Response Time Model Research Articles

Correction to “Expanding the Lognormal Response Time Model Using Profile Similarity Metrics to Improve the Detection of Anomalous Testing Behavior”

Correction to “Expanding the Lognormal Response Time Model Using Profile Similarity Metrics to Improve the Detection of Anomalous Testing Behavior”

On the Choice of Parameters for the Lognormal Model for Response Times: Commentary on Becker et al. (2013)

AbstractIn a recently published article in this journal, Becker et al. claim that, because of a missing slope parameter, the lognormal model for response times on test items almost never holds in practice. However, the authors' critique rests on a misrepresentation of the model, which already does have the equivalent of a slope parameter. More importantly, their extra parameter spoils the interpretation of the parameters for the test‐takers' speed and labor intensity of the items necessary for a response‐time model to be empirically meaningful while their proposed interpretation of the extra parameter seems unwarranted. An analysis of the authors' earlier empirical comparison between the original and their alternative version of the model does not seem to support much of a conclusion about the relative fit of the two models. Also, their simulation study conducted to demonstrate the necessity of the extra slope parameter appears to be based on data simulated in favor of their parameter.

The neural network RTNet exhibits the signatures of human perceptual decision-making.

Convolutional neural networks show promise as models of biological vision. However, their decision behaviour, including the facts that they are deterministic and use equal numbers of computations for easy and difficult stimuli, differs markedly from human decision-making, thus limiting their applicability as models of human perceptual behaviour. Here we develop a new neural network, RTNet, that generates stochastic decisions and human-like response time (RT) distributions. We further performed comprehensive tests that showed RTNet reproduces all foundational features of human accuracy, RT and confidence and does so better than all current alternatives. To test RTNet's ability to predict human behaviour on novel images, we collected accuracy, RT and confidence data from 60 human participants performing a digit discrimination task. We found that the accuracy, RT and confidence produced by RTNet for individual novel images correlated with the same quantities produced by human participants. Critically, human participants who were more similar to the average human performance were also found to be closer to RTNet's predictions, suggesting that RTNet successfully captured average human behaviour. Overall, RTNet is a promising model of human RTs that exhibits the critical signatures of perceptual decision-making.

Expanding the Lognormal Response Time Model Using Profile Similarity Metrics to Improve the Detection of Anomalous Testing Behavior

AbstractThe Lognormal Response Time (LNRT) model measures the speed of test‐takers relative to the normative time demands of items on a test. The resulting speed parameters and model residuals are often analyzed for evidence of anomalous test‐taking behavior associated with fast and poorly fitting response time patterns. Extending this model, we demonstrate the connection between the existing LNRT model parameters and the “level” component of profile similarity, and we define two new parameters for the LNRT model representing profile “dispersion” and “shape.” We show that while the LNRT model measures level (speed), profile dispersion and shape are conflated in model residuals, and that distinguishing them provides meaningful and useful parameters for identifying anomalous testing behavior. Results from data in a situation where many test‐takers gained preknowledge of test items revealed that profile shape, not currently measured in the LNRT model, was the most sensitive response time index to the abnormal test‐taking behavior patterns. Results strongly support expanding the LNRT model to measure not only each test‐taker's level of speed, but also the dispersion and shape of their response time profiles.

Multi-objective cooperation optimization research on dynamic response and energy loss of high-speed solenoid valve for diesel engine injector

The dynamic response and operational reliability of high-speed solenoid valve (HSV) for diesel engine injector are the main indicators to measure their performance. At high-frequency, the eddy current energy and Joule energy generated by the HSV will be converted into heat, which has a significant impact on the service life of HSV. The optimization of HSV involves the interaction between energy loss and the dynamic response of HSV. To optimize the HSV dynamic response time considering energy loss, the HSV work process simulation model was established in this paper, and the model was verified based on armature lift experimental data. Without changing the structural parameters of the HSV, the four parameters of electroconductibility, spring stiffness, damping coefficient, and coil resistance were selected as the key parameters affecting the dynamic response and energy loss. The response surface models (RSMs) of opening response time, closing response time, eddy current energy and Joule energy of the HSV were constructed by using the smoothing spline-analysis of variance method. The multi-objective cooperation optimization of HSV under the interaction of dynamic response characteristics and energy loss was completed by using non-dominated sorting genetic algorithms. After optimization, the opening and closing response times of HSV were reduced by 15.1% and 16.6% respectively, while the eddy current energy and Joule energy were reduced by 5.2% and 48.4% respectively. In this paper, the dynamic response and energy loss were jointly optimized. The presented results provide theory instruction for multi-objective cooperative optimization of HSV.

Research on Multi-Objective Optimization of High-Speed Solenoid Valve Drive Strategies under the Synergistic Effect of Dynamic Response and Energy Loss

Under high-frequency operating conditions, the high-speed solenoid valve (HSV) experiences energy loss and heat generation, which significantly impacts its operational lifetime. Reducing the energy loss of an HSV without compromising its opening response characteristics poses a significant challenge. To address this issue, a finite element simulation model of an HSV coupled with a current feedback model is constructed to investigate the synergistic effects of dynamic response and energy loss. Prediction models for the opening response time, HSV driving energy, and Joule energy using a back propagation neural network (BPNN) are established. Furthermore, a multi-objective optimization study on the current driving strategy using a non-dominated sorting genetic algorithm II (NSGA-II) is conducted. After optimization, although there was a 6.24% increase in the opening response time, both HSV drive energy and Joule energy were significantly reduced by 15.67% and 22.49%, respectively. The proposed multi-objective optimization method for an HSV driving strategy holds great significance for improving its working durability.

Frequency stability control method for multi-energy system considering storage characteristics and transmission inertia of hot and gas pipeline network

The grid-connected capacity of renewable energy generation in multi-energy microgrid is increasing. This leads to a decrease in the inertia level in the microgrid, which has a great impact on the frequency stabilization control. This article proposes an adaptive control method for frequency control of inertia in multi-energy microgrids. Firstly, the system frequency fluctuation problem is addressed. Analyze the response characteristics of virtual inertia and the influence of physical inertia of rotating equipment on system frequency dynamics in multi-energy microgrid. Secondly, study the energy transfer characteristics of electric, thermal and gas systems in multi-energy microgrids. The energy coupling model between the subsystems in the multi-energy microgrid is established. And according to the difference of energy transmission time inertia of electric, heat and gas subsystems, the microgrid inertia response time model of different energy systems is established. Then, according to the energy balance stability criterion of multi-energy microgrid, combined with the current operating state of the system and the state of the higher-level distribution network, the fast response adaptive over-compensation control of multi-energy microgrid cluster is carried out to realize the inertia allocation in multi-energy microgrids. Finally, the proposed advanced frequency control method of multi-energy microgrid considering inertia demand is verified by simulation.

A response time model of the three-choice Mnemonic Similarity Task provides stable, mechanistically interpretable individual-difference measures.

The Mnemonic Similarity Task (MST) is a widely used measure of individual tendency to discern small differences between remembered and presently presented stimuli. Significant work has established this measure as a reliable index of neurological and cognitive dysfunction and decline. However, questions remain about the neural and psychological mechanisms that support performance in the task. Here, we provide new insights into these questions by fitting seven previously-collected MST datasets (total N = 519), adapting a three-choice evidence accumulation model (the Linear Ballistic Accumulator). The model decomposes choices into automatic and deliberative components. We show that these decomposed processes both contribute to the standard measure of behavior in this task, as well as capturing individual variation in this measure across the lifespan. We also exploit a delayed test/re-test manipulation in one of the experiments to show that model parameters exhibit improved stability, relative to the standard metric, across a 1 week delay. Finally, we apply the model to a resting-state fMRI dataset, finding that only the deliberative component corresponds to off-task co-activation in networks associated with long-term, episodic memory. Taken together, these findings establish a novel mechanistic decomposition of MST behavior and help to constrain theories about the cognitive processes that support performance in the task.

An extension of the shifted Wald model of human response times: Capturing the time dynamic properties of human cognition : Trial-varying Wald model.

Despite the ubiquitous nature of evidence accumulation models in cognitive and experimental psychology, there has been a comparatively limited uptake of such techniques in the applied literature. While quantifying latent cognitive processing properties has significant potential for applied domains such as adaptive work systems, accumulator models often fall short in practical applications. Two primary reasons for these shortcomings are the complexities and time needed for the application of cognitive models, and the failure of current models to capture systematic trial-to-trial variability in parameters. In this manuscript, we develop a novel, trial-varying extension of the shifted Wald model to address these concerns. By leveraging conjugate properties of the Wald distribution, we derive computationally efficient solutions for threshold and drift parameters which can be updated instantaneously with new data. The resulting model allows the quantification of systematic variation in latent cognitive parameters across trials and we demonstrate the utility of such analyses through simulations and an exemplar application to an existing data set. The analytic nature of our solutions opens the door for real-world applications, significantly extending the reach of computational models of behavioral responses.

What Can We Learn from a Semiparametric Factor Analysis of Item Responses and Response Time? An Illustration with the PISA 2015 Data.

It is widely believed that a joint factor analysis of item responses and response time (RT) may yield more precise ability scores that are conventionally predicted from responses only. For this purpose, a simple-structure factor model is often preferred as it only requires specifying an additional measurement model for item-level RT while leaving the original item response theory (IRT) model for responses intact. The added speed factor indicated by item-level RT correlates with the ability factor in the IRT model, allowing RT data to carry additional information about respondents' ability. However, parametric simple-structure factor models are often restrictive and fit poorly to empirical data, which prompts under-confidence in the suitablity of a simple factor structure. In the present paper, we analyze the 2015 Programme for International Student Assessment mathematics data using a semiparametric simple-structure model. We conclude that a simple factor structure attains a decent fit after further parametric assumptions in the measurement model are sufficiently relaxed. Furthermore, our semiparametric model implies that the association between latent ability and speed/slowness is strong in the population, but the form of association is nonlinear. It follows that scoring based on the fitted model can substantially improve the precision of ability scores.

Simultaneous modeling of choice, confidence, and response time in visual perception.

How can choice, confidence, and response times be modeled simultaneously? Here, we propose the new dynamical weighted evidence and visibility (dynWEV) model, an extension of the drift-diffusion model of decision-making, to account for choices, reaction times, and confidence simultaneously. The decision process in a binary perceptual task is described as a Wiener process accumulating sensory evidence about the choice options bounded by two constant thresholds. To account for confidence judgments, we assume a period of postdecisional accumulation of sensory evidence and parallel accumulation of information about the reliability of the present stimulus. We examined model fits in two experiments, a motion discrimination task with random dot kinematograms and a postmasked orientation discrimination task. A comparison between the dynWEV model, two-stage dynamical signal detection theory, and several versions of race models of decision-making showed that only dynWEV produced acceptable fits of choices, confidence, and reaction time. This finding suggests that confidence judgments depend not only on choice evidence but also on a parallel estimate of stimulus discriminability and postdecisional accumulation of evidence. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Open system model of choice and response time

Sequential sampling models have provided accurate accounts of people’s choice, response time, and preference strength in value-based decision-making tasks. Conventionally, these models are developed as Markov-type models (such as random walks or diffusion models) following the Kolmogorov axioms. Quantum probability theory has been proposed as an alternative framework upon which to develop models of cognition, including quantum random walk models. When modeling people’s behavior during decision-making tasks, previous work has demonstrated that both the Markov and quantum models have their respective strengths. Recently, the open system model, which is a hybrid version of the Markov and quantum models, has been shown to provide a more accurate account of preference strength compared to the Markov and quantum models in isolation. In this work, we extend the open system model to make predictions on pairwise choice and response time. We report a new experiment on preferential choice between gift cards, and we compare the fits of the open system model to the pure Markov and pure quantum random walk models using AIC, BIC, and χ2 tests. Although the pure Markov model was favored for most participants, a substantial number required the more complex open system model.

Using item scores and response times in person-fit assessment.

The use of joint models for item scores and response times is becoming increasingly popular in educational and psychological testing. In this paper, we propose two new person-fit statistics for such models in order to detect aberrant behaviour. The first statistic is computed by combining two existing person-fit statistics: one for the item scores, and one for the item response times. The second statistic is computed directly using the likelihood function of the joint model. Using detailed simulations, we show that the empirical null distributions of the new statistics are very close to the theoretical null distributions, and that the new statistics tend to be more powerful than several existing statistics for item scores and/or response times. A real data example is also provided using data from a licensure examination.

Collaborative on-demand dynamic deployment via deep reinforcement learning for IoV service in multi edge clouds

In vehicular edge computing, the low-delay services are invoked by the vehicles from the edge clouds while the vehicles moving on the roads. Because of the insufficiency of computing capacity and storage resource for edge clouds, a single edge cloud cannot handle all the services, and thus the efficient service deployment strategy in multi edge clouds should be designed according to the service demands. Noticed that the service demands are dynamic in temporal, and the inter-relationship between services is a non-negligible factor for service deployment. In order to address the new challenges produced by these factors, a collaborative service on-demand dynamic deployment approach with deep reinforcement learning is proposed, which is named CODD-DQN. In our approach, the number of service request of each edge clouds are forecasted by a time-aware service demands prediction algorithm, and then the interacting services are discovered through the analysis of service invoking logs. On this basis, the service response time models are constructed to formulated the problem, aiming to minimize service response time with data transmission delay between services. Furthermore, a collaborative service dynamic deployment algorithm with DQN model is proposed to deploy the interacting services. Finally, the real-world dataset based experiments are conducted. The results show our approach can achieve lowest service response time than other algorithms for service deployment.

Latent Trait Item Response Models for Continuous Responses

A general framework of latent trait item response models for continuous responses is given. In contrast to classical test theory (CTT) models, which traditionally distinguish between true scores and error scores, the responses are clearly linked to latent traits. It is shown that CTT models can be derived as special cases, but the model class is much wider. It provides, in particular, appropriate modeling of responses that are restricted in some way, for example, if responses are positive or are restricted to an interval. Restrictions of this sort are easily incorporated in the modeling framework. Restriction to an interval is typically ignored in common models yielding inappropriate models, for example, when modeling Likert-type data. The model also extends common response time models, which can be treated as special cases. The properties of the model class are derived and the role of the total score is investigated, which leads to a modified total score. Several applications illustrate the use of the model including an example, in which covariates that may modify the response are taken into account.

A Multidimensional Partially Compensatory Response Time Model on Basis of the Log-Normal Distribution

In the existing multidimensional extensions of the log-normal response time (LNRT) model, the log response times are decomposed into a linear combination of several latent traits. These models are fully compensatory as low levels on traits can be counterbalanced by high levels on other traits. We propose an alternative multidimensional extension of the LNRT model by assuming that the response times can be decomposed into two response time components. Each response time component is generated by a one-dimensional LNRT model with a different latent trait. As the response time components—but not the traits—are related additively, the model is partially compensatory. In a simulation study, we investigate the recovery of the model’s parameters. We also investigate whether the fully and the partially compensatory LNRT model can be distinguished empirically. Findings suggest that parameter recovery is good and that the two models can be distinctly identified under certain conditions. The utility of the model in practice is demonstrated with an empirical application. In the empirical application, the partially compensatory model fits better than the fully compensatory model.

Modeling and Reconstruction of Response Times on Single Board Computers with RT-Linux

Modeling and Reconstruction of Response Times on Single Board Computers with RT-Linux

A Latent Space Diffusion Item Response Theory Model to Explore Conditional Dependence between Responses and Response Times.

Traditional measurement models assume that all item responses correlate with each other only through their underlying latent variables. This conditional independence assumption has been extended in joint models of responses and response times (RTs), implying that an item has the same item characteristics fors all respondents regardless of levels of latent ability/trait and speed. However, previous studies have shown that this assumption is violated in various types of tests and questionnaires and there are substantial interactions between respondents and items that cannot be captured by person- and item-effect parameters in psychometric models with the conditional independence assumption. To study the existence and potential cognitive sources of conditional dependence and utilize it to extract diagnostic information for respondents and items, we propose a diffusion item response theory model integrated with the latent space of variations in information processing rate of within-individual measurement processes. Respondents and items are mapped onto the latent space, and their distances represent conditional dependence and unexplained interactions. We provide three empirical applications to illustrate (1) how to use an estimated latent space to inform conditional dependence and its relation to person and item measures, (2) how to derive diagnostic feedback personalized for respondents, and (3) how to validate estimated results with an external measure. We also provide a simulation study to support that the proposed approach can accurately recover its parameters and detect conditional dependence underlying data.

Speeded response tasks with unpredictable deadlines

In response time (RT) research, it is common to instruct participants to respond as fast and as accurately as possible, which is easily conceived as a contradiction. Participants may overcome this dilemma using a two-fold strategy, with (A) delaying their response until they feel confident that enough information has been sampled, and (B) scheduling the response right before the end of the response window to avoid omissions. The purpose of this strategy is to satisfy the contradictory requirements of the task instructions, but both (A) and (B) may yield a distorted picture of the processing times under investigation. We asked participants to discriminate random dot motion with fixed and variable deadlines for responding. With the exponentially distributed variable deadline, strategic responding is useless because it is impossible to schedule an optimal time point for the response. We present two analyses, a model-free approach that investigates the effect of an unpredictable deadline on standard RT measures, and the fit of an RT model testing for effects of the deadline on specific parameters. Compared to the fixed deadline, faster responses that were less variable across participants were observed under the variable deadline, suggesting that the new paradigm can reduce strategic responding. We demonstrate how to deal with omitted responses and conclude that variable deadlines are a promising tool to exert time pressure in RT experiments and potentially yield better estimates of the underlying processing times.

Controlling the Speededness of Assembled Test Forms: A Generalization to the Three‐Parameter Lognormal Response Time Model

AbstractWhen designing or modifying a test, an important challenge is controlling its speededness. To achieve this, van der Linden (2011a, 2011b) proposed using a lognormal response time model, more specifically the two‐parameter lognormal model, and automated test assembly (ATA) via mixed integer linear programming. However, this approach has a severe limitation, in that the two‐parameter lognormal model lacks a slope parameter. This means that the model assumes that all items are equally speed sensitive. From a conceptual perspective, this assumption seems very restrictive. Furthermore, various other empirical studies and new data analyses performed by us show that this assumption almost never holds in practice. To overcome this shortcoming, we bring together the already frequently used three‐parameter lognormal model for response times, which contains a slope parameter, and the ATA approach for controlling speededness by van der Linden. Using multiple empirically based illustrations, the proposed extension is illustrated, including complete and documented R code. Both the original van der Linden approach and our newly proposed approach are available to practitioners in the freely available R package eatATA.