrates offers a response of about 36000% when LWS is exposed to water w.r.t air. The observed response time of the fabricated LWS is about 10 seconds and hysteresis of about ± 4 %. Further, sensor capacitance changes only by 6% over a temperature range from 20 °C to 65 °C. Furthermore, three fabricated sensors LWS and in-house developed internet of things (IoT) enabled systems are deployed on the Ocimum tenuiflorum (Tulsi) medical plant. Field measurement indicates that measured LWD using the fabricated flexible LWS and commercially available LWS (Phytos 31:LWS-L12), METER Group, Inc. USA) shows the absolute difference of 30 minutes.

Large area vertical Ga2O3 Schottky diodes for X-ray detection

The Schottky barrier diodes were fabricated from a bulk Sn-doped (001) n-type Ga2O3 substrate with a Si-doped epitaxial layer grown by hydride vapor phase epitaxy (HVPE), which demonstrate a good response to X-rays. Circular metal contacts with diameters ranging from 50 to 1500μm and square metal contacts ranging from 100×100μm2 to 1600×1600μm2 were deposited on the wafer. The devices were characterized for their electrical performance including forward current–voltage (FIV), reverse current–voltage (RIV), and capacitance–voltage (CV) measurements. The best device showed a breakdown voltage of −804 V and the devices tested had an average ideality of 1.12. The devices exhibited a clear response to X-rays even at zero bias with an experimentally observed response time ∼1.03 s and a linear response of detector signal to the X-ray dose rate. The experimentally observed device response time improved to ∼0.25 s when bias voltage is applied. The device also survived a long-term stability test of over 2 h under a constant X-ray irradiation. The sensitivity and the lower limit of detection for X-ray by Ga2O3 epitaxial Schottky detectors were discussed and determined as 43.5 μC/mGy cm−2 at −200 V and 8.31 nGyAir/s, respectively.

Linear Ballistic Accumulator Item Response Theory Model for Multidimensional Multiple-Alternative Forced-Choice Measurement of Personality

There has been a growing interest in psychological measurements that use the multiple-alternative forced-choice (MAFC) response format for its resistance to response biases. Although several models have been proposed for the data obtained from such measurements, none have succeeded in incorporating the response time information. Given that currently, many psychological measurements are performed via computers, it would be beneficial to develop a joint model involving an MAFC item response and response time. The present study proposes the first model that combines a cognitive process model that underlies the observed response time and the forced-choice item response model. Specifically, the proposed model is based on the linear ballistic accumulator model of response time, which is substantially extended by reformulating its parameters so as to incorporate the MAFC item responses. The model parameters are estimated by the Markov chain Monte Carlo (MCMC) algorithm. A simulation study confirmed that the proposed approach could appropriately recover the parameters. Two empirical applications are reported to demonstrate the use of the proposed model and compare it with existing models. The results showed that the proposed model could be a useful tool for jointly modeling the MAFC item responses and response times.

Thermal conductivity of nano- and micro-crystalline diamond films studied by photothermal excitation of cantilever structures

Polycrystalline diamond films have unique structural and thermal properties that make them suitable for use in extreme environments. Recently, they have been utilized in accelerator beamlines for electron stripping due to their unique combination of mechanical and thermal properties. Thermal conductivities of nanocrystalline diamond (NCD) and microcrystalline diamond (μCD) films were characterized using photothermally actuated bimaterial cantilevers. Approximately one micrometer thick NCD and μCD cantilevers were fabricated from microwave plasma enhanced chemical vapor deposition grown polycrystalline diamond (PD) films. A layer of gold was sputtered on the diamond film surfaces to make bilayer cantilevers and the thermal response time was measured by photothermally exciting the bilayer cantilevers, causing them to deflect. Finite element thermomechanical modeling of the deflection dynamics in response to photothermal actuation was performed to determine the NCD and μCD thermal diffusivities and conductivities. By fitting the simulated and experimentally observed response times, thermal conductivities of 10 and 60 W/(m-K) were extracted for the NCD and μCD samples, respectively. Expected changes in thermal conductivity of PD in higher temperature regimes are also discussed. In addition to applications of PD films as electron stripping foils, these findings also have implication in fields such as micro/nano-electromechanical systems.

Acetone sensing and modeling through functionalized Ag-catalysts and CNTs via low cost metal-organic framework-derived ZnO nanostructures

Cost-effective synthesis, portability, quick response and fascinating features of semiconducting metal oxide sensors have drawn much accredited to be utilized in wide variety of potential sensing applications. Herein, flexible acetone sensor was fabricated through spray coating method using different conductive inks of carbon nanotubes (CNTs), zinc oxide (ZnO) and silver (Ag) nanoparticles solutions on cellulose paper. Morphological analysis revealed the fiber like morphology of cellulose over which CNTs, ZnO and Ag nanoparticles are uniformly distributed. The energy dispersive X-ray spectroscopic analysis was performed to observe the elemental composition in the as-prepared samples. The recovery time toward acetone was greater for CNTs/Ag acetone sensor at 95% as compared to CNTs/ZnO/Ag sensor. Additionally, sensing time of the samples towards acetone was measured and the observed response time was greater for CNTs/ZnO/Ag (0.52 s) acetone sensor as compared to CNTs/Ag sensor (0.41 s). Hence, CNTs/ZnO/Ag can be easily employed for sensing applications due to quick response.

Item response and response time model for personality assessment via linear ballistic accumulation

On the basis of a combination of linear ballistic accumulation (LBA) and item response theory (IRT), this paper proposes a new class of item response models, namely LBA IRT, which incorporates the observed response time (RT) by means of LBA. Our main objective is to develop a simple yet effective alternative to the diffusion IRT model, which is one of best-known RT-incorporating IRT models that explicitly models the underlying psychological process of the elicited item response. Through a simulation study, we show that the proposed model enables us to obtain the corresponding parameter estimates compared with the diffusion IRT model while achieving a much faster convergence speed. Furthermore, the application of the proposed model to real personality measurement data indicates that it fits the data better than the diffusion IRT model in terms of its predictive performance. Thus, the proposed model exhibits good performance and promising modeling capabilities in terms of capturing the cognitive and psychometric processes underlying the observed data.

GPUart - An application-based limited preemptive GPU real-time scheduler for embedded systems

Emerging technologies like autonomous driving entail computational intense software solutions. More and more companies accelerate their embedded applications by General Purpose Computing on the Graphics Processing Unit (GPGPU), in order to overcome those computational demands. Unfortunately, Graphics Processing Units (GPUs) severely lack real-time capability, for example controllable preemption support, which limits their applicability in the embedded domain. We therefore present GPUart, a framework for GPU real-time scheduling. GPUart focuses on embedded systems and requires neither hardware nor driver stack extensions. We propose a software-only approach for preemption, based on the fixed preemption point strategy. In contrast to prior work, GPUart enables preemption inside a thread block by adding fixed preemption points. We further propose a portable high-level resource management concept to enable gang scheduling on GPUs. GPUart can schedule GPU workload either under the Gang-Earliest Deadline First (EDF) or Gang-Fixed Task Priority (FTP) policy. A case-study on Nvidia Tegra X1, using real-world engine management applications from Audi AG and Continental Automotive GmbH, shows that only up to 0.28% additional global memory is required to enable interruptible thread blocks. GPUart reduces the worst observed response times by a factor of up to 221, leading to response times without deadline misses.

Fully coupled numerical model of actin treadmilling in the lamellipodium of the cell.

Cells rely on an interplay of subcellular elements for motility and migration. Certain regions of motile cells, such as the lamellipodium, are made of a complex mixture of actin monomers and filaments, which polymerize at the front of the cell, close to the cell membrane, and depolymerize at the rear. The dynamic actin turnover induces the so-called intracellular retrograde flow, and it is a fundamental process for cell motility. Apart from some comprehensive mathematical models, the computational modelling of actin treadmilling has been based on simpler biophysical models. Here, we adopt a highly detailed theoretical model of the actin treadmilling process and develop a coupled unsteady finite element formulation. We clearly describe the structure and implementation of the coupled problem within the finite element method. Our numerical results show an excellent correlation with experimental results from literature and with previous models. We include time dependent effects and convective transport terms, which expose puzzling dynamics in the retrograde flow. We propose several biological scenarios to analyze the behavior of the actin treadmilling along space and time. We observed response times of the main density variables in the order of seconds. Compared with previous analytical solutions, which make assumptions related to convective transport, transient dynamics, and actin fluxes, the generic solution can have significant influence on the retrograde flow. All together, our results unveil a promising applicability of classical finite element methods to derive an in silico testing platform for the actin treadmilling processes in motile cells, which could allow for an extension to other biophysical effects.

Modeling the Covariance Structure of Complex Datasets Using Cognitive Models: An Application to Individual Differences and the Heritability of Cognitive Ability.

Understanding individual differences in cognitive performance is an important part of understanding how variations in underlying cognitive processes can result in variations in task performance. However, the exploration of individual differences in the components of the decision process-such as cognitive processing speed, response caution, and motor execution speed-in previous research has been limited. Here, we assess the heritability of the components of the decision process, with heritability having been a common aspect of individual differences research within other areas of cognition. Importantly, a limitation of previous work on cognitive heritability is the underlying assumption that variability in response times solely reflects variability in the speed of cognitive processing. This assumption has been problematic in other domains, due to the confounding effects of caution and motor execution speed on observed response times. We extend a cognitive model of decision-making to account for relatedness structure in a twin study paradigm. This approach can separately quantify different contributions to the heritability of response time. Using data from the Human Connectome Project, we find strong evidence for the heritability of response caution, and more ambiguous evidence for the heritability of cognitive processing speed and motor execution speed. Our study suggests that the assumption made in previous studies-that the heritability of cognitive ability is based on cognitive processing speed-may be incorrect. More generally, our methodology provides a useful avenue for future research in complex data that aims to analyze cognitive traits across different sources of related data, whether the relation is between people, tasks, experimental phases, or methods of measurement.

A heteroscedastic generalized linear model with a non-normal speed factor for responses and response times.

In generalized linear modelling of responses and response times, the observed response time variables are commonly transformed to make their distribution approximately normal. A normal distribution for the transformed response times is desirable as it justifies the linearity and homoscedasticity assumptions in the underlying linear model. Past research has, however, shown that the transformed response times are not always normal. Models have been developed to accommodate this violation. In the present study, we propose a modelling approach for responses and response times to test and model non‐normality in the transformed response times. Most importantly, we distinguish between non‐normality due to heteroscedastic residual variances, and non‐normality due to a skewed speed factor. In a simulation study, we establish parameter recovery and the power to separate both effects. In addition, we apply the model to a real data set.

Visibility of Two-Wheelers Approaching Left-Turning Vehicles Compared with Other Hazards Under Nighttime Conditions at Urban Signalized Intersections

In the past, researchers have investigated ways to increase the conspicuity of two-wheelers (i.e., motorcycles, mopeds, scooters, and other motorized vehicles) under daytime conditions. However, only a few researchers have analyzed their conspicuity under nighttime conditions. The aim of this study was to evaluate the visibility of two-wheelers encountered by left-turning motorists at urban intersections under nighttime conditions, and compare them with other hazards. A low-fidelity simulator was used to collect data and perform evaluations. The simulator presented a series of videos from a motorist’s perspective, while a passenger vehicle was positioned in the permitted left-turn lane at a signalized intersection. During each video the study participants were instructed to determine whether a left-turn maneuver was safe under the given set of nighttime traffic conditions. The response times for 30 participants were recorded and totaled 627 responses. The observed response times to a two-wheeler were not different from the response times to a passenger vehicle with two headlights. However, the response times were significantly shorter than the times to recognize no hazard or a two-wheeler with no headlight. The conspicuity issues that pertain to two-wheelers in daytime and nighttime conditions were not observed to be the same. Further, the same treatments proposed for two-wheelers in daytime conditions may not be as beneficial or even needed during nighttime driving.

Step Response Characteristics of Polymer/Ceramic Pressure-Sensitive Paint.

Experiments and numerical simulations have been used in this work to understand the step response characteristics of Polymer/Ceramic Pressure-Sensitive Paint (PC-PSP). A recently developed analytical model describing the essential physics in PC-PSP quenching kinetics is used, which includes the effect of both diffusion time scale and luminescent lifetime on the net response of PC-PSP. Step response simulations using this model enables an understanding of the effects of parameters, such as the diffusion coefficient of O2 in the polymer/ceramic coating, attenuation of excitation light, ambient luminescent lifetime, sensitivity, and the magnitude and direction of pressure change on the observed response time scales of PC-PSP. It was found that higher diffusion coefficient and greater light attenuation lead to faster response, whereas longer ambient lifetime and larger sensitivity lead to slower response characteristics. Due to the inherent non-linearity of the Stern-Volmer equation, response functions also change with magnitude and direction of the pressure change. Experimental results from a shock tube are presented where the effects of varying the roughness, pressure jump magnitude and luminophore probe have been studied. Model parameters have been varied to obtain a good fit to experimental results and this optimized model is then used to obtain the response time for a step decrease in pressure, an estimate of which is currently not obtainable from experiments.

Zinc oxide nanocones as potential scaffold for the fabrication of ultra-high sensitive hydrazine chemical sensor

The fabrication and characterization of ultra-high sensitive hydrazine chemical sensor based on zinc oxide (ZnO) nanocones are reported. The ZnO nanocones were synthesized by a low-temperature solution process and examined by various techniques in terms of their morphological, structural, compositional and optical properties. The studies revealed that the synthesized ZnO materials possessed good-crystallinity and defined nanocone morphologies and exhibited good optical properties. The hydrazine chemical sensor was fabricated on the amperometric principle and shows ultra-high sensitivity and low detection limit. The estimated sensitivity and detection limit of the fabricated hydrazine chemical sensor were 50×104µAµM−1cm−2 and 0.01µM, respectively. The observed response time was <2s.