Horse-race model simulations of the stop-signal procedure.

By Guido P. H. Band, Maurits W. van der Molen, Gordon D. Logan. Acta Psychologica, 2003

727 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/7617b0058a07535376524a08f53d3f2a1c831335

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Developmental and Educational Psychology

Citations in the corpus

Executive functions and the generation of “random” sequential responses: A computational account (2016, unknown): “…Gilbert & Shallice, 2002; Brown, Reynolds & Braver, 2007; Altmann & Gray, 2008; Herd et al., 2014; memory maintenance and updating: Ashby, Ell, Valentin & Casale, 2005; O’Reilly & Frank, 2006; response inhibition: Band et al., 2003; Boucher, Palmeri, Logan & Schall, 2007; Wiecki & Frank, 2013).”
Cortical and Subcortical Contributions to Stop Signal Response Inhibition: Role of the Subthalamic Nucleus (2006, methodology): “…SSRT was estimable by subtracting average SSD from median correct Go RT (according to the race model) This tracking procedure is ideal for producing a summary measure of SSRT, which is relatively robust against any violations of independence between Go and Stop processes (Band et al., 2003).”
Do emotional stimuli interfere with response inhibition? Evidence from the stop signal paradigm (2007, methodology): “, 1997; see also Band et al., 2003). Note that, in accordance with the procedure of Logan et al. (1997), we used the untrimmed mean CRT for the SSRT estimation because the working space of the tracking algorithm covers the whole distribution of choice responses (thus, including RTs longer than 2.”
Executive Functions (2012, unknown): “…automatically by a stimulus) to race to the response threshold and then fade, enabling the correct answer to compete more successfully [to do something other than your prepotent response requires mental effort and reaches the response threshold more slowly (Figure 1; see Band et al. 2003)].”
Models of Response Inhibition in the Stop-Signal and Stop-Change Paradigms (2008, methodology): “Both simulations (Band et al., 2003) and reliability tests showed that when the”
Individual Differences in Heart Rate Variability Predict the Degree of Slowing during Response Inhibition and Initiation in the Presence of Emotional Stimuli (2011, unknown): “arousal cut-off scores that would ensure that we would have enough remaining trials for the reliable computation of SSRT’s Band et al., 2003; Verbruggen and Logan, 2009) as well as having cut-off scores close to the middle of both scales (5).”
Electroencephalography of response inhibition tasks: functional networks and cognitive contributions. (2013, methodology): “Furthermore, increasing the pressure towards shortened reaction times in go trials leads to an increase in N200 amplitudes (Band et al., 2003b; Jodo and Kayama, 1992).”
Response inhibition in the stop-signal paradigm (2008, methodology): “can be estimated by subtracting SSD from the finishing time (for reviews of estimation methods of SSRT, see Refs [1, 63 ]).”
Triangulating a Cognitive Control Network Using Diffusion-Weighted Magnetic Resonance Imaging (MRI) and Functional MRI (2007, methodology): “Because the dynamic tracking of SSD (see above) was varied to yield 50% P(inhibit), we estimated SSRT by subtracting the average SSD from the mean correct Go RT (Logan and Cowan, 1984; Band et al., 2003).”
STOP-IT: Windows executable software for the stop-signal paradigm (2008, background): “This provides SSRT estimates that are derived from the central part of the primary-task RT distribution, and Logan et al. (1997; see also Band et al., 2003) demonstrated that these " central " estimates are most reliable.”