{ "id": "epej4sn5eb", "slug": "stop-signal-task", "title": "Stop-signal task", "updated": "2026-10-15", "topic_path": [ { "id": "society", "label": "Society and history", "api_url": "https://www.edgepedia.org/api/v1/topics/society" }, { "id": "society.people", "label": "Social life and human behavior", "api_url": "https://www.edgepedia.org/api/v1/topics/society.people" }, { "id": "society.people.psychology", "label": "Psychology and behavior", "api_url": "https://www.edgepedia.org/api/v1/topics/society.people.psychology" }, { "id": "society.people.psychology.cognitive-psychology", "label": "Cognitive psychology", "api_url": "https://www.edgepedia.org/api/v1/topics/society.people.psychology.cognitive-psychology" } ], "geo": [ { "id": "geo.nongeo.t1946.society.people.psychology.cognitive-psychology", "label": "Non-geographic · 1946 to 2000: Cognitive psychology", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.society.people.psychology.cognitive-psychology", "path": [ { "id": "geo.nongeo", "label": "Non-geographic", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo" }, { "id": "geo.nongeo.t1946", "label": "Non-geographic · 1946 to 2000", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946" }, { "id": "geo.nongeo.t1946.society", "label": "Society and history", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.society" }, { "id": "geo.nongeo.t1946.society.people", "label": "Social life and human behavior", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.society.people" }, { "id": "geo.nongeo.t1946.society.people.psychology", "label": "Psychology and behavior", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.society.people.psychology" }, { "id": "geo.nongeo.t1946.society.people.psychology.cognitive-psychology", "label": "Cognitive psychology", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.society.people.psychology.cognitive-psychology" } ] } ], "excerpt": "The stop-signal task is a behavioral paradigm that measures response inhibition by interrupting a go task with a stop signal, estimating covert stop-signal reaction time.", "snippet": "The stop-signal task is a behavioral paradigm that measures response inhibition by interrupting a go task with a stop signal, estimating covert stop-signal reaction time.", "node": "society.people.psychology.cognitive-psychology", "markdown": "# Stop-signal task\n\nThe stop-signal task is a behavioral paradigm that measures response inhibition by interrupting a go task on a minority of trials with a stop signal, allowing estimation of the covert stop-signal reaction time (SSRT), the latency of the inhibitory process itself. Because the stop process is never observed directly, its speed must be inferred from a formal model, which makes the task one of the few behavioral measures of inhibition that yields a latency in milliseconds rather than an accuracy or interference score. It is an essential tool in neuroscience, psychiatry, and psychology[1](https://doi.org/10.7554/elife.46323), with SSRT appearing in over 8,500 publications, including the [ABCD study](https://www.edgepedia.org/abcd-study) following more than 10,000 children through adolescence.[2](https://elifesciences.org/reviewed-preprints/111819)\n\n| Key fact | Value |\n|---|---|\n| What is measured | Covert stop-signal reaction time (SSRT), an estimate of the central tendency, usually the mean, of the latent distribution of stop-process finishing times[1](https://doi.org/10.7554/elife.46323) |\n| Typical SSRT (healthy young adults) | 150–300 ms, mean close to 200 ms[3](https://doi.org/10.1037/0033-295x.91.3.295); 200–250 ms in choice RT[4](https://doi.org/10.1037//0096-1523.10.2.276) |\n| Core model | Independent horse race between a go process and a stop process[3](https://doi.org/10.1037/0033-295x.91.3.295) |\n| Standard tracking target | p(respond\\|signal) ≈ 0.50, adjusted in 50 ms steps[1](https://doi.org/10.7554/elife.46323) |\n| Recommended estimation | Integration method with replacement of go omissions (reliability r = 0.57 vs 0.53 for the mean method)[1](https://doi.org/10.7554/elife.46323) |\n| Adult ADHD deficit | g = 0.51 (95% CI 0.376–0.644)[5](https://link.springer.com/article/10.1007/s11065-023-09592-5) |\n| Scale of use | At least 3,202 stop-signal task publications by 2019[6](https://psycnet.apa.org/manuscript/2026-67050-001.pdf) |\n\n## How it works\n\nThe task rests on the independent horse-race model, in which a go process triggered by the go stimulus races against a stop process triggered by the stop signal; inhibition succeeds when the stop process finishes first.[1](https://doi.org/10.7554/elife.46323) • [3](https://doi.org/10.1037/0033-295x.91.3.295) The model assumes context independence, that the go RT distribution is the same on no-signal and stop-signal trials, and stochastic independence, that on a given trial the finishing times of the go and stop processes are statistically independent random variables.[7](https://www.sciencedirect.com/science/article/abs/pii/S0149763408001449) Under these assumptions the probability of responding given a signal, p(respond\\|signal), depends on the stop-signal delay (SSD) and the finishing-time distributions of the go and stop processes, so SSRT can be estimated from the observed go RT distribution and inhibition rates, usually as the mean stop-process latency, by subtracting SSD from the estimated stop-process finishing time.[8](https://pmc.ncbi.nlm.nih.gov/articles/PMC2709177/) Formally, \\( \\mathrm{SSRT} = r(t_{d}) - t_{d} \\), where \\( r(t_{d}) \\) is the point on the cumulative no-signal RT distribution equal to \\( p(t_{d}) \\).[3](https://doi.org/10.1037/0033-295x.91.3.295) SSRT is identifiable only because these assumptions link the unobservable stop process to observable go RTs and inhibition rates; without them the estimates are invalid.[9](https://www.science.org/doi/10.1126/sciadv.abf4355) An interactive race refinement lets go and stop processes interact strongly near the end of their latencies while remaining independent for most of their course.[8](https://pmc.ncbi.nlm.nih.gov/articles/PMC2709177/) • [10](https://doi.org/10.1037/0033-295x.114.2.376)\n\n## How it is done\n\nEach trial presents a go stimulus (for example, a letter or arrow); on a minority of trials a stop signal (often a tone) appears after a variable SSD and instructs the participant not to respond.[4](https://doi.org/10.1037//0096-1523.10.2.276) SSD is adjusted by a one-up/one-down staircase: it increases after each successful stop and decreases after each failed stop, converging on p(respond\\|signal) ≈ 0.50, commonly in 50 ms steps from an initial SSD of 250 ms.[1](https://doi.org/10.7554/elife.46323) • [11](https://www.ampl-psych.com/wp-content/uploads/2021/04/StopSignal_Stevens.pdf) Recommended samples are roughly 120–150 go trials and 40–50 stop-signal trials with tracking[11](https://www.ampl-psych.com/wp-content/uploads/2021/04/StopSignal_Stevens.pdf); clinical or developmental studies typically run 250–500 total trials.[12](https://doi.org/10.1037/a0030543)\n\nWith the tracking procedure in place, SSRT is estimated by the integration method: find the nth go RT, where n = number of go RTs × p(respond\\|signal) (with 200 go trials and p = 0.45, the 90th fastest RT), then subtract mean SSD; go omissions are assigned the maximum RT.[1](https://doi.org/10.7554/elife.46323) The older mean method assumes mean RT = SSRT + mean SSD, but it is biased by right-skewed go RT distributions and go omissions, and the median method has no justification in the race-model mathematics.[1](https://doi.org/10.7554/elife.46323) • [11](https://www.ampl-psych.com/wp-content/uploads/2021/04/StopSignal_Stevens.pdf) Consensus checks: do not estimate SSRT when p(respond\\|signal) is below 0.25 or above 0.75, or when mean RT on unsuccessful stop trials exceeds mean RT on go trials, which signals a race-model violation.[1](https://doi.org/10.7554/elife.46323) Turnkey implementations such as STOP-IT and commercial scripts automate the staircase and both scoring methods.[13](https://doi.org/10.3758/brm.40.2.479) • [14](https://www.millisecond.com/library/v7/stopsignaltask/stopsignaltask2019/stopsignaltask2019/stopsignaltask2019.manual)\n\n## Origin\n\nDelayed-signal stopping experiments were reported by Joseph S. Lappin and Charles W. Eriksen in 1966, in \"Use of a delayed signal to stop a visual reaction-time response\" in the Journal of Experimental Psychology.[15](https://doi.org/10.1037/h0021266) In those early studies participants slowed their go RTs to keep response rate constant.[11](https://www.ampl-psych.com/wp-content/uploads/2021/04/StopSignal_Stevens.pdf) The qualitative horse-race idea was already present in Vince (1948), who observed that participants were unable to stop their responses when the stop-signal delay was longer than 50 ms, with inhibition very rare at delays of 100 ms and longer.[7](https://www.sciencedirect.com/science/article/abs/pii/S0149763408001449) The field was reorganized by two 1984 papers: [Gordon D. Logan](https://www.edgepedia.org/gordon-d-logan) and William B. Cowan's \"On the ability to inhibit thought and action: A theory of an act of control\" in Psychological Review, which supplied the general theory and the defining estimation equation[3](https://doi.org/10.1037/0033-295x.91.3.295), and Logan, Cowan, and Kenneth A. Davis's companion paper in JEP: Human [Perception](https://www.edgepedia.org/perception) & [Performance](https://www.edgepedia.org/performance), which reported four experiments using a tone stop signal and applied the estimation method.[4](https://doi.org/10.1037//0096-1523.10.2.276) Later work added horse-race simulations of the procedure[16](https://doi.org/10.1016/s0001-6918%2802%2900079-3) and the interactive race model of countermanding saccades.[10](https://doi.org/10.1037/0033-295x.114.2.376)\n\n## Variants\n\nStimulus-selective stopping adds task-irrelevant \"ignore\" or \"continue\" stimuli presented with the same frequency and delay as the stop signal.[17](https://www.ovid.com/journals/devs/fulltext/10.1111/desc.13210~the-development-of-selective-stopping-qualitative-and) Participants may adopt a selective \"discriminate-then-stop\" strategy, inhibiting only after the signal is discriminated, or a nonselective \"stop-then-discriminate\" strategy, inhibiting automatically and then restarting.[18](https://pmc.ncbi.nlm.nih.gov/articles/PMC8802939/) Response-selective variants use multicomponent responses: stop-all trials cancel the entire response, while partial-stop trials cancel only the signaled subcomponent.[18](https://pmc.ncbi.nlm.nih.gov/articles/PMC8802939/) The anticipatory response inhibition (ARI) paradigm, in which stopping is timed against a predictable response, is packaged with stop-signal variants in the open-source SeleST toolbox.[19](https://doi.org/10.1007/s00221-022-06539-9) Animal countermanding analogs extend the paradigm to monkeys, rats, pigeons, and sheep.[6](https://psycnet.apa.org/manuscript/2026-67050-001.pdf)\n\n## Applications\n\nSSRT is elevated in younger children and older adults relative to young adults, and going and stopping develop and decline independently.[8](https://pmc.ncbi.nlm.nih.gov/articles/PMC2709177/) Reliability is moderate: ICC 0.71 with lenient scoring across pooled studies, similar to ICC 0.72 reported in ADHD children.[20](https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2012.00037/full) A meta-analysis of 26 publications (883 adult ADHD patients, 916 controls) found prolonged SSRT in adult ADHD with g = 0.51 (95% CI 0.376–0.644), not moderated by study quality or clinical parameters.[5](https://link.springer.com/article/10.1007/s11065-023-09592-5) An earlier cross-disorder meta-analysis reported g = 0.62 in ADHD, 0.77 in OCD, and 0.69 in schizophrenia.[5](https://link.springer.com/article/10.1007/s11065-023-09592-5) • [21](https://doi.org/10.1017/s1355617710000895) Slower SSRT in people with ADHD and their relatives has motivated proposals that stop-signal inhibition is an endophenotype of ADHD, and SSRT is prolonged in OCD and first-degree relatives, trichotillomania, and Tourette's syndrome, typically with unaffected go RT.[8](https://pmc.ncbi.nlm.nih.gov/articles/PMC2709177/) Neural interpretation is anchored by lesion and stimulation work: damage to right inferior frontal gyrus disrupts stop-signal inhibition[22](https://doi.org/10.1038/nn1003), rTMS over right IFG (but not left IFG or right middle frontal gyrus) impairs stopping, and a network model routes stopping from right IFG through the subthalamic nucleus to the superior colliculus.[8](https://pmc.ncbi.nlm.nih.gov/articles/PMC2709177/) • [23](https://www.yorku.ca/science/research/schalljd/wp-content/uploads/sites/654/2022/10/Schall-Palmeri-Logan-2017-Phil-Trans-B-Models-of-Inhibitory-control-1.pdf)\n\n## Limitations and alternatives\n\nThe keystone independence assumption is violated in practice. Across 860,568 trials from 675 subjects in 14 datasets, severe context-independence violations occur systematically at SSDs below 200 ms, across fast and slow RTs, auditory and visual signals, manual and saccadic responses, and especially in selective stopping; in severe violations stop-failure RT can exceed no-signal RT, which is impossible under the independent race model and invalidates SSRT.[9](https://www.science.org/doi/10.1126/sciadv.abf4355) Recommended mitigations include restricting SSDs to ≥200 ms, preferring simple over selective stopping, and using short go deadlines.[9](https://www.science.org/doi/10.1126/sciadv.abf4355) Participants also trade go speed for stop success when they expect signals and reactively slow after stop trials[8](https://pmc.ncbi.nlm.nih.gov/articles/PMC2709177/); gradual slowing, present in about half of participants, makes the integration method underestimate SSRT.[24](https://lirias.kuleuven.be/retrieve/5d54fda9-2139-4c26-a945-50fb25c6c008) Trigger failures, in which the stop process is never initiated, distort traditional estimates and may explain poor performance in schizophrenia and ADHD.[25](https://pmc.ncbi.nlm.nih.gov/articles/PMC9826813/) • [26](https://www.nature.com/articles/s41598-026-65804-x) Against alternatives, the go/no-go task measures action restraint rather than cancellation, recruits different neural dynamics, yields no individual inhibition-speed measure, and performance in one task can deteriorate without deficits in the other, so interchangeable use is unwarranted.[27](https://www.sciencedirect.com/science/article/pii/S1053811920300690) The anticipated response paradigm gives more reliable SSRT, with within-subject variability eight times lower than the simple-response task in young participants[24](https://lirias.kuleuven.be/retrieve/5d54fda9-2139-4c26-a945-50fb25c6c008), and the independence assumption is met in 100% of participants on stop-all SST trials but only 20% on ARI trials.[19](https://doi.org/10.1007/s00221-022-06539-9) Reanalyses of 7 archival datasets plus a preregistered study of 37 participants found that low-level visuo-motor delays account for two-thirds of manual SSRT duration and 40% of its individual differences; the authors propose a new measure, the selective stopping delay \\( \\Delta T = T_{\\mathrm{S}} - T_{0} \\), designed to factor out incompressible sensory and motor delays.[2](https://elifesciences.org/reviewed-preprints/111819) Modeling has moved toward hybrid and Bayesian architectures: a hybrid racing-diffusion plus ex-Gaussian stop-runner model (RDEX) avoids the lower-bound shift-parameter problem, while models with an evidence-accumulation stop runner are not mathematically identified and have poor psychometric properties.[28](https://link.springer.com/article/10.3758/s13428-023-02295-y) The RDEX-ABCD model was built for the ABCD study's stop-signal task, whose design (the stop signal replaces the go stimulus at short SSDs) violates context independence and calls non-parametric SSRT estimates into question.[25](https://pmc.ncbi.nlm.nih.gov/articles/PMC9826813/) • [29](https://doi.org/10.1016/j.dcn.2022.101191) A 2026 reanalysis of human and macaque data using trigger-failure-aware estimation found that higher stop-signal salience both shortens SSRT and reduces trigger-failure probability, cautioning that apparent SSRT effects under traditional methods may reflect changes in triggering rather than inhibitory speed.[26](https://www.nature.com/articles/s41598-026-65804-x)\n\n## References\n\n1. [Frederick Verbruggen and colleagues (2019). A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task. eLife.](https://doi.org/10.7554/elife.46323)\n2. [Non-decision time: the elephant in the executive control room (eLife Reviewed Preprint)](https://elifesciences.org/reviewed-preprints/111819)\n3. [Gordon D. Logan, William B. Cowan (1984). On the ability to inhibit thought and action: A theory of an act of control.. Psychological Review.](https://doi.org/10.1037/0033-295x.91.3.295)\n4. [Gordon D. Logan, William B. Cowan, Kenneth A. Davis (1984). On the ability to inhibit simple and choice reaction time responses: A model and a method.. Journal of Experimental Psychology Human Perception & Performance.](https://doi.org/10.1037//0096-1523.10.2.276)\n5. [Assessing Inhibitory Control Deficits in Adult ADHD: A Systematic Review and Meta-analysis of the Stop-signal Task (Neuropsychology Review, 2023)](https://link.springer.com/article/10.1007/s11065-023-09592-5)\n6. [Commentary on the 1984 Logan, Cowan and Davis paper (JEP:HPP)](https://psycnet.apa.org/manuscript/2026-67050-001.pdf)\n7. [Models of response inhibition in the stop-signal and stop-change paradigms](https://www.sciencedirect.com/science/article/abs/pii/S0149763408001449)\n8. [Response inhibition in the stop-signal paradigm (Verbruggen & Logan, Trends in Cognitive Sciences, 2008)](https://pmc.ncbi.nlm.nih.gov/articles/PMC2709177/)\n9. [Severe violations of independence in response inhibition tasks](https://www.science.org/doi/10.1126/sciadv.abf4355)\n10. [Leanne Boucher and colleagues (2007). Inhibitory control in mind and brain: An interactive race model of countermanding saccades.. Psychological Review.](https://doi.org/10.1037/0033-295x.114.2.376)\n11. [The Stop-Signal Paradigm (Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience)](https://www.ampl-psych.com/wp-content/uploads/2021/04/StopSignal_Stevens.pdf)\n12. [Dora Matzke and colleagues (2012). Bayesian parametric estimation of stop-signal reaction time distributions.. Journal of Experimental Psychology General.](https://doi.org/10.1037/a0030543)\n13. [Frederick Verbruggen, Gordon D. Logan, Michaël A. Stevens (2008). STOP-IT: Windows executable software for the stop-signal paradigm. Behavior Research Methods.](https://doi.org/10.3758/brm.40.2.479)\n14. [Technical Manual: Inquisit Stop Signal Task 2019](https://www.millisecond.com/library/v7/stopsignaltask/stopsignaltask2019/stopsignaltask2019/stopsignaltask2019.manual)\n15. [Joseph S. Lappin, Charles W. Eriksen (1966). Use of a delayed signal to stop a visual reaction-time response.. Journal of Experimental Psychology.](https://doi.org/10.1037/h0021266)\n16. [Horse-race model simulations of the stop-signal procedure (Acta Psychologica, 2003)](https://doi.org/10.1016/s0001-6918%2802%2900079-3)\n17. [The development of selective stopping (Developmental Science)](https://www.ovid.com/journals/devs/fulltext/10.1111/desc.13210~the-development-of-selective-stopping-qualitative-and)\n18. [Stopping Interference in Response Inhibition: Behavioral and Neural Signatures of Selective Stopping](https://pmc.ncbi.nlm.nih.gov/articles/PMC8802939/)\n19. [Corey G. Wadsley and colleagues (2023). Comparing anticipatory and stop-signal response inhibition with a novel, open-source selective stopping toolbox. Experimental Brain Research.](https://doi.org/10.1007/s00221-022-06539-9)\n20. [Measurement and Reliability of Response Inhibition (Congdon et al., Frontiers in Psychology, 2012)](https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2012.00037/full)\n21. [JONATHAN LIPSZYC, RUSSELL SCHACHAR (2010). Inhibitory control and psychopathology: A meta-analysis of studies using the stop signal task. Journal of the International Neuropsychological Society.](https://doi.org/10.1017/s1355617710000895)\n22. [Adam R. Aron and colleagues (2003). Stop-signal inhibition disrupted by damage to right inferior frontal gyrus in humans. Nature Neuroscience.](https://doi.org/10.1038/nn1003)\n23. [Models of inhibitory control (Schall, Palmeri & Logan, Phil. Trans. R. Soc. B, 2017)](https://www.yorku.ca/science/research/schalljd/wp-content/uploads/sites/654/2022/10/Schall-Palmeri-Logan-2017-Phil-Trans-B-Models-of-Inhibitory-control-1.pdf)\n24. [Comparing three stop-signal paradigms: choice response, simple response, and anticipated response](https://lirias.kuleuven.be/retrieve/5d54fda9-2139-4c26-a945-50fb25c6c008)\n25. [A cognitive process modeling framework for the ABCD study stop-signal task (RDEX-ABCD model)](https://pmc.ncbi.nlm.nih.gov/articles/PMC9826813/)\n26. [The salience of the stop signal affects triggering and latency of response inhibition across species: findings from a Bayesian approach (Scientific Reports, 2026)](https://www.nature.com/articles/s41598-026-65804-x)\n27. [Differences in unity: The go/no-go and stop signal tasks rely on different mechanisms](https://www.sciencedirect.com/science/article/pii/S1053811920300690)\n28. [A hybrid approach to dynamic cognitive psychometrics (Behavior Research Methods)](https://link.springer.com/article/10.3758/s13428-023-02295-y)\n29. [Alexander Weigard and colleagues (2022). A cognitive process modeling framework for the ABCD study stop-signal task. Developmental Cognitive Neuroscience.](https://doi.org/10.1016/j.dcn.2022.101191)\n\n---\n*Topic: Encyclopedia › Society and history › Social life and human behavior › Psychology and behavior › Cognitive psychology*\n\n*Initially written Sep 29, 2026 · Edited: Oct 15, 2026*\n\n*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*\n\nLicense: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license\n", "same_as": [], "url": "https://www.edgepedia.org/stop-signal-task", "markdown_url": "https://www.edgepedia.org/stop-signal-task.md", "license": { "name": "Edgepedia Community License 1.0", "url": "https://www.edgepedia.org/edgepedia/license", "summary": "Free with credit, commercial use included. AI training is open to everyone. For other uses, organizations over USD 100M in revenue or 100M monthly users license separately.", "spdx": "LicenseRef-Edgepedia-Community-1.0" }, "credit": "\"Stop-signal task\", Edgepedia (EdgeChat), https://www.edgepedia.org/stop-signal-task. Edgepedia Community License 1.0.", "credit_md": "\"[Stop-signal task](https://www.edgepedia.org/stop-signal-task)\", Edgepedia (EdgeChat), [https://www.edgepedia.org/stop-signal-task](https://www.edgepedia.org/stop-signal-task). [Edgepedia Community License 1.0](https://www.edgepedia.org/edgepedia/license).", "credit_html": "\"Stop-signal task\", Edgepedia (EdgeChat), https://www.edgepedia.org/stop-signal-task. Edgepedia Community License 1.0.", "speakable": "The stop-signal task is a behavioral paradigm that measures response inhibition by interrupting a go task with a stop signal, estimating covert stop-signal reaction time." }