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Relevant bibliographies by topics / GO-STOP-GO Models

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Journal articles

Academic literature on the topic 'GO-STOP-GO Models'

Author: Grafiati

Published: 6 September 2023

Last updated: 7 September 2023

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Journal articles on the topic "GO-STOP-GO Models"

1

Pujol, Oriol, Debora Gil, and Petia Radeva. "Fundamentals of Stop and Go active models." Image and Vision Computing 23, no. 8 (2005): 681–91. http://dx.doi.org/10.1016/j.imavis.2005.03.007.

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2

Schall, Jeffrey D., Thomas J. Palmeri, and Gordon D. Logan. "Models of inhibitory control." Philosophical Transactions of the Royal Society B: Biological Sciences 372, no. 1718 (2017): 20160193. http://dx.doi.org/10.1098/rstb.2016.0193.

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We survey models of response inhibition having different degrees of mathematical, computational and neurobiological specificity and generality. The independent race model accounts for performance of the stop-signal or countermanding task in terms of a race between GO and STOP processes with stochastic finishing times. This model affords insights into neurophysiological mechanisms that are reviewed by other authors in this volume. The formal link between the abstract GO and STOP processes and instantiating neural processes is articulated through interactive race models consisting of stochastic

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3

Pamanikabud, Pichai, and Chaisri Tharasawatpipat. "Stop-and-Go Urban Traffic Noise Simulation and Prediction." Noise & Vibration Worldwide 33, no. 4 (2002): 17–24. http://dx.doi.org/10.1260/095745602320753156.

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This paper presents techniques for simulating of urban traffic noise along with a mathematical model of stop-and-go traffic noise for the road network in the city center. The road network in the central part of Bangkok surrounded by the cordon line of Rajchadapisek Ring Road was used as the study area. The analysed data consisted of traffic characteristics and its noise levels which were collected from the study area together with the geometrical dimensions of road sections at each data collection locations. Finally, the stop-and-go traffic noise models generated by this study in the form of a

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4

Penadés, R., R. Catalán, K. Rubia, S. Andrés, M. Salamero, and C. Gastó. "Impaired response inhibition in obsessive compulsive disorder." European Psychiatry 22, no. 6 (2006): 404–10. http://dx.doi.org/10.1016/j.eurpsy.2006.05.001.

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AbstractObjectiveThe present study investigates different three inhibitory control functions in patients with obsessive-compulsive disorder (OCD). Selective motor response inhibition was tested in a GO/NO-GO paradigm, the inhibition of a triggered motor response in a STOP paradigm and the ability to inhibit cognitive interference in a motor STROOP paradigm.Methods27 patients who met DSM-IV criteria for OCD and 25 age, handedness and IQ-matched healthy control subjects were tested in the GO/NO-GO, STOP and motor STROOP tasks.ResultsOCD patients performed significantly worse than controls in the

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5

Farah, Ashraf, Ashraf Talaat, and Farrag Farrag. "Accuracy Assessment of Digital Elevation Models Using GPS." Artificial Satellites 43, no. 4 (2008): 151–61. http://dx.doi.org/10.2478/v10018-009-0014-7.

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Accuracy Assessment of Digital Elevation Models Using GPSA Digital Elevation Model (DEM) is a digital representation of ground surface topography or terrain with different accuracies for different application fields. DEM have been applied to a wide range of civil engineering and military planning tasks. DEM is obtained using a number of techniques such as photogrammetry, digitizing, laser scanning, radar interferometry, classical survey and GPS techniques. This paper presents an assessment study of DEM using GPS (Stop&Go) and kinematic techniques comparing with classical survey. The result

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6

Soltanifar, Mohsen, Michael Escobar, Annie Dupuis, Andre Chevrier, and Russell Schachar. "The Asymmetric Laplace Gaussian (ALG) Distribution as the Descriptive Model for the Internal Proactive Inhibition in the Standard Stop Signal Task." Brain Sciences 12, no. 6 (2022): 730. http://dx.doi.org/10.3390/brainsci12060730.

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Measurements of response inhibition components of reactive inhibition and proactive inhibition within the stop-signal paradigm have been of particular interest to researchers since the 1980s. While frequentist nonparametric and Bayesian parametric methods have been proposed to precisely estimate the entire distribution of reactive inhibition, quantified by stop signal reaction times (SSRT), there is no method yet in the stop signal task literature to precisely estimate the entire distribution of proactive inhibition. We identify the proactive inhibition as the difference of go reaction times f

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7

Czapla, Marta, Sabine Vollstädt-Klein, Mira Fauth-Bühler, et al. "Response inhibition deficits: Reliability of alcohol-related assessment tasks." SUCHT 62, no. 4 (2016): 203–15. http://dx.doi.org/10.1024/0939-5911/a000431.

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Abstract. Background: While models of addictive behaviour stress the role of an impairment of response inhibition when drug-dependent individuals are confronted with drug-associated stimuli, results from different studies are conflicting. However, there is a considerable lack of studies that assess the reliability of tasks to assess deficits of response inhibition when drug-associated stimuli are presented. Methods: In the present paper we present results from four different studies in which either a stop-signal task (study 1), a go/no-go task (study 2/3) or a go/no-go shifting task (study 4)

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8

Wilson, R. Eddie. "Mechanisms for spatio-temporal pattern formation in highway traffic models." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 366, no. 1872 (2008): 2017–32. http://dx.doi.org/10.1098/rsta.2008.0018.

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A key qualitative requirement for highway traffic models is the ability to replicate a type of traffic jam popularly referred to as a phantom jam , shock wave or stop-and-go wave . Despite over 50 years of modelling, the precise mechanisms for the generation and propagation of stop-and-go waves and the associated spatio-temporal patterns are in dispute. However, the increasing availability of empirical datasets, such as those collected from motorway incident detection and automatic signalling system (MIDAS) inductance loops in the UK or the next-generation simulation trajectory data (NGSIM) pr

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9

Dong, Sheng, and Jibiao Zhou. "A Comparative Study on Drivers’ Stop/Go Behavior at Signalized Intersections Based on Decision Tree Classification Model." Journal of Advanced Transportation 2020 (May 29, 2020): 1–13. http://dx.doi.org/10.1155/2020/1250827.

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The stop/go decisions at signalized intersections are closely related to driving speed during signal change intervals. The speed during stop/go decision-making has a significant influence on the dilemma area, resulting in changes of stop/go decisions and high complexity of the decision-making process. Considering that traffic delays and vehicle exhaust pollution are mainly caused by queuing at intersections, the stop-line passing speed during the signal change interval will affect both vehicle operation safety and the atmospheric environment. This paper presents a comparative study on drivers’

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10

Tordeux, Antoine, Andreas Schadschneider, and Sylvain Lassarre. "Stop-and-go waves induced by correlated noise in pedestrian models without inertia." Journal of Traffic and Transportation Engineering (English Edition) 7, no. 1 (2020): 52–60. http://dx.doi.org/10.1016/j.jtte.2019.01.006.

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