The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Modeling Response Times for Two-Choice Decisions
Abstract
The diffusion model for two-choice real-time decisions is applied to four psychophysical tasks. The model reveals how stimulus information guides decisions and shows how the information is processed through time to yield sometimes correct and sometimes incorrect decisions. Rapid two-choice decisions yield multiple empirical measures: response times for correct and error responses, the probabilities of correct and error responses, and a variety of interactions between accuracy and response time that depend on instructions and task difficulty. The diffusion model can explain all these aspects of the data for the four experiments we present. The model correctly accounts for error response times, something previous models have failed to do. Variability within the decision process explains how errors are made, and variability across trials correctly predicts when errors are faster than correct responses and when they are slower.
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In the citing paper
“the discharge on any one trial resembles a random walk to a threshold representing the level of evidence required to reach a decision, at which point the decision process terminates”
From Response of Neurons in the Lateral Intraparietal Area during a Combined Visual Discrimination Reaction Time Task (2002) · cites this paper as Ratcliff and Rouder, 1998
In the citing paper
“Psychophysical and neural data1 support a model termed drift diffusion6, random walk5,7 or race8 in which a decision is made when the accumulated noisy evidence (decision variable) reaches a criterion level, termed a decision bound.”
From Changes of mind in decision-making (2009) · cites this paper as [6]
In the citing paper
“Several formalisms propose that a representation of noisy evidence is evaluated against a criterion to produce a decision4-8.”
From Changes of mind in decision-making (2009) · cites this paper as [6]
In the citing paper
“The ensuing tradeoff between speed and accuracy is explained by sequential sampling with optional stopping models in which evidence is accumulated to some stopping criterion or bound (Link, 1975; Ratcliff and Rouder, 1998).”
From A common mechanism underlies changes of mind about decisions and confidence (2016) · cites this paper as Ratcliff and Rouder, 1998
In the citing paper
“The class of bounded sequential sampling models, including many variants (Wald, 1947; Stone, 1960; Laming, 1968; Link, 1975; Good, 1979; Luce, 1986; Ratcliff and Rouder, 1998) explains the tradeoff between speed and accuracy, and many of the essential steps have correlates in neurophysiology (Romo et al., 2002; Heekeren et al., 2004; Ploran et al., 2007; Heitz and Schall, 2012; White et al., 2012;”
From A common mechanism underlies changes of mind about decisions and confidence (2016) · cites this paper as Ratcliff and Rouder, 1998
In the citing paper
“Many models are proposed to explain the coordination of RT and choice accuracy in 2-choice tasks 4,18,25–27. These models share a common feature: a termination bound is applied to some form of evidence representation, the decision variable.”
From Decision-making with multiple alternatives (2008) · cites this paper as [25]
In the citing paper
“A simple diffusion model provides a plausible account for the stimulation effects in both areas (Fig. 5).”
From Microstimulation of macaque area LIP affects decision-making in a motion discrimination task (2006) · cites this paper as [17]
In the citing paper
“On the other hand, human studies commonly report skewed RT distributions with a long right tail, which is well captured by the diffusion model (Ratcliff, 1978; Luce, 1986; Ratcliff and Rouder, 1998; Usher and McClelland, 2001; Sigman and Dehaene, 2005) but not the existing neural circuit model.”
From Decision Making in Recurrent Neuronal Circuits (2008) · cites this paper as Ratcliff and Rouder, 1998
Citations
Cites
An introduction to probability theory and its applications
(1958) · Journal of the Franklin Institute · doi:10.1016/0016-0032(58)90688-4 · Cited by 29,621
An Introduction to Probability Theory and Its Applications.
Bharucha-Reid, A. T., & Feller, W. (1967) · American Mathematical Monthly · doi:10.2307/2314609 · Cited by 6,365
Ratcliff, R. (1978) · Psychological Review · doi:10.1037/0033-295x.85.2.59 · Cited by 4,513
Self-citationDecision field theory: A dynamic-cognitive approach to decision making in an uncertain environment.
Busemeyer, J. R., & Townsend, J. T. (1993) · Psychological Review · doi:10.1037/0033-295x.100.3.432 · Cited by 1,925
Information theory of choice-reaction times
Laming, D. (1968) · Medical Entomology and Zoology · Cited by 945
Models for choice-reaction time
Stone, M. (1960) · Psychometrika · doi:10.1007/bf02289729 · Cited by 696
Decision Processes in Visual Perception
(1979) · Elsevier eBooks · doi:10.1016/c2013-0-11654-6 · Cited by 513
A Sequential Theory of Psychological Discrimination
Link, S. W., & Heath, R. A. (1975) · Psychometrika · doi:10.1007/bf02291481 · Cited by 492
Retrieval processes in recognition memory.
Ratcliff, R., & Murdock, B. B. (1976) · Psychological Review · doi:10.1037/0033-295x.83.3.190 · Cited by 481
Self-citationComparing decision bound and exemplar models of categorization
Maddox, W. T., & Ashby, F. G. (1993) · Perception & Psychophysics · doi:10.3758/bf03211715 · Cited by 373
Flagged first, then by citations
Research works printed but not linked: 3
- Estes W.K., 1995, Sources of coherence in text comprehension, 51
- Luce R.D., 1986, Response times
- Ratcliff R., Psychological Review
Cited by
Sorted by citations
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Clayton, E. C., Rajkowski, J., Cohen, J. D., & Aston‐Jones, G. (2004) · Journal of Neuroscience · doi:10.1523/jneurosci.2446-04.2004 · Cited by 226
How to measure post-error slowing: A confound and a simple solution
Dutilh, G., van Ravenzwaaij, D., Nieuwenhuis, S., van der Maas, H. L. J., Forstmann, B. U., & Wagenmakers, E. (2012) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2012.04.001 · Cited by 243
CRISP: A computational model of fixation durations in scene viewing.
Nuthmann, A., Smith, T. J., Engbert, R., & Henderson, J. M. (2010) · Psychological Review · doi:10.1037/a0018924 · Cited by 236
Simen, P., Contreras, D., Buck, C. L., Hu, P. T., Holmes, P. J., & Cohen, J. D. (2009) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/a0016926 · Cited by 231
Erlich, J. C., Brunton, B. W., Duan, C. A., Hanks, T. D., & Brody, C. D. (2015) · eLife · doi:10.7554/elife.05457 · Cited by 232
A neural mechanism of speed-accuracy tradeoff in macaque area LIP
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Wong, K.-F. (2007) · Frontiers in Computational Neuroscience · doi:10.3389/neuro.10.006.2007 · Cited by 204
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Pupil-Linked Arousal Determines Variability in Perceptual Decision Making
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Global gain modulation generates time-dependent urgency during perceptual choice in humans
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Ratcliff, R., & Childers, R. (2015) · Decision · doi:10.1037/dec0000030 · Cited by 202
Self-citationDehaene, S. (1993) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199231447.003.0024 · Cited by 195
Nash Unsworth, A., Redick, T. S., Lakey, C. E., & Young, D. L. (2009) · Intelligence · doi:10.1016/j.intell.2009.08.002 · Cited by 209
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Rouder, J. N., Lu, J., Speckman, P. L., Sun, D., & Jiang, Y. (2005) · Psychonomic Bulletin & Review · doi:10.3758/bf03257252 · Cited by 194
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Fetsch, C. R., Kiani, R., Newsome, W. T., & Shadlen, M. N. (2014) · Neuron · doi:10.1016/j.neuron.2014.07.011 · Cited by 185
A mechanism for value-guided choice based on the excitation-inhibition balance in prefrontal cortex
Jocham, G., Hunt, L. T., Near, J., & Behrens, T. E. J. (2012) · Nature Neuroscience · doi:10.1038/nn.3140 · Cited by 183
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Schall, J. D. (2004) · Annual Review of Psychology · doi:10.1146/annurev.psych.55.090902.141907 · Cited by 186
van der Maas, H. L. J., Molenaar, D., Maris, G., Kievit, R., & Borsboom, D. (2011) · Psychological Review · doi:10.1037/a0022749 · Cited by 181
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Gluth, S., Kern, N., Kortmann, M., & Vitali, C. L. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0822-0 · Cited by 188
Brose, A., Schmiedek, F., Lövdén, M., & Lindenberger, U. (2011) · Emotion · doi:10.1037/a0024436 · Cited by 202
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Donkin, C., Brown, S. D., Heathcote, A., & Wagenmakers, E. (2010) · Psychonomic Bulletin & Review · doi:10.3758/s13423-010-0022-4 · Cited by 185
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Neurobiology of Decision Making
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Effects of Altered Excitation-Inhibition Balance on Decision Making in a Cortical Circuit Model
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Metadata
- Authors
- Ratcliff, R., & Rouder, J. N.
- Affiliations
- Northwestern University (US)
- Venue
- Psychological Science
- Publisher
- SAGE Publications
- Published
- 1998-09
- Volume
- 9
- Issue
- 5
- Pages
- 347-356
- ISSN
- 0956-7976 (ISSN-L)
- 1467-9280
- OpenAlex
- W2100476792
- Cited by
- 1,478
- References
- 24
- Citations by year
- 2026: 45 · 2025: 84 · 2024: 87 · 2023: 88 · 2022: 76 · 2021: 99 · 2020: 94 · 2019: 91 · 2018: 92 · 2017: 79 · 2016: 82 · 2015: 72 · 2014: 68 · 2013: 48 · 2012: 41
- FWCI
- 4.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Science (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Keywords
- diffusion models, reaction time (psychology), decision accuracy, reaction time variability, task difficulty, real-time decision making
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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