Response Time Model Research Articles

Extending systems factorial technology to errored responses.

Systems factorial technology (SFT) is a theoretically derived methodology that allows for strong inferences to be made about underlying processing architectures (e.g., whether processing occurs in a pooled, coactive fashion or in serial or in parallel). Measures of mental architecture using SFT have been restricted to the use of error-free response times (RTs). In this article, through formal proofs and demonstrations, we extended the measure of architecture, the survivor interaction contrast (SIC), to RTs conditioned on whether they are correct or incorrect. We show that so long as an ordering relation (between stimulus conditions of different difficulty) is preserved, we learn that the canonical SIC predictions result when exhaustive processing is necessary and sufficient for a response. We further prove that this ordering relation holds for the popular Wiener diffusion model for both correct and error RTs but fails under some classes of a Poisson counter model, which affords a strong potential experimental test of the latter class versus the others. Our exploration also serves to point to the importance of detailed studies of how errors are made in perceptual and cognitive tasks. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Generating Models for Item Preknowledge

AbstractDetection methods for item preknowledge are often evaluated in simulation studies where models are used to generate the data. To ensure the reliability of such methods, it is crucial that these models are able to accurately represent situations that are encountered in practice. The purpose of this article is to provide a critical analysis of common models that have been used to simulate preknowledge. Both response accuracy (RA) and response time (RT) models are considered. The justifications and supporting evidence for each model are evaluated using three real data sets, and the impact of generating model on detection power is examined in two simulation studies.

Response time modelling reveals evidence for multiple, distinct sources of moral decision caution

People are often cautious in delivering moral judgements of others' behaviours, as falsely accusing others of wrongdoing can be costly for social relationships. Caution might further be present when making judgements in information-dynamic environments, as contextual updates can change our minds. This study investigated the processes with which moral valence and context expectancy drive caution in moral judgements. Across two experiments, participants (N = 122) made moral judgements of others' sharing actions. Prior to judging, participants were informed whether contextual information regarding the deservingness of the recipient would follow. We found that participants slowed their moral judgements when judging negatively valenced actions and when expecting contextual updates. Using a diffusion decision model framework, these changes were explained by shifts in drift rate and decision bias (valence) and boundary setting (context), respectively. These findings demonstrate how moral decision caution can be decomposed into distinct aspects of the unfolding decision process.

PathTracer: Understanding Response Time of Signal Processing Applications on Heterogeneous MPSoCs

In embedded and cyber-physical systems, the design of a desired functionality under constraints increasingly requires parallel execution of a set of tasks on a heterogeneous architecture. The nature of such parallel systems complicates the process of understanding and predicting performance in terms of response time. Indeed, response time depends on many factors related to both the functionality and the target architecture. State-of-the-art strategies derive response time by examining the operations required by each task for both processing and accessing shared resources. This procedure is often followed by the addition or elimination of potential interference due to task concurrency. However, such approaches require an advanced knowledge of the software and hardware details, rarely available in practice. This work presents an alternative “top-down” strategy, called PathTracer, aimed at understanding software response time and extending the cases in which it can be analyzed and estimated. PathTracer leverages on dataflow-based application representation and response time estimation of signal processing applications mapped on heterogeneous Multiprocessor Systems-on-a-Chip (MPSoCs). Experimental results demonstrate that PathTracer provides (i) information on the nature of the application (work-dominated, span-dominated, or balanced parallel), and (ii) response time modeling which can reach high accuracy when performed post-execution, leading to prediction errors with average and standard deviation under 5% and 3% respectively.

Mitigating Cold Start Problem in Serverless Computing with Function Fusion.

As Artificial Intelligence (AI) is becoming ubiquitous in many applications, serverless computing is also emerging as a building block for developing cloud-based AI services. Serverless computing has received much interest because of its simplicity, scalability, and resource efficiency. However, due to the trade-off with resource efficiency, serverless computing suffers from the cold start problem, that is, a latency between a request arrival and function execution. The cold start problem significantly influences the overall response time of workflow that consists of functions because the cold start may occur in every function within the workflow. Function fusion can be one of the solutions to mitigate the cold start latency of a workflow. If two functions are fused into a single function, the cold start of the second function is removed; however, if parallel functions are fused, the workflow response time can be increased because the parallel functions run sequentially even if the cold start latency is reduced. This study presents an approach to mitigate the cold start latency of a workflow using function fusion while considering a parallel run. First, we identify three latencies that affect response time, present a workflow response time model considering the latency, and efficiently find a fusion solution that can optimize the response time on the cold start. Our method shows a response time of 28–86% of the response time of the original workflow in five workflows.

Explanatory Cognitive Diagnostic Modeling Incorporating Response Times

AbstractThis study proposes explanatory cognitive diagnostic model (CDM) jointly incorporating responses and response times (RTs) with the inclusion of item covariates related to both item responses and RTs. The joint modeling of item responses and RTs intends to provide more information for cognitive diagnosis while item covariates can be used to predict item parameters when item calibration is not feasible in diagnostic assessments or item parameter estimation errors could be too large due to small sample sizes for calibration. In addition, the inclusion of the item covariates allows the evaluation of cognitive theories underlying the test design in item development. Model parameter estimation is explored using the Bayesian Markov chain Monte Carlo (MCMC) method. A Monte Carlo simulation study is conducted to examine the parameter recovery of the proposed model under different simulated conditions in comparison to alternative competing models. Further, the application of the proposed model is illustrated using the Programme for International Student Assessment (PISA) 2012 problem‐solving items modeling both item response and RT data. The study results indicate that model parameters can be well recovered using the MCMC algorithm and the explanatory CDM jointly incorporating item responses and RTs with item covariates holds promising applications in digital‐based diagnostic assessments.

Evaluating user cognition of network diagrams

Edges crossing and nodes overlapping have a significant effect on the users’ recognition and comprehension of network diagrams. In this study, we propose a visual evaluation method for users’ cognition of network diagrams. First, this method carries out a set of cognitive experiments to collect the user’s cognitive performance that affects the variables, including accuracy and response time. The user’s pupil diameter is measured through an eye tracker to reflect their cognitive load. Second, the significance test points out the visual features as independent variables and then establishes an evaluation regression model. The experimental results show that the number of edges, edge length, node visual interference, and edge occlusion contribute to the evaluation models of response time, and edge occlusion and the number of node connections contribute to the accuracy model. Finally, these evaluation models demonstrate good predictability when assessing users’ cognition of network diagrams and provide practical recommendations for their use.

Modeling electrophysiological measures of decision-making and performance monitoring in neurotypical children engaging in a speeded flanker task.

This study aims to use structural equation modeling (SEM) to investigate the role of error processing in behavioral adaptation in children by testing relationships between error-related and stimulus-related event-related potentials (ERPs) obtained from two sessions of a speeded Eriksen flanker task. First, path models of averaged ERP components and mean response times (N1→P2→N2→P3→RTs) while controlling for trait effects, age, and sex, on each was examined separately for correct and incorrect trials from each session. While the model demonstrated acceptable fit statistics, the four models yielded diverse results. Next, path models for correct and incorrect trials were tested using latent variables defined by factoring together respective measures of ERP component amplitudes from each session. Comparison of correct and incorrect models revealed significant differences in the relationships between the successive measures of neural processing after controlling for trait effects. Moreover, latent variable models controlling for both trait and session-specific state variables yielded excellent model fit while models without session-specific state variables did not. In the final model, the error-related neural activity (i.e., the ERN and Pe) from incorrect trials was found to significantly relate to the stream of neural processes contributing to trials with the correct behavior. Importantly, the relationship between RT and error detection in the final model signifies a brain-and-behavior feedback loop. These findings provided empirical evidence that supports the adaptive orienting theory of error processing by demonstrating how the neural signals of error processing influence behavioral adaptations that facilitate correct behavioral performance.

On Joining a Signal Detection Choice Model with Response Time Models

AbstractIn a signal detection theory (SDT) approach to multiple choice exams, examinees are viewed as choosing, for each item, the alternative that is perceived as being the most plausible, with perceived plausibility depending in part on whether or not an item is known. The SDT model is a process model and provides measures of item difficulty, item discrimination, and the relative plausibility of each alternative. It is shown how to incorporate information from response times into the model, which has potential benefits for estimation and also offers a way to study underlying processes. The SDT model is joined with a lognormal response time (RT) model in a manner similar to that used in hierarchical models. In addition, a mixture extension of the RT model is joined in a novel way with the SDT choice model, using the idea that the probability of “knowing” in the SDT model might be related to the probability of working in one of two speed states in the mixture RT model. A semiparametric version of the mixture RT model is also used to assess robustness. The fused SDT/RT models are examined with science items from the 2015 Program for International Student Assessment.

Research on Emergency Power Support Method after Distributed Energy Storage System Blocking UHV DC

In order to explore the emergency support effect of distributed energy storage on reducing the peak power flow of AC transmission lines and improving the operating power of DC transmission lines after UHV DC locking fault, based on the analysis of UHV DC locking fault, the active power is provided according to the distributed energy storage system. A mathematical model is established to reduce the peak power flow of AC transmission lines by distributed energy storage after DC locking. According to the relationship between power gap and frequency variation, a mathematical model of distributed energy storage to improve UHV DC operating power is established. Based on frequency variation and inertial response time, a mathematical model of dynamic response time of energy storage output on system support effect was established. Taking Henan Power Grid of UHV AC/DC hybrid operation as an example, the simulation analysis of distributed energy storage system response in two ways is carried out, which verifies the effectiveness of distributed energy storage system for emergency power support method after UHV DC locking.

TalkBank의 음성 말뭉치를 활용한 대화 참여자의 상호작용 연구

This paper analyzes speaker dynamics in conversational exchanges between mothers and their young children based on open corpus data in the TalkBank system. In particular, we analyze four mother-infant dyads in the Providence corpus with focus on the interaction of the initiator of Conversational Block and the Conversational Turn with regard to variables such as the turn length and the response time. We show that both the mother and child speak longer in conversational blocks that they have initiated, which is consistent with the findings in Ko et al. (2016) based on data collected and processed by the LENA^® system. The model for response time, however, did not yield a significant interaction despite some indications towards such a tendency. The methodology for conducting such analyses using the corpus data in TalkBank is described in detail so that it could serve as a reference for research adopting similar procedures.

A Study on Federated Cloud Computing Environment

Federated cloud computing is the advancement in the area of the general cloud computing paradigm. In a federated cloud environment, multiple cloud service providers share their computing assets, servers, and various facilities to fulfill customer demands. Federated cloud computing terminology consists of the aggregation of services considered by interoperability characteristics and creates the integration of several cloud service providers regardless of any geographical location. It improves the performance, utilization of facilities, minimizes response time and pricing model by partial subcontracting various computing resources and facilities from the nearby cost-efficient province. Customers also get profited from service level agreements signed between the cloud service providers through intermediator cloud brokers. This work aims to survey the federated cloud environment, its various architectural types, advantages associated with the federation, challenges associated with a federated cloud environment, and future research directions in the federated cloud computing research area.

A Geomorphic Examination of the Calabrian Forearc Translation

AbstractThe evolution of subduction forearcs as archived in the tectonic and geomorphic record serves to constrain geodynamic models of many convergent plate margins. This paper presents geomorphic stream channel metrics and river longitudinal profile modeling to assess geodynamic models describing the late Cenozoic horizontal translation of a Mediterranean forearc. The northern flank of Sicily is drained by roughly parallel north‐flowing streams arranged across the hypothesized west‐to‐east migration path of the Calabrian forearc high. These streams present a snapshot of current geomorphic processes driven by spatiotemporal variations in crustal deformation, including river incision and landscape steepness. A stream power rule‐based numerical modeling of the fluvial response time and knickpoint migration provides an independent prediction for the history of rock uplift associated with the translating forearc over the past ∼4 Myr. The results serve as a test for two, crustal wave versus transform‐fault, end‐member geodynamic models for the Calabrian forearc evolution, suggesting that our modeling supports the former. The river profile modeling shows that northern Sicily rivers experienced two pulses of time‐transgressive rock uplift that progressively sweeps west to east. This analysis suggests the most rapid contemporary rock uplift rates are focused in NE‐Sicily, where the forearc is currently located and where Pleistocene marine terraces indicate the highest rock uplift rates. These results are consistent with the west‐to‐east passage of a forearc high embedded in the subduction wedge, followed by a dynamic wave of rock uplift in its wake similar to what has been proposed for slab windows in other plate boundary settings.

Cyber threat Detection and Response Time Modeling

Cyber threat Detection and Response Time Modeling

Variation in Respondent Speed and its Implications: Evidence from an Adaptive Testing Scenario

AbstractThe more frequent collection of response time data is leading to an increased need for an understanding of how such data can be included in measurement models. Models for response time have been advanced, but relatively limited large‐scale empirical investigations have been conducted. We take advantage of a large data set from the adaptive NWEA MAP Growth Reading Assessment to shed light on emergent features of response time behavior. We identify two behaviors in particular. The first, response acceleration, is a reduction in response time for responses that occur later in the assessment. We note that such reductions are heterogeneous as a function of estimated ability (lower ability estimates are associated with larger increases in acceleration) and that reductions in response time lead to lower accuracy relative to expectation for lower ability students. The second is within‐person variation in the association between time usage and accuracy. Idiosyncratic within‐person changes in response time have inconsistent implications for accuracy; in some cases additional response time predicts higher accuracy but in other cases additional response time predicts declines in accuracy. These findings have implications for models that incorporate response time and accuracy. Our approach may be useful in other studies of adaptive testing data.

On the Speed Sensitivity Parameter in the Lognormal Model for Response Times and Implications for High-Stakes Measurement Practice.

In high-stakes testing, often multiple test forms are used and a common time limit is enforced. Test fairness requires that ability estimates must not depend on the administration of a specific test form. Such a requirement may be violated if speededness differs between test forms. The impact of not taking speed sensitivity into account on the comparability of test forms regarding speededness and ability estimation was investigated. The lognormal measurement model for response times by van der Linden was compared with its extension by Klein Entink, van der Linden, and Fox, which includes a speed sensitivity parameter. An empirical data example was used to show that the extended model can fit the data better than the model without speed sensitivity parameters. A simulation was conducted, which showed that test forms with different average speed sensitivity yielded substantial different ability estimates for slow test takers, especially for test takers with high ability. Therefore, the use of the extended lognormal model for response times is recommended for the calibration of item pools in high-stakes testing situations. Limitations to the proposed approach and further research questions are discussed.

Understanding Speed-Accuracy Processing Dynamics in Aphasia Using Response Time Modeling.

People with aphasia demonstrate language impairments evident in both performance accuracy and processing speed, but the direct relationship between accuracy and speed requires further consideration. This article describes two recent attempts to make quantitative progress in this domain using response time modeling: the diffusion model (Ratcliff, 1978) applied to two-choice tasks and a multinomial ex-Gaussian model applied to picture naming. The diffusion model may be used to characterize core linguistic processing efficiency and speed-accuracy tradeoffs independently, and research suggests that maladaptive speed-accuracy tradeoffs lead to performance impairments in at least some people with aphasia. The multinomial ex-Gaussian response time model of picture naming provides a simple and straightforward way to estimate the optimal response time cutoffs for individual people with aphasia (i.e., the cutoff where additional time is unlikely to lead to a correct response). While response time modeling applied to aphasia research is at an early stage of development, both the diffusion model and multinomial ex-Gaussian response time model of picture naming show promise and should be further developed in future work. This article also provides preliminary recommendations for clinicians regarding how to conceptualize, identify, and potentially address maladaptive speed-accuracy tradeoffs for people with aphasia.

Reward-Seeking Deficits in Major Depression: Unpacking Appetitive Task Performance with Ex-Gaussian Response Time Variability Analysis.

Major depressive disorder (MDD) has extensive ties to motivation, including impaired response time (RT) performance. Average RT, however, conflates response speed and variability, so RT differences can be complex. Because recent studies have shown inconsistent effects of MDD on RT variability, the present research sought to unpack RT performance with several key improvements: (1) a sample of adults (n = 78; 18 MDD, 60 Control) free of antidepressant medication; (2) an unambiguously appetitive task with appealing incentives at stake; and (3) ex-Gaussian RT modeling, which can unconfound speed and variability by estimating parameters for the mean (Mu) and standard deviation (Sigma) of the normal component and the mean of the exponential component (Tau). The groups had comparable Mu and Sigma parameters, but the MDD group had a significantly larger Tau, reflecting greater intraindividual RT variability. The findings suggest that MDD's effect on average RT can stem from greater intraindividual variability, not from overall slowness. Possible mechanisms, such as impaired executive processes in MDD and difficulties maintaining stable mental representations of incentives, are considered.

Learning Deep Generative Models for Queuing Systems

Modern society is heavily dependent on large scale client-server systems with applications ranging from Internet and Communication Services to sophisticated logistics and deployment of goods. To maintain and improve such a system, a careful study of client and server dynamics is needed – e.g. response/service times, aver-age number of clients at given times, etc. To this end, one traditionally relies, within the queuing theory formalism,on parametric analysis and explicit distribution forms.However, parametric forms limit the model’s expressiveness and could struggle on extensively large datasets. We propose a novel data-driven approach towards queuing systems: the Deep Generative Service Times. Our methodology delivers a flexible and scalable model for service and response times. We leverage the representation capabilities of Recurrent Marked Point Processes for the temporal dynamics of clients, as well as Wasserstein Generative Adversarial Network techniques, to learn deep generative models which are able to represent complex conditional service time distributions. We provide extensive experimental analysis on both empirical and synthetic datasets, showing the effectiveness of the proposed models

Optimising Subsea Well Source Control Timeframes

ABSTRACT In response to the Montara and Macondo subsea well incidents in 2009–10, the industry's knowledge of and ability to respond to a subsea source control (SSC) event has greatly improved. Industry has invested heavily in its response capabilities and established best practices to resolve future incidents that may arise in the offshore oil and gas operations. The investment has driven rapid advancements in science, engineering, and new technological equipment developments to establish a higher standard for SSC preparedness and readiness. The industry now has a high confidence in its ability to deal with a subsea well release. The growth in capability has led to many variations in equipment and response plans, which has led to complexity in an already highly technical field. To reduce the complexity, common understanding is required of all the actions that comprise a SSC response, the linkages and dependencies between all the actions, and the critical path items that influence the overall timeframes of regaining control of the well. With a common understanding of the response plan comes enhanced industry, regulator and community confidence in the ability of the oil and gas industry to appropriately manage its environmental and social impacts. To help with this effort, the International Association of Oil and Gas Producers (IOGP) has produced reports 592, 594 and 595. Report 594 is a guideline that can be used to support subsea source control response planning and Report 595 addresses capping stack design and operational reliability. IOGP Report 592 - Subsea Capping Response Time Model Toolkit User Guide, was completed in December 2019. It was jointly developed by IOGP and the Australian National Offshore Petroleum Safety and Environmental Authority (NOPSEMA). This report involved the creation of a digital subsea response time model that is freely available with a number of different software templates. The objective was to create a common standardized document that described the processes for preparing and implementing a subsea well blowout response in a timeline format, and in doing so, identify and communicate critical path activities, areas that can be prioritised pre-response, be easily transferrable to other parties to support mutual aid activities and, should the need arise, be used as an actual response project planning tool. This paper informs readers of these resources and explains the reasoning behind their creation.