Diffusion decision model: Current issues and history
Open accessAbstract
There is growing interest in diffusion models to represent the cognitive and neural processes of speeded decision making. Sequential-sampling models like the diffusion model have a long history in psychology. They view decision making as a process of noisy accumulation of evidence from a stimulus. The standard model assumes that evidence accumulates at a constant rate during the second or two it takes to make a decision. This process can be linked to the behaviors of populations of neurons and to theories of optimality. Diffusion models have been used successfully in a range of cognitive tasks and as psychometric tools in clinical research to examine individual differences. In this review, we relate the models to both earlier and more recent research in psychology.
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In the standard drift-diffusion model, a decision variable accumulates sensory evidence with stochastic noise and produces a decision at reaction time when it reaches either the upper or lower bound.
“A DV in standard drift-diffusion model (DDM) (3–5, 7) (Fig. 4A) accumulates sensory evidence (drift), accompanied by stochastic internal or sensory noise (diffusion) that, when reaches either upper (correct trials) or lower (error trials) bound, produces a decision at RT.”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [7]
A canonical framework of perceptual decision-making considers the decision process as linear integration of evidence over time during sequential noisy sensory sampling until a decision bound is reached.
“A canonical framework of perceptual decision-making (7) considers the decision process as the linear integration of evidence over time during sequential noisy sensory sampling until a decision bound is reached.”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [7]
The neural activity ramp during epoch 4 is consistent with a bounded integration mechanism of decision-making.
“In what follows we argue the neural activity ramp during epoch 4 is consistent with a bounded integration mechanism of decision-making (3–5, 7).”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [7]
Internal decision variables are accumulated as ramping neural activity and transformed into a decision when a decision boundary is reached.
“Here, close to the top of the cortical hierarchy, internal decision variables (DV) are accumulated in a form of ramping neural activity (3–6) that is transformed into a decision when a decision boundary is reached (7).”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [7]
The single latent variable is consistent with models of gradual accumulation of noisy sensory evidence to a decision bound.
“We show that this variable is consistent with models of gradual accumulation of noisy sensory evidence to a decision bound.”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [7]
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“This and other diffusion models have been used to account for, among other things, value-based decision, social choice, and purchasing decisions [150,152,153,157,207–210].”
MethodTheoretical interpretations of the speed and accuracy of positive and negative responses.
Ratcliff, R. (1985) · Psychology Review · doi:10.1037/0033-295x.92.2.212 · Cited by 294 · see citing works
Self-citationEye tracking and pupillometry are indicators of dissociable latent decision processes.
Cavanagh, J. F., Wiecki, T. V., Kochar, A., & Frank, M. J. (2014) · Journal of Experimental Psychology: General · doi:10.1037/a0035813 · Cited by 289 · cited 1× · see citing works
“Relationships have been established between diffusion model analyses and behavioral measures such as eye tracking [128,137] and pupil dilation [142].”
BackgroundDiffusion models of the flanker task: Discrete versus gradual attentional selection
White, C. N., Ratcliff, R., & Starns, J. J. (2011) · Cognitive Psychology · doi:10.1016/j.cogpsych.2011.08.001 · Cited by 286 · cited 2× · see citing works
“Two diffusion models have been developed, one that assumes dual stages, and one that assumes that evidence driving the process changed continuously over time as a result of attention gradually focusing on the central target [70,71].”
BackgroundSelf-citationDeliberation and Commitment in the Premotor and Primary Motor Cortex during Dynamic Decision Making
Thura, D., & Cisek, P. (2014) · Neuron · doi:10.1016/j.neuron.2014.01.031 · Cited by 284 · see citing works
Gold, J. I., & Shadlen, M. N. (2003) · Journal of Neuroscience · doi:10.1523/jneurosci.23-02-00632.2003 · Cited by 278 · cited 1× · see citing works
“Other studies in animals and humans have found that accuracy does not increase after some stimulus duration [37,106–110], suggesting a time by which the relevant stimulus information has been constructed.”
BackgroundRatcliff, R., Philiastides, M. G., & Sajda, P. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0812589106 · Cited by 275 · cited 5× · see citing works
“Another source of support for across-trial variability in drift rate comes from a face/car perceptual discrimination task in which EEG signals are used to sort responses into two groups, those that are more face-like and those that are more car-like ([42], see also [43]).”
BackgroundInfluentialSelf-citationSullivan, N. J., Hutcherson, C. A. C., Harris, A. M., & Rangel, A. (2014) · Psychological Science · doi:10.1177/0956797614559543 · Cited by 275 · see citing works
Stochastic models of decisions about motion direction: Behavior and physiology
Ditterich, J. (2006) · Neural Networks · doi:10.1016/j.neunet.2000.05.o42 · Cited by 272 · cited 1×
“Another assumption with much the same effect is that fixed boundaries are maintained, but an ‘urgency signal’ is added to the accumulated evidence [82,83].”
BackgroundA dual-stage two-phase model of selective attention.
Hübner, R., Steinhauser, M., & Lehle, C. (2010) · Psychological Review · doi:10.1037/a0019471 · Cited by 271 · cited 2× · see citing works
“Two diffusion models have been developed, one that assumes dual stages, and one that assumes that evidence driving the process changed continuously over time as a result of attention gradually focusing on the central target [70,71].”
BackgroundAn Exemplar-Based Random Walk Model of Speeded Classification
Nosofsky, R., & Palmeri, T. (1997) · Psychological Review · doi:10.1037/0033-295x.104.2.266 · Cited by 271 · cited 1× · see citing works
“Diffusion and other models have been applied to many basic perceptual and memory tasks such as item and associative recognition [2,15,121,122,132], lexical decision [121,133], perceptual tasks including brightness, letter, motion, visual search, contrast, orientation discrimination tasks [7,14,16,44,103,104,134,135],…”
MethodDecision making by urgency gating: theory and experimental support
Thura, D., Beauregard-Racine, J., Fradet, C.-W., & Cisek, P. (2012) · Journal of Neurophysiology · doi:10.1152/jn.01071.2011 · Cited by 267 · cited 1× · see citing works
“Other studies that have identified support for nonstationary models have mostly employed unusual decision-making tasks, for example, with very long decision times or slowly changing stimulus properties [85,87].”
Polanía, R. H., Krajbich, I. M., Grueschow, M., & Ruff, C. C. (2014) · Neuron · doi:10.1016/j.neuron.2014.03.014 · Cited by 261 · cited 5× · see citing works
“Another source of support for across-trial variability in drift rate comes from a face/car perceptual discrimination task in which EEG signals are used to sort responses into two groups, those that are more face-like and those that are more car-like ([42], see also [43]).”
BackgroundInfluentialThe relative judgment theory of two choice response time
Link, S. W. (1975) · Journal of Mathematical Psychology · doi:10.1016/0022-2496(75)90053-x · Cited by 251 · see citing works
Modeling confidence and response time in recognition memory.
Ratcliff, R., & Starns, J. J. (2009) · Psychological Review · doi:10.1037/a0014086 · Cited by 250 · see citing works
Self-citationfMRI and EEG Predictors of Dynamic Decision Parameters during Human Reinforcement Learning
Frank, M. J., Gagne, C., Nyhus, E., Masters, S. E., Wiecki, T. V., Cavanagh, J. F., & Badre, D. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.2036-14.2015 · Cited by 242 · cited 1× · see citing works
“Other tasks that have more interdisciplinary relationships include stop signal tasks [137,138], conflict tasks [70,71], reinforcement learning [69,139], preferential choice and value-based decisions (Box 7), and social decisions [140,141] (Box 7).”
BackgroundA diffusion model analysis of the effects of aging on recognition memory
Ratcliff, R., Thapar, A., & McKoon, G. (2004) · Journal of Memory and Language · doi:10.1016/j.jml.2003.11.002 · Cited by 239 · cited 1× · see citing works
“Diffusion and other models have been applied to many basic perceptual and memory tasks such as item and associative recognition [2,15,121,122,132], lexical decision [121,133], perceptual tasks including brightness, letter, motion, visual search, contrast, orientation discrimination tasks [7,14,16,44,103,104,134,135],…”
MethodSelf-citationIndividual differences, aging, and IQ in two-choice tasks
Ratcliff, R., Thapar, A., & McKoon, G. (2009) · Cognitive Psychology · doi:10.1016/j.cogpsych.2009.09.001 · Cited by 237 · cited 2× · see citing works
“Diffusion and other models have been applied to many basic perceptual and memory tasks such as item and associative recognition [2,15,121,122,132], lexical decision [121,133], perceptual tasks including brightness, letter, motion, visual search, contrast, orientation discrimination tasks [7,14,16,44,103,104,134,135],…”
MethodSelf-citationStochastic models of decisions about motion direction: Behavior and physiology
Ditterich, J. (2006) · Neural Networks · doi:10.1016/j.neunet.2006.05.042 · Cited by 233 · see citing works
Time course of item and associative information: Implications for global memory models.
Gronlund, S. D., & Ratcliff, R. (1989) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037//0278-7393.15.5.846 · Cited by 233 · see citing works
Self-citationContinuous versus discrete information processing: Modeling accumulation of partial information.
Ratcliff, R. (1988) · Psychological Review · doi:10.1037/0033-295x.95.2.238 · Cited by 231 · see citing works
Self-citationAnalysis of sequential effects in choice reaction times.
Remington, R. (1969) · Journal of Experimental Psychology · doi:10.1037/h0028122 · Cited by 229 · see citing works
Process components of the Implicit Association Test: a diffusion-model analysis.
Klauer, K. C., Voss, A., Schmitz, F., & Teige-Mocigemba, S. (2007) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.93.3.353 · Cited by 227 · cited 1× · see citing works
“Other tasks that have more interdisciplinary relationships include stop signal tasks [137,138], conflict tasks [70,71], reinforcement learning [69,139], preferential choice and value-based decisions (Box 7), and social decisions [140,141] (Box 7).”
BackgroundSequential effects in choice reaction time.
Schvaneveldt, R., & Chase, W. (1969) · Journal of Experimental Psychology · doi:10.1037/h0027144 · Cited by 227 · see citing works
On optimal decision-making in brains and social insect colonies
Marshall, J. A. R., Bogacz, R., Dornhaus, A., Planqué, R., Kovacs, T., & Franks, N. R. (2009) · Journal of The Royal Society Interface · doi:10.1098/rsif.2008.0511 · Cited by 227 · cited 1× · see citing works
“Diffusion model analyses have also been applied to bees and animal swarms [158,159] and even to slime molds [160].”
MethodSimen, 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 224 · cited 1× · see citing works
“An alternative definition of optimality is reward rate maximization [76], defined as maximizing the number of correct decisions (and the associated reward) per unit time [72,77–81].”
BackgroundThe accumulator model of two-choice discrimination
Smith, P. L., & Vickers, D. (1988) · Journal of Mathematical Psychology · doi:10.1016/0022-2496(88)90043-0 · Cited by 223 · cited 1× · see citing works
“Instead, they were better described by a version of the Vickers accumulator model [98], in which only large stimulus elements are accumulated and small ones are ignored.”
Self-citationFitting the ratcliff diffusion model to experimental data
Vandekerckhove, J., & Tuerlinckx, F. (2007) · Psychonomic Bulletin & Review · doi:10.3758/bf03193087 · Cited by 215 · cited 1× · see citing works
“Fitting packages are available (but subject to misuse if not understood): fast-dm [124], DMAT [125], HDDM [126], as well as alternative fitting methods [127].”
BackgroundThe hare and the tortoise: Emphasizing speed can change the evidence used to make decisions.
Rae, B., Heathcote, A., Donkin, C., Averell, L., & Brown, S. D. (2014) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/a0036801 · Cited by 215 · cited 1× · see citing works
“, [4,13–16]) and much smaller changes in nondecision time [17,18], although instructions that extremely stress speed can reduce drift rates ([19], see also [20]).”
BackgroundSelf-citationPerceptual decision neurosciences – A model-based review
Mulder, M., van Maanen, L., & Forstmann, B. U. (2014) · Neuroscience · doi:10.1016/j.neuroscience.2014.07.031 · Cited by 214 · cited 1× · see citing works
“Many studies have established relationships between diffusion models and neurophysiological measures such as single cell recordings in rodents and monkeys [52,88,105,143–148], EEG [42,43,149,150], fMRI [42,43,139,151–155], transcranial magnetic stimulation (TMS) [156], and transcranial alternating current stimulation…”
BackgroundRatcliff, R., & Frank, M. J. (2012) · Neural Computation · doi:10.1162/neco_a_00270 · Cited by 203 · cited 2× · see citing works
“The conflict conditions were modeled with collapsing decision boundaries that accounted for the shifts in RT distributions [69].”
BackgroundMethodSelf-citationModeling response signal and response time data☆
Ratcliff, R. (2006) · Cognitive Psychology · doi:10.1016/j.cogpsych.2005.10.002 · Cited by 201 · cited 3× · see citing works
“In application of the diffusion model to response signal data [17,54,55], there are two response boundaries just as for the usual two-choice task.”
BackgroundMethodSelf-citationCausal Role of Dorsolateral Prefrontal Cortex in Human Perceptual Decision Making
Philiastides, M. G., Auksztulewicz, R., Heekeren, H. R., & Blankenburg, F. (2011) · Current Biology · doi:10.1016/j.cub.2011.04.034 · Cited by 200 · cited 1× · see citing works
“Many studies have established relationships between diffusion models and neurophysiological measures such as single cell recordings in rodents and monkeys [52,88,105,143–148], EEG [42,43,149,150], fMRI [42,43,139,151–155], transcranial magnetic stimulation (TMS) [156], and transcranial alternating current stimulation…”
BackgroundA diffusion model analysis of the effects of aging on letter discrimination.
Thapar, A., Ratcliff, R., & McKoon, G. (2003) · Psychology and Aging · doi:10.1037/0882-7974.18.3.415 · Cited by 195 · cited 3× · see citing works
“Overall, error responses are slower than correct responses, not the result found elsewhere [14,16,37,102].”
MethodResultSelf-citationIndividual differences and fitting methods for the two-choice diffusion model of decision making.
Ratcliff, R., & Childers, R. (2015) · Decision · doi:10.1037/dec0000030 · Cited by 192 · see citing works
Self-citationNormative evidence accumulation in unpredictable environments
Glaze, C. M., Kable, J. W., & Gold, J. I. (2015) · eLife · doi:10.7554/elife.08825 · Cited by 190 · cited 1× · see citing works
“Diffusion and related model analyses of tasks in which stimulus information varies from moment to moment provide a theoretical account of change detection [67,68] in which evidence accumulation has to be balanced against changes in the stimulus environment.”
BackgroundBennur, S., & Gold, J. I. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.4417-10.2011 · Cited by 188 · cited 1× · see citing works
“, [51–53]).”
Adaptive Gain Control during Human Perceptual Choice
Cheadle, S. W., Wyart, V., Tsetsos, K., Myers, N. E., de Gardelle, V., Castañón, S. H., & Summerfield, C. (2014) · Neuron · doi:10.1016/j.neuron.2014.01.020 · Cited by 186 · cited 3× · see citing works
“Recency weighting has been explained by various mechanisms, including a gain control process [95] and a working memory capacity limitation [97].”
BackgroundRobust averaging during perceptual judgment
de Gardelle, V., & Summerfield, C. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1104517108 · Cited by 183 · cited 2× · see citing works
“The stimuli have included random dots [44], colored lights [92] and patches [93], line segments [94], sinusoidal gratings [95], clicks and visual pulses [52], and geometric shapes [96], with interelement intervals ranging from a few tens of milliseconds to several hundred milliseconds or longer.”
BackgroundFrom salience to saccades: multiple-alternative gated stochastic accumulator model of visual search
Purcell, B. A., Schall, J. D., Logan, G. D., & Palmeri, T. J. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.4622-11.2012 · Cited by 179 · cited 1× · see citing works
“Diffusion and other models have been applied to many basic perceptual and memory tasks such as item and associative recognition [2,15,121,122,132], lexical decision [121,133], perceptual tasks including brightness, letter, motion, visual search, contrast, orientation discrimination tasks [7,14,16,44,103,104,134,135],…”
MethodTemporal and spatial integration in dynamic random-dot stimuli
Watamaniuk, S. N. J., & Sekuler, R. (1992) · Vision Research · doi:10.1016/0042-6989(92)90097-3 · Cited by 177 · cited 1× · see citing works
“Integration time, as assessed by a change in accuracy thresholds as a function of stimulus duration, was shown to be the same (around 400 ms) for high and low coherence stimuli [107].”
BackgroundSleep deprivation affects multiple distinct cognitive processes
Ratcliff, R., & Dongen, H. P. A. V. (2009) · Psychonomic Bulletin & Review · doi:10.3758/pbr.16.4.742 · Cited by 175 · cited 1× · see citing works
“Diffusion model analyses have been used to study manipulations of state such as sleep deprivation [161], hypoglycemia [162], and alcohol [163].”
MethodSelf-citationA diffusion model analysis of the effects of aging on brightness discrimination
Ratcliff, R., Thapar, A., & McKoon, G. (2003) · Perception & Psychophysics · doi:10.3758/bf03194580 · Cited by 171 · cited 4× · see citing works
“Overall, error responses are slower than correct responses, not the result found elsewhere [14,16,37,102].”
MethodResultInfluentialSelf-citationRinkenauer, G., Osman, A. M., Ulrich, R., Müller‐Gethmann, H., & Mattes, S. (2004) · Journal of Experimental Psychology General · doi:10.1037/0096-3445.133.2.261 · Cited by 171 · see citing works
Attention, Reward, and Information Seeking
Gottlieb, J., Hayhoe, M., Hikosaka, O., & Rangel, A. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.3270-14.2014 · Cited by 168 · cited 1× · see citing works
“This and other diffusion models have been used to account for, among other things, value-based decision, social choice, and purchasing decisions [150,152,153,157,207–210].”
MethodPassino, K. M., Seeley, T. D., & Visscher, P. K. (2007) · Behavioral Ecology and Sociobiology · doi:10.1007/s00265-007-0468-1 · Cited by 163 · see citing works
Strength theory of decision rules and latency in retrieval from short-term memory
Norman, D., & Wickelgren, W. A. (1969) · Journal of Mathematical Psychology · doi:10.1016/0022-2496(69)90002-9 · Cited by 162 · see citing works
The Timescale of Perceptual Evidence Integration Can Be Adapted to the Environment
Ossmy, O., Moran, R., Pfeffer, T. A., Tsetsos, K., Usher, M., & Donner, T. H. (2013) · Current Biology · doi:10.1016/j.cub.2013.04.039 · Cited by 162 · cited 1× · see citing works
“Diffusion and related model analyses of tasks in which stimulus information varies from moment to moment provide a theoretical account of change detection [67,68] in which evidence accumulation has to be balanced against changes in the stimulus environment.”
BackgroundA Probabilistic, Dynamic, and Attribute-wise Model of Intertemporal Choice
Dai, J., & Busemeyer, J. (2014) · Journal of experimental psychology. General · doi:10.1037/a0035976 · Cited by 161 · cited 1× · see citing works
“DFT has also been used to account for puzzling reversals in preference between choice and price of gambles [203] and to explain choice and RTs for intertemporal choice [204].”
MethodRatcliff, R., & Starns, J. J. (2013) · Psychological Review · doi:10.1037/a0033152 · Cited by 160 · see citing works
Self-citationThe Effects of Aging and IQ on Item and Associative Memory
Ratcliff, R., Thapar, A., & McKoon, G. (2011) · Journal of experimental psychology. General · doi:10.1037/a0023810 · Cited by 157 · cited 2× · see citing works
“Diffusion and other models have been applied to many basic perceptual and memory tasks such as item and associative recognition [2,15,121,122,132], lexical decision [121,133], perceptual tasks including brightness, letter, motion, visual search, contrast, orientation discrimination tasks [7,14,16,44,103,104,134,135],…”
MethodSelf-citationPreference reversal in multiattribute choice.
Tsetsos, K., Usher, M., & Chater, N. (2010) · Psychology Review · doi:10.1037/a0020580 · Cited by 154 · see citing works
Fast guesses in choice reaction time
Ollman, R. (1966) · Psychonomic Science · doi:10.3758/bf03328004 · Cited by 154 · cited 1× · see citing works
“Changes in starting point and drift rates can also explain sequential effects [21,28–33] in which trial-by-trial variations in RT are partly determined by the prior stimulus and the prior response.”
BackgroundThe dynamics of choice among multiple alternatives
McMillen, T., & Holmes, P. (2006) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2005.10.003 · Cited by 153 · cited 1× · see citing works
“Recent studies have examined models for multialternative decision making that use diffusion processes [24,41,72,79,80,181–189] and a number of algorithms have been used: (i) Independent racing accumulators with termination when one reaches its decision criterion.”
MethodParallel and serial processes in visual search.
Thornton, T. L., & Gilden, D. L. (2007) · Psychological Review · doi:10.1037/0033-295x.114.1.71 · Cited by 152 · cited 1× · see citing works
“Diffusion and other models have been applied to many basic perceptual and memory tasks such as item and associative recognition [2,15,121,122,132], lexical decision [121,133], perceptual tasks including brightness, letter, motion, visual search, contrast, orientation discrimination tasks [7,14,16,44,103,104,134,135],…”
MethodA dynamic, stochastic, computational model of preference reversal phenomena.
Johnson, J. G., & Busemeyer, J. (2005) · Psychology Review · doi:10.1037/0033-295x.112.4.841 · Cited by 152 · see citing works
A Theory of Order Relations in Perceptual Matching
Ratcliff, R. (2005) · doi:10.1037//0033-295x.88.6.552 · Cited by 152 · cited 1×
“The standard model that we will discuss was developed by Ratcliff in the 1970s [2] and has only changed in assuming a single diffusion process instead of racing processes [3] and in adding across trial variability in starting point [4,5] and nondecision time [6].”
MethodSelf-citationAccounting for cognitive aging: context processing, inhibition or processing speed?
Rush, B. K., Barch, D. M., & Braver, T. S. (2006) · Aging Neuropsychology and Cognition · doi:10.1080/13825580600680703 · Cited by 150 · see citing works
SOME ALTERNATIVE STOCHASTIC MODELS OF CHOICE1
Audley, R., & Pike, A. (1965) · British Journal of Mathematical and Statistical Psychology · doi:10.1111/j.2044-8317.1965.tb00342.x · Cited by 147 · cited 1× · see citing works
“There is no simple generalization of the two-choice diffusion model to multiple alternatives [9,180].”
BackgroundShort-Term Memory Scanning Viewed as Exemplar-Based Categorization
Nosofsky, R., Little, D. R., Donkin, C., & Fifić, M. (2011) · Psychology Review · doi:10.1037/a0022494 · Cited by 144 · cited 1× · see citing works
“Diffusion and other models have been applied to many basic perceptual and memory tasks such as item and associative recognition [2,15,121,122,132], lexical decision [121,133], perceptual tasks including brightness, letter, motion, visual search, contrast, orientation discrimination tasks [7,14,16,44,103,104,134,135],…”
MethodThe precision of value-based choices depends causally on fronto-parietal phase coupling
Polanía, R. H., Moisa, M., Opitz, A., Grueschow, M., & Ruff, C. C. (2015) · Nature Communications · doi:10.1038/ncomms9090 · Cited by 144 · cited 1× · see citing works
“Many studies have established relationships between diffusion models and neurophysiological measures such as single cell recordings in rodents and monkeys [52,88,105,143–148], EEG [42,43,149,150], fMRI [42,43,139,151–155], transcranial magnetic stimulation (TMS) [156], and transcranial alternating current stimulation…”
BackgroundDiederich, A., & Busemeyer, J. (2006) · Perception & Psychophysics · doi:10.3758/bf03193669 · Cited by 143 · see citing works
Diederich, A., & Busemeyer, J. R. (2003) · Journal of Mathematical Psychology · doi:10.1016/s0022-2496(03)00003-8 · Cited by 142 · cited 1× · see citing works
“Fitting packages are available (but subject to misuse if not understood): fast-dm [124], DMAT [125], HDDM [126], as well as alternative fitting methods [127].”
BackgroundSmith, P. L., Ratcliff, R., & Wolfgang, B. J. (2004) · Vision Research · doi:10.1016/j.visres.2004.01.002 · Cited by 141 · cited 2× · see citing works
“Diffusion and other models have been applied to many basic perceptual and memory tasks such as item and associative recognition [2,15,121,122,132], lexical decision [121,133], perceptual tasks including brightness, letter, motion, visual search, contrast, orientation discrimination tasks [7,14,16,44,103,104,134,135],…”
BackgroundMethodSelf-citationStochastic models for choice reaction time with applications to experimental results
Falmagne, J. (1965) · Journal of Mathematical Psychology · doi:10.1016/0022-2496(65)90018-0 · Cited by 137 · cited 1× · see citing works
“Changes in starting point and drift rates can also explain sequential effects [21,28–33] in which trial-by-trial variations in RT are partly determined by the prior stimulus and the prior response.”
BackgroundRoxin, A., & Ledberg, A. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000046 · Cited by 135 · see citing works
Dosher, B. (1984) · Cognitive Psychology · doi:10.1016/0010-0285(84)90019-7 · Cited by 135 · cited 2× · see citing works
“Response signal studies from the 1980s show that drift rate can change from stimulus-to-signal intervals that are about the mean RTs in the usual procedure (600 ms or less) up to intervals of around 2 s [56–59].”
BackgroundMulder, M. J., Bos, D., Weusten, J. M. H., van Belle, J., van Dijk, S. C., Simen, P., van Engeland, H., & Durston, S. (2010) · Biological Psychiatry · doi:10.1016/j.biopsych.2010.07.031 · Cited by 131 · cited 1× · see citing works
“They have been used to study individual differences in IQ, working memory, and reading measures [120–123], and to examine deficits in populations such as aphasics [164], older adults and children [13,14,121,122], children [165], low literacy adults [166], dyslexics [167], attention deficit hyperactivity disorder (ADHD…”
MethodDo humans make good decisions?
Summerfield, C., & Tsetsos, K. (2014) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2014.11.005 · Cited by 131 · cited 1× · see citing works
“There has been a recent resurgence of interest in the application of diffusion and random walk models to expanded judgment tasks, especially in neuroscience, motivated in part by a renewed interest in optimality [76,90,91].”
BackgroundMormann, M., Malmaud, J., Huth, A. G., Christof Koch, A., & Rangel, A. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1901533 · Cited by 130 · cited 1× · see citing works
“Note that there are other ways of producing fast or slow errors (relative to correct responses) such as collapsing bounds (discussed later), but few of these have received extensive testing [41].”
BackgroundWhat cognitive processes drive response biases? A diffusion model analysis
Leite, F. P., & Ratcliff, R. (2011) · Judgment and Decision Making · doi:10.1017/s1930297500002680 · Cited by 129 · cited 3× · see citing works
“Recent studies have examined models for multialternative decision making that use diffusion processes [24,41,72,79,80,181–189] and a number of algorithms have been used: (i) Independent racing accumulators with termination when one reaches its decision criterion.”
BackgroundMethodSelf-citationSelf-referential forces are sufficient to explain different dendritic morphologies
Memelli, H., Torben-Nielsen, B., & Kozloski, J. (2013) · Front. Neuroinform. · doi:10.3389/fninf.2013.00001 · Cited by 127 · see citing works
Unfalsifiability and mutual translatability of major modeling schemes for choice reaction time.
Jones, M., & Dzhafarov, E. N. (2013) · Psychological Review · doi:10.1037/a0034190 · Cited by 125 · cited 3× · see citing works
“If the forms of across-trial variability distributions are unconstrained, then Jones and Dzhafarov argued that the diffusion model and other evidence accumulation models can exactly match any data (response probabilities and RT distributions), rendering the models unfalsifiable [197].”
BackgroundA Common Mechanism Underlying Food Choice and Social Decisions
Krajbich, I. M., Hare, T. A., Bartling, B., Morishima, Y., & Fehr, E. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004371 · Cited by 123 · cited 1× · see citing works
“This and other diffusion models have been used to account for, among other things, value-based decision, social choice, and purchasing decisions [150,152,153,157,207–210].”
MethodRatcliff, R., Love, J., Thompson, C. A., & Opfer, J. E. (2011) · Child Development · doi:10.1111/j.1467-8624.2011.01683.x · Cited by 122 · cited 1× · see citing works
“They have been used to study individual differences in IQ, working memory, and reading measures [120–123], and to examine deficits in populations such as aphasics [164], older adults and children [13,14,121,122], children [165], low literacy adults [166], dyslexics [167], attention deficit hyperactivity disorder (ADHD…”
MethodSelf-citationPredicting Perceptual Decision Biases from Early Brain Activity
Bode, S., Sewell, D. K., Lilburn, S. D., Forte, J. D., Smith, P. L., & Stähl, J. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.1708-12.2012 · Cited by 118 · see citing works
Self-citationA note on modeling accumulation of information when the rate of accumulation changes over time
Ratcliff, R. (1980) · Journal of Mathematical Psychology · doi:10.1016/0022-2496(80)90006-1 · Cited by 112 · see citing works
Self-citationTemporal Integration of Olfactory Perceptual Evidence in Human Orbitofrontal Cortex
Bowman, N. E., Kording, K. P., & Gottfried, J. A. (2012) · Neuron · doi:10.1016/j.neuron.2012.06.035 · Cited by 108 · cited 1× · see citing works
“Models with collapsing bounds have been identified with urgency gating signals in some recent theoretical accounts of optimal coding in neural populations and in empirical single cell recording studies [41,78,82–85].”
MethodApplication of the diffusion model to two-choice tasks for adults 75-90 years old.
Ratcliff, R., Thapar, A., & McKoon, G. (2007) · Psychology and Aging · doi:10.1037/0882-7974.22.1.56 · Cited by 106 · cited 2× · see citing works
“Overall, error responses are slower than correct responses, not the result found elsewhere [14,16,37,102].”
ResultSelf-citationStarns, J., Ratcliff, R., & McKoon, G. (2011) · Cognitive Psychology · doi:10.1016/j.cogpsych.2011.10.002 · Cited by 106 · see citing works
Self-citationSimilarity information versus relational information: differences in the time course of retrieval.
Ratcliff, R., & McKoon, G. (1989) · Cognitive Psychology · doi:10.1016/0010-0285(89)90005-4 · Cited by 103 · cited 2× · see citing works
“Response signal studies from the 1980s show that drift rate can change from stimulus-to-signal intervals that are about the mean RTs in the usual procedure (600 ms or less) up to intervals of around 2 s [56–59].”
BackgroundSelf-citationRelative Expected Instantaneous Loss Bounds
Forster, J., & Warmuth, M. (2000) · Journal of computer and system sciences (Print) · doi:10.1006/jcss.2001.1798 · Cited by 101 · cited 1×
“Recent studies have examined models for multialternative decision making that use diffusion processes [24,41,72,79,80,181–189] and a number of algorithms have been used: (i) Independent racing accumulators with termination when one reaches its decision criterion.”
MethodRatcliff, R., & Smith, P. L. (2010) · Journal of Experimental Psychology General · doi:10.1037/a0018128 · Cited by 100 · cited 3× · see citing works
“(E,F) Fits to a dataset that has large differences in the leading edge of the RT distribution, changes too large for a diffusion model with only drift rate changing over conditions to fit (from Experiment 1 [38]).”
BackgroundInfluentialSelf-citationPerceptual Decisions between Multiple Directions of Visual Motion
Niwa, M., & Ditterich, J. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.5564-07.2008 · Cited by 98 · cited 1× · see citing works
“Recent studies have examined models for multialternative decision making that use diffusion processes [24,41,72,79,80,181–189] and a number of algorithms have been used: (i) Independent racing accumulators with termination when one reaches its decision criterion.”
MethodProcessing of unattended emotional visual scenes.
Calvo, M. G., & Nummenmaa, L. (2007) · Journal of Experimental Psychology General · doi:10.1037/0096-3445.136.3.347 · Cited by 94 · see citing works
Parameter variability and distributional assumptions in the diffusion model.
Ratcliff, R. (2012) · Psychological Review · doi:10.1037/a0030775 · Cited by 92 · cited 2× · see citing works
“In the standard model, drift rate is normally distributed across trials with standard deviation (SD) h, the starting point is uniformly distributed with range sz (starting point variability is equivalent to variability in the boundaries), and nondecision time is uniformly distributed with range st (Ratcliff examined t…”
BackgroundSelf-citationA neuro-computational model of economic decisions
Rustichini, A., & Padoa‐Schioppa, C. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00184.2015 · Cited by 92 · cited 1× · see citing works
“The attractor model developed by Wang assumes two pools of excitatory neurons coupled via a third pool of inhibitory neurons [174,175].”
BackgroundMaking Decisions with Unknown Sensory Reliability
Denève, S. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00075 · Cited by 87 · cited 1× · see citing works
“cases where it is possible to set different boundaries for each difficulty level, human decision makers seem fairly efficient at setting those many boundaries in an optimal manner [74,75].”
BackgroundOptimal decision making in heterogeneous and biased environments
Moran, R. (2014) · Psychonomic Bulletin & Review · doi:10.3758/s13423-014-0669-3 · Cited by 86 · cited 1× · see citing works
“A pure (Wiener) diffusion process, with no across-trial variability, can be viewed as a continuous-time log-likelihood ratio accumulator, and is optimal in the same sense [72,73].”
BackgroundMeasuring psychometric functions with the diffusion model.
Ratcliff, R. (2014) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/a0034954 · Cited by 85 · cited 1× · see citing works
“These conditions are weighted most heavily in determining some model parameters and then RTs alone are sufficient to determine drift rates [118,119].”
BackgroundSelf-citationThe Limits of Deliberation in a Perceptual Decision Task
Zariwala, H. A., Kepecs, A., Uchida, N., Hirokawa, J., & Mainen, Z. (2013) · Neuron · doi:10.1016/j.neuron.2013.02.010 · Cited by 85 · see citing works
Starns, J., & Ratcliff, R. (2011) · Psychonomic Bulletin & Review · doi:10.3758/s13423-011-0189-3 · Cited by 85 · cited 1× · see citing works
“For difficult relative to easy blocks, subjects slow down when it is reward rate optimal for them to speed up [200].”
BackgroundSelf-citationA comparison of four methods for simulating the diffusion process
Tuerlinckx, F., Maris, E., Ratcliff, R., & de Boeck, P. A. L. (2001) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03195402 · Cited by 82 · cited 2× · see citing works
“(B) Shows the mean and plus and minus 1 standard deviation ( 1 SD) average paths from simulations (using the random walk method [171]).”
MethodSelf-citationDysphoria and memory for emotional material: A diffusion-model analysis
White, C. N., Ratcliff, R., Vasey, M. W., & McKoon, G. (2008) · Cognition & Emotion · doi:10.1080/02699930801976770 · Cited by 81 · cited 1× · see citing works
“They have been used to study individual differences in IQ, working memory, and reading measures [120–123], and to examine deficits in populations such as aphasics [164], older adults and children [13,14,121,122], children [165], low literacy adults [166], dyslexics [167], attention deficit hyperactivity disorder (ADHD…”
MethodSelf-citationModeling Regularities in Response Time and Accuracy Data With the Diffusion Model
Ratcliff, R., Smith, P. L., & McKoon, G. (2015) · Current Directions in Psychological Science · doi:10.1177/0963721415596228 · Cited by 79 · see citing works
Self-citationA diffusion model decomposition of the effects of alcohol on perceptual decision making
van Ravenzwaaij, D., Dutilh, G., & Wagenmakers, E. (2011) · Psychopharmacology · doi:10.1007/s00213-011-2435-9 · Cited by 78 · cited 1× · see citing works
“Diffusion model analyses have been used to study manipulations of state such as sleep deprivation [161], hypoglycemia [162], and alcohol [163].”
MethodModeling individual differences in response time and accuracy in numeracy
Ratcliff, R., Thompson, C. A., & McKoon, G. (2015) · Cognition · doi:10.1016/j.cognition.2014.12.004 · Cited by 77 · cited 1× · see citing works
“Diffusion and other models have been applied to many basic perceptual and memory tasks such as item and associative recognition [2,15,121,122,132], lexical decision [121,133], perceptual tasks including brightness, letter, motion, visual search, contrast, orientation discrimination tasks [7,14,16,44,103,104,134,135],…”
MethodSelf-citationPriming and retrieval from short-term memory: A speed accuracy trade-off analysis
Wickelgren, W. A., Corbett, A. T., & Dosher, B. A. (1980) · Journal of Verbal Learning and Verbal Behavior · doi:10.1016/s0022-5371(80)90276-5 · Cited by 73 · see citing works
The EZ diffusion method: Too EZ?
Ratcliff, R. (2008) · Psychonomic Bulletin & Review · doi:10.3758/pbr.15.6.1218 · Cited by 73 · cited 1× · see citing works
“These conditions are weighted most heavily in determining some model parameters and then RTs alone are sufficient to determine drift rates [118,119].”
BackgroundSelf-citationSmith, P. L. (2010) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2009.12.002 · Cited by 70 · see citing works
Self-citationAging and response times: a comparison of sequential sampling models
Ratcliff, R., Thapar, A., Smith, P. L., & McKoon, G. (2005) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198566427.003.0001 · Cited by 69 · cited 1× · see citing works
“Other current sequential sampling models usually offer similar explanations of phenomena in terms of the behavior of model components (parameters) [130,131].”
BackgroundSelf-citationSubjective probability in choice-reaction experiments ☆
Laming, D. (1969) · Journal of Mathematical Psychology · doi:10.1016/0022-2496(69)90030-3 · Cited by 69 · see citing works
Prior probability and feature predictability interactively bias perceptual decisions
Dunovan, K., Tremel, J. J., & Wheeler, M. E. (2014) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2014.06.024 · Cited by 68 · cited 1× · see citing works
“If the probability that one alternative is tested is made higher than the other, then the starting point moves toward the higher probability boundary (Figure 2D) [5,22–26].”
BackgroundSpeed and Accuracy in the Processing of False Statements About Semantic Information
Ratcliff, R., & McKoon, G. (1982) · doi:10.1037//0278-7393.8.1.16 · Cited by 67 · cited 2×
“Response signal studies from the 1980s show that drift rate can change from stimulus-to-signal intervals that are about the mean RTs in the usual procedure (600 ms or less) up to intervals of around 2 s [56–59].”
BackgroundSelf-citationResponse times from ensembles of accumulators
Zandbelt, B. B., Purcell, B. A., Palmeri, T. J., Logan, G. D., & Schall, J. D. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1310577111 · Cited by 60 · cited 1× · see citing works
“Recent studies have examined models for multialternative decision making that use diffusion processes [24,41,72,79,80,181–189] and a number of algorithms have been used: (i) Independent racing accumulators with termination when one reaches its decision criterion.”
MethodDiscriminating between the frequency of occurrence of two alternative events
Vickers, D., Caudrey, D., & Willson, R. (1971) · Acta Psychologica · doi:10.1016/0001-6918(71)90018-7 · Cited by 56 · cited 1× · see citing works
“The stimuli have included random dots [44], colored lights [92] and patches [93], line segments [94], sinusoidal gratings [95], clicks and visual pulses [52], and geometric shapes [96], with interelement intervals ranging from a few tens of milliseconds to several hundred milliseconds or longer.”
BackgroundDoes the name-race implicit association test measure racial prejudice?
van Ravenzwaaij, D., van der Maas, H. L. J., & Wagenmakers, E. (2011) · Experimental Psychology · doi:10.1027/1618-3169/a000093 · Cited by 56 · cited 1×
“Other tasks that have more interdisciplinary relationships include stop signal tasks [137,138], conflict tasks [70,71], reinforcement learning [69,139], preferential choice and value-based decisions (Box 7), and social decisions [140,141] (Box 7).”
BackgroundRouder, J. (2000) · Journal of Experimental Psychology: Human Perception and Performance · doi:10.1037/0096-1523.26.1.359 · Cited by 53
Smith, P. L., Ratcliff, R., & Sewell, D. K. (2013) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2013.05.007 · Cited by 51 · cited 1× · see citing works
“RT distributions from the dynamic noise task were modeled with a diffusion model with a time-varying drift, in which the growth of the drift rate depended on external noise [100].”
MethodSelf-citationA diffusion model account of normal and impaired readers
Ratcliff, R., Perea, M., Colangelo, A., & Buchanan, L. (2004) · Brain and Cognition · doi:10.1016/j.bandc.2004.02.051 · Cited by 51 · cited 1× · see citing works
“They have been used to study individual differences in IQ, working memory, and reading measures [120–123], and to examine deficits in populations such as aphasics [164], older adults and children [13,14,121,122], children [165], low literacy adults [166], dyslexics [167], attention deficit hyperactivity disorder (ADHD…”
MethodSelf-citationNOTE ON A SIMPLE FIXED‐POINT PROPERTY OF BINARY MIXTURES
Falmagne, J.-C. (1968) · doi:10.1111/j.2044-8317.1968.tb00403.x · Cited by 50
Linking cognitive and reaching trajectories via intermittent movement control
Friedman, J., Brown, S. D., & Finkbeiner, M. S. (2013) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2013.06.005 · Cited by 49 · cited 1× · see citing works
“, [61]).”
Self-citationA diffusion modeling approach to understanding contextual cueing effects in children with ADHD
Weigard, A. S., & Huang‐Pollock, C. L. (2014) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12250 · Cited by 48 · cited 2× · see citing works
“They have been used to study individual differences in IQ, working memory, and reading measures [120–123], and to examine deficits in populations such as aphasics [164], older adults and children [13,14,121,122], children [165], low literacy adults [166], dyslexics [167], attention deficit hyperactivity disorder (ADHD…”
MethodPietsch, A., & Vickers, D. (1997) · The Journal of general psychology · doi:10.1080/00221309709595520 · Cited by 47 · cited 2× · see citing works
“Recency weighting has been explained by various mechanisms, including a gain control process [95] and a working memory capacity limitation [97].”
BackgroundResultDiscriminating evidence accumulation from urgency signals in speeded decision making
Hawkins, G. E., Wagenmakers, E., Ratcliff, R., & Brown, S. D. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00088.2015 · Cited by 45 · cited 2× · see citing works
“A large-scale investigation of data from hundreds of participants in ten different studies, from three different laboratories addressed these questions empirically [86].”
BackgroundSelf-citationWinkel, J., Keuken, M. C., van Maanen, L., Wagenmakers, E., & Forstmann, B. U. (2014) · Psychonomic Bulletin & Review · doi:10.3758/s13423-013-0551-8 · Cited by 45 · cited 1× · see citing works
“Some investigations have supported nonstationarity but have not generated the critical predictions for correct and error RT distributions ([52,87,111–113], see also [45,86,114]).”
BackgroundModeling evidence accumulation with partial loss in expanded judgment.
Smith, P. L., & Vickers, D. (1989) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/0096-1523.15.4.797 · Cited by 45 · cited 2× · see citing works
“When they have been analyzed [94], the decision time distributions were much more variable than those found in single stimulus tasks and could not be predicted by a random walk model with normally distributed increments, which is the discrete time counterpart of the diffusion model with no across-trial variability in…”
BackgroundSelf-citationSpecifying theories of developmental dyslexia: a diffusion model analysis of word recognition.
Zeguers, M. H. T., Snellings, P., Tijms, J., Weeda, W., Tamboer, P., Bexkens, A., & Huizenga, H. (2011) · Developmental Science · doi:10.1111/j.1467-7687.2011.01091.x · Cited by 44 · cited 1× · see citing works
“They have been used to study individual differences in IQ, working memory, and reading measures [120–123], and to examine deficits in populations such as aphasics [164], older adults and children [13,14,121,122], children [165], low literacy adults [166], dyslexics [167], attention deficit hyperactivity disorder (ADHD…”
MethodThe Ising Decision Maker: A binary stochastic network for choice response time.
Verdonck, S., & Tuerlinckx, F. (2014) · Psychological Review · doi:10.1037/a0037012 · Cited by 39 · cited 1× · see citing works
“The attractor network model, the Ising Decision Model [177], is based on a stochastic Hopfield network.”
MethodAn optimal adjustment procedure to minimize experiment time in decisions with multiple alternatives
Hawkins, G. E., Brown, S. D., Steyvers, M., & Wagenmakers, E. (2012) · Psychonomic Bulletin & Review · doi:10.3758/s13423-012-0216-z · Cited by 39 · cited 2× · see citing works
“Recent studies have examined models for multialternative decision making that use diffusion processes [24,41,72,79,80,181–189] and a number of algorithms have been used: (i) Independent racing accumulators with termination when one reaches its decision criterion.”
BackgroundMethodSelf-citationThe diffusion model is not a deterministic growth model: Comment on Jones and Dzhafarov (2014).
Smith, P. L., Ratcliff, R., & McKoon, G. (2014) · Psychological Review · doi:10.1037/a0037667 · Cited by 37 · cited 1× · see citing works
“Below are considerations about this argument [198].”
Self-citationAging and IQ effects on associative recognition and priming in item recognition
McKoon, G., & Ratcliff, R. (2012) · Journal of Memory and Language · doi:10.1016/j.jml.2011.12.001 · Cited by 35 · cited 1× · see citing works
“Diffusion and other models have been applied to many basic perceptual and memory tasks such as item and associative recognition [2,15,121,122,132], lexical decision [121,133], perceptual tasks including brightness, letter, motion, visual search, contrast, orientation discrimination tasks [7,14,16,44,103,104,134,135],…”
MethodSelf-citationDiffusive Information Accumulation by Minimal Recurrent Neural Models of Decision Making
Smith, P. L., & McKenzie, C. (2011) · Neural Computation · doi:10.1162/neco_a_00150 · Cited by 34 · cited 1× · see citing works
“Diffusive noise in a decision process can be derived from a Poisson shot noise model of stimulus representations [178,179].”
MethodSelf-citationAging and Predicting Inferences: A Diffusion Model Analysis
McKoon, G., & Ratcliff, R. (2012) · Journal of Memory and Language · doi:10.1016/j.jml.2012.11.002 · Cited by 32 · cited 1× · see citing works
“Diffusion and other models have been applied to many basic perceptual and memory tasks such as item and associative recognition [2,15,121,122,132], lexical decision [121,133], perceptual tasks including brightness, letter, motion, visual search, contrast, orientation discrimination tasks [7,14,16,44,103,104,134,135],…”
MethodSelf-citationHolmes, P. J., & Cohen, J. D. (2014) · Topics in Cognitive Science · doi:10.1111/tops.12084 · Cited by 31 · cited 1× · see citing works
“There has been a recent resurgence of interest in the application of diffusion and random walk models to expanded judgment tasks, especially in neuroscience, motivated in part by a renewed interest in optimality [76,90,91].”
BackgroundContext Effects in Multi‐Alternative Decision Making: Empirical Data and a Bayesian Model
Hawkins, G. E., Brown, S. D., Steyvers, M., & Wagenmakers, E. (2012) · Cognitive Science · doi:10.1111/j.1551-6709.2011.01221.x · Cited by 30 · cited 2× · see citing works
“Recent studies have examined models for multialternative decision making that use diffusion processes [24,41,72,79,80,181–189] and a number of algorithms have been used: (i) Independent racing accumulators with termination when one reaches its decision criterion.”
BackgroundMethodSelf-citationMcKoon, G., & Ratcliff, R. (2015) · Cognition · doi:10.1016/j.cognition.2015.10.009 · Cited by 24 · cited 1× · see citing works
“They have been used to study individual differences in IQ, working memory, and reading measures [120–123], and to examine deficits in populations such as aphasics [164], older adults and children [13,14,121,122], children [165], low literacy adults [166], dyslexics [167], attention deficit hyperactivity disorder (ADHD…”
MethodSelf-citationComputational approaches to visual decision making.
Ditterich, J. (2006) · Novartis Foundation symposium · doi:10.1002/9780470034989.ch10 · Cited by 20 · cited 1× · see citing works
“Another assumption with much the same effect is that fixed boundaries are maintained, but an ‘urgency signal’ is added to the accumulated evidence [82,83].”
Background
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Research works printed but not linked: 19
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Cited by
Sorted by citations
The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences
Hedge, C., Powell, G., & Sumner, P. (2018) · Behavior Research Methods · doi:10.3758/s13428-017-0935-1 · Cited by 1,457 · see citing works
Guided Search 6.0: an updated model of visual search
Wolfe, J. M. (2021) · Psychonomic Bulletin & Review · doi:10.3758/s13423-020-01859-9 · Cited by 703 · see citing works
Large-scale analysis of test–retest reliabilities of self-regulation measures
Enkavi, A. Z., Eisenberg, I. W., Bissett, P. G., Mazza, G. L., MacKinnon, D. P., Marsch, L. A., & Poldrack, R. A. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1818430116 · Cited by 489 · see citing works
“We compare the reliability of raw DVs (response times and accuracies) to parameters of the DDM, a well-established model that addresses speed–accuracy trade-offs and offers interpretable latent variables (34, 35).”
MethodAdults with autism overestimate the volatility of the sensory environment
Lawson, R. P., Mathys, C., & Rees, G. (2017) · Nature Neuroscience · doi:10.1038/nn.4615 · Cited by 470 · see citing works
Individual Differences in Language Acquisition and Processing.
Kidd, E., Donnelly, S., & Christiansen, M. H. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.11.006 · Cited by 407 · see citing works
“However, these raw means reflect the combination of many distinct cognitive parameters, such as encoding, evidence accumulation, response thresholds, and response execution times [143].”
BackgroundMissing-Value, M.-V. (2023) · doi:10.4324/9781315127675-7 · Cited by 375
“Participants’ choice behavior was consistent with that observed in previous studies and predicted by prevailing models of evidence accumulation (Pisauro et al., 2017; Polania et al., 2014; Ratcliff et al., 2016): participants chose faster (LMM fixed effect: b = -348.”
ResultInfluentialCombining speed and accuracy to control for speed-accuracy trade-offs(?)
Liesefeld, H., & Janczyk, M. (2018) · Behavior Research Methods · doi:10.3758/s13428-018-1076-x · Cited by 359 · see citing works
Suboptimality in perceptual decision making
Rahnev, D., & Denison, R. N. (2018) · Behavioral and Brain Sciences · doi:10.1017/s0140525x18000936 · Cited by 342 · see citing works
Motivation and Cognitive Control in DepressionPreprint
Grahek, I., Shenhav, A., Musslick, S., Krebs, R., & Koster, E. (2018) · bioRxiv · doi:10.1101/500561 · Cited by 322 · see citing works
Perceptual Decision Making in Rodents, Monkeys, and Humans
Hanks, T. D., & Summerfield, C. (2017) · Neuron · doi:10.1016/j.neuron.2016.12.003 · Cited by 388 · see citing works
“Reprinted from Katz et al. (2016). signal quality, time pressure, and the relative probability or reward value of either response (Bogacz et al., 2006; Ratcliff and Rouder, 1998; Ratcliff et al., 2016).”
BackgroundSelf-Control as Value-Based Choice
Berkman, E. T., Hutcherson, C. A. C., Livingston, J., Kahn, L. E., & Inzlicht, M. (2017) · Current Directions in Psychological Science · doi:10.1177/0963721417704394 · Cited by 348 · see citing works
Physiological markers of motor inhibition during human behavior
Duque, J., Greenhouse, I., Labruna, L., & Ivry, R. (2017) · Trends in Neurosciences · doi:10.1016/j.tins.2017.02.006 · Cited by 250 · see citing works
Resolving uncertainty in a social world
FeldmanHall, O., & Shenhav, A. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0590-x · Cited by 276 · see citing works
Inferring Causality from Noninvasive Brain Stimulation in Cognitive Neuroscience
Bergmann, T. O., & Hartwigsen, G. (2020) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01591 · Cited by 266 · see citing works
Mice alternate between discrete strategies during perceptual decision-making
Ashwood, Z. C., Roy, N. A., Stone, I. R., Laboratory, T. I. B., Urai, A. E., Churchland, A. K., Pouget, A., & Pillow, J. W. (2022) · Nature Neuroscience · doi:10.1038/s41593-021-01007-z · Cited by 229 · see citing works
“We found that the most extreme response 71 times were typically associated with the disengaged states, which is consistent with previous findings 72 linking accuracy and response times [32, 53, 61].”
BackgroundResultForaging for foundations in decision neuroscience: insights from ethology
Mobbs, D., Trimmer, P. C., Blumstein, D. T., & Dayan, P. (2018) · Nature reviews. Neuroscience · doi:10.1038/s41583-018-0010-7 · Cited by 262 · see citing works
Wischnewski, M., Alekseichuk, I., & Opitz, A. (2022) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2022.11.013 · Cited by 252 · see citing works
“Standard analyses of outcome measures may benefit from computational modeling analyses such as drift diffusion modeling [67,85,101].”
Cognitive and Neural Bases of Multi-Attribute, Multi-Alternative, Value-based Decisions
Busemeyer, J. R., Gluth, S., Rieskamp, J., & Turner, B. M. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.12.003 · Cited by 266 · see citing works
Mental illness and well‐being: an affect regulation perspective
Gross, J. J., Uusberg, H., & Uusberg, A. (2019) · World Psychiatry · doi:10.1002/wps.20618 · Cited by 248 · see citing works
Global gain modulation generates time-dependent urgency during perceptual choice in humans
Murphy, P. R., Boonstra, E., & Nieuwenhuis, S. (2016) · Nature Communications · doi:10.1038/ncomms13526 · Cited by 196 · see citing works
“While these findings have illuminated the mechanistic basis of SAT regulation in non-human primates, time-invariance remains a dominant assumption in the human decision-making literature 10 and recent empirical and model comparison reports have reinforced this stance 15,29–31 .”
Pothos, E. M., & Busemeyer, J. R. (2022) · Annual Review of Psychology · doi:10.1146/annurev-psych-033020-123501 · Cited by 184 · see citing works
An Overview of Models for Response Times and Processes in Cognitive Tests
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“The model is a modification of the original drift diffusion model (Ratcliff, 1978; Ratcliff and McKoon, 2008; Ratcliff et al., 2016) so that it can be used for multiple-choice data from cognitive tests.”
MethodBridging Neural and Computational Viewpoints on Perceptual Decision-Making
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“One of the main reasons why collapsing bounds have not been incorporated in the dominant models is because key behavioural consequences of doing so, such as decreased accuracy for trials with longer reaction times, can be also produced within a drift diffusion model with constant bounds, via an alternative mechanism i…”
InfluentialUnraveling circuits of visual perception and cognition through the superior colliculus
Basso, M. A., Bickford, M. E., & Cang, J. (2021) · Neuron · doi:10.1016/j.neuron.2021.01.013 · Cited by 229 · see citing works
“In simple, two-choice perceptual decisions, two bounds represent the two alternatives and the relative strength of evidence for the two choices is the accumulated decision variable (Mazurek et al., 2003; Ratcliff et al., 2016).”
BackgroundA reinforcement learning diffusion decision model for value-based decisions
Fontanesi, L., Gluth, S., Spektor, M. S., & Rieskamp, J. (2019) · Psychonomic Bulletin & Review · doi:10.3758/s13423-018-1554-2 · Cited by 195 · see citing works
Neural circuits for evidence accumulation and decision making in larval zebrafish
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Spontaneous Brain Oscillations and Perceptual Decision-Making
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Accounting for attention in sequential sampling models of decision making
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Robustness of statistical methods when measure is affected by ceiling and/or floor effect
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Rationally Inattentive Behavior: Characterizing and Generalizing Shannon Entropy
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What Is the Readiness Potential?
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Use of Response Time for Measuring Cognitive Ability
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“The diffusion model [80] is a popular model for representing cognitive and neural processes underlying simple two-choice decision making (see [81], Box 2 for a comprehensive summary of the domains of its applications).”
BackgroundPredicting lapses of attention with sleep-like slow waves
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“Over the last 40 years, the DDM has become one of the most popular models for explaining RT and accuracy data from a wide range of domains (Forstmann et al., 2016; Ratcliff & McKoon, 2008; Ratcliff et al., 2016).”
MethodMyers, C. E., Interian, A., & Moustafa, A. A. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1039172 · Cited by 150 · see citing works
“The drift diffusion model (DDM), first described by Ratcliff and colleagues (Ratcliff, 1978; Ratcliff and McKoon, 2008; Ratcliff et al., 2016), is one example of a broader class of models called evidence accumulation models.”
BackgroundMethodAdvancing theorizing about fast-and-slow thinking
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Slow Drift of Neural Activity as a Signature of Impulsivity in Macaque Visual and Prefrontal CortexPreprint
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“Perceptual decisions have often been characterized with models that accumulate (noisy) sensory evidence, such as variants of the drift-diffusion model (Krajbich and Rangel, 2011; Brunton et al., 2013; Ratcliff et al., 2016), and there are neural signatures of this accumulation process (Schall, 2003; Gold and Shadlen,…”
BackgroundA watershed model of individual differences in fluid intelligencePreprint
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Amount and time exert independent influences on intertemporal choice
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A flexible framework for simulating and fitting generalized drift-diffusion models
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Decisions are expedited through multiple neural adjustments spanning the sensorimotor hierarchy
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The geometry of decision-making in individuals and collectives
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“While the majority of decision-making studies consider only two options [due to both theoretical and experimental tractability (14, 26, 27)], animals moving in real space frequently encounter a greater number than this.”
A parameter recovery assessment of time-variant models of decision-making
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Freezing revisited: coordinated autonomic and central optimization of threat coping
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Fixation patterns in simple choice reflect optimal information sampling
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A Role for the Superior Colliculus in Decision Criteria
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“In static decision models, such as signal detection theory, the quantity or strength of sensory evidence required for a particular decision is determined by the position of a decision criterion and is equivalent to the starting point of evidence accumulation in dynamic models (Ratcliff et al., 2016).”
Prevalence-induced concept change in human judgment
Levari, D. E., Gilbert, D. T., Wilson, T. D., Sievers, B., Amodio, D. M., & Wheatley, T. P. (2018) · Science · doi:10.1126/science.aap8731 · Cited by 145 · see citing works
Hick’s law for choice reaction time: A review
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Identifying Transdiagnostic Mechanisms in Mental Health Using Computational Factor Modeling
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“…sensitive (or potentially distinct) measure of this can be gleaned from studying how values that we learn to associate with cues spread or “ generalize ” to other similar cues (50,144) and change the way that evidence is accumulated [as modeled by drift diffusion modeling of reaction times (145)].”
Modeling Trait Anxiety: From Computational Processes to Personality
Raymond, J. G., Steele, J. D., & Seriès, P. (2017) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2017.00001 · Cited by 203 · see citing works
“Decisions susceptible to analysis with such models are made rapidly and typically involve just two alternatives (52).”
Evidence Accumulation Models: Current Limitations and Future Directions
Evans, N., & Wagenmakers, E. (2019) · The Quantitative Methods for Psychology · doi:10.31234/osf.io/74df9 · Cited by 101
“decision-making process to date (Ratcliff et al., 2016), which can provide intuitively and neurally plausible explanations for how information processing may operate (Usher & McClelland, 2001).”
On the importance of avoiding shortcuts in applying cognitive models to hierarchical data
Boehm, U., Marsman, M., Matzke, D., & Wagenmakers, E. (2018) · Behavior Research Methods · doi:10.3758/s13428-018-1054-3 · Cited by 101 · see citing works
“For example, the diffusion decision model (DDM; Ratcliff, 1978; Ratcliff et al., 2016) conceptualizes decision-making in terms of seven model parameters that represent different cognitive processes, such as encoding of the response stimulus and response caution.”
Models, movements, and minds: bridging the gap between decision making and action
Wispinski, N. J., Gallivan, J. P., & Chapman, C. S. (2018) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.13973 · Cited by 105 · see citing works
Exploring brain-behavior relationships in the N-back task
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“, drift diffusion, linear ballistic accumulator (Ratcliff et al., 2016).”
Chen, F., & Krajbich, I. (2018) · Nature Communications · doi:10.1038/s41467-018-05994-9 · Cited by 96 · see citing works
Matzke, D., Verbruggen, F., & Logan, G. D. (2018) · Wiley eBooks · doi:10.1002/9781119170174.epcn510 · Cited by 92 · see citing works
Intrahemispheric theta rhythm desynchronization impairs working memory
Alekseichuk, I., Pabel, S. C., Antal, A., & Paulus, W. (2017) · Restorative Neurology and Neuroscience · doi:10.3233/rnn-160714 · Cited by 89 · see citing works
“Further we introduced the computational behavioral model – the diffusion drift model, in order to evaluate the signal-to-noise ratio in the memory system and response conservativeness due to the frontoparietal de/synchronization (Ratcliff, 1978; Ratcliff, Smith, Brown, & McKoon, 2016).”
Neurocomputational mechanisms of prior-informed perceptual decision-making in humans
Kelly, S. P., Corbett, E. A., & O’Connell, R. G. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-00967-9 · Cited by 145 · see citing works
Expectations Do Not Alter Early Sensory Processing during Perceptual Decision-Making
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“According to classic accounts, expectation about a motor response should reduce the amount of evidence needed to trigger a decision (i.e., introduce a response bias; Wald and Wolfowitz, 1949; Green and Swets, 1966; Ratcliff, 1978; Voss et al., 2004; Macmillan and Creelman, 2005; Bogacz et al., 2006; Ratcliff et al., 2…”
BackgroundBeyond simple laboratory studies: Developing sophisticated models to study rich behavior
Maselli, A., Gordon, J. R., Eluchans, M., Lancia, G. L., Thiery, T., Moretti, R., Cisek, P., & Pezzulo, G. (2023) · Physics of Life Reviews · doi:10.1016/j.plrev.2023.07.006 · Cited by 94 · see citing works
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Quantitative susceptibility mapping of brain iron in healthy aging and cognition
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Response Times in Economics: Looking Through the Lens of Sequential Sampling Models
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Shared song detector neurons in Drosophila male and female brains drive sex-specific behaviors
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Time Will Tell: Recovering Preferences When Choices Are Noisy
Alós‐Ferrer, C., Fehr, E., & Netzer, N. (2018) · Journal of Political Economy · doi:10.1086/713732 · Cited by 92 · see citing works
“In addition, our paper contributes to a large literature in psychology, neuroeconomics, and neuroscience on response times and process models (e.g., Ratcliff and Rouder 1998; Krajbich, Armel, and Rangel 2010; Krajbich and Rangel 2011; Ratcliff et al. 2016).”
MethodMetacognitive impairments extend perceptual decision making weaknesses in compulsivity
Hauser, T. U., Allen, M., Bullmore, E. T., Goodyer, I. M., Fonagy, P., Jones, P. B., Fearon, P. R. M., Prabhu, G., Moutoussis, M., Clair, M. S., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-06116-z · Cited by 81 · see citing works
Foraging as an evidence accumulation process
Davidson, J. D., & Hady, A. E. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007060 · Cited by 91 · see citing works
“The two-boundary drift-diffusion model is an optimal strategy to balance the speed-accuracy tradeoff, in that it acheives the fastest decisions for a given level of accuracy (or vice versa) [6, 45].”
Experimental validation of the diffusion model based on a slow response time paradigm
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An attentional drift diffusion model over binary-attribute choice
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“Fixing the barrier separation comes without loss of generality since multiplying the barriers, slope (d), and noise by a fixed constant has no effect on the data that the model generates (Ratcliff & McKoon, 2008; Ratcliff et al., 2016).”
BackgroundOvercoming indecision by changing the decision boundary.
Malhotra, G., Leslie, D. S., Ludwig, C. J. H., & Bogacz, R. (2017) · Journal of Experimental Psychology General · doi:10.1037/xge0000286 · Cited by 67 · see citing works
“Sequential sampling models have had a very successful history of fitting data in a variety of decision-making experiments (Bogacz et al., 2006; Ratcliff, 1978; Ratcliff & Smith, 2004; Ratcliff et al., 2016; Smith & Vickers, 1989).”
BackgroundUnderstanding neural signals of post-decisional performance monitoring: An integrative review
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“Algorithmic models incorporating these features have been highly successful in fitting choice behavior across many domains (Forstmann et al., 2016; Ratcliff et al., 2016; Shadlen and Kiani, 2013).”
BackgroundJun, E. J., Bautista, A. R., Nunez, M. D., Allen, D. C., Tak, J. H., Alvarez, E. T., & Basso, M. A. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00878-6 · Cited by 93 · see citing works
The road towards understanding embodied decisions
Gordon, J. R., Maselli, A., Lancia, G. L., Thiery, T., Cisek, P., & Pezzulo, G. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.09.034 · Cited by 88 · see citing works
Experimental Tests of Rational Inattention
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Action biases perceptual decisions toward expected outcomes.
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“Drift diffusion models (DDMs) of perceptual decision making have enjoyed growing prominence in the cognitive sciences (Ratcliff, Smith, Brown, & McKoon, 2016; see Figure 3).”
O’Callaghan, C., Hall, J. M., Tomassini, A., Muller, A. J., Walpola, I. C., Moustafa, A. A., Shine, J. M., & Lewis, S. J. G. (2017) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2017.04.007 · Cited by 77 · see citing works
Cerebellar Roles in Self-Timing for Sub- and Supra-Second Intervals
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“These properties of neuronal activity were in line with the model of decision making that assumed a climbing of neuronal activity to a certain threshold (Wittmann, 2013; Ratcliff et al., 2016).”
ResultDissociable mechanisms govern when and how strongly reward attributes affect decisions
Maier, S. U., Beharelle, A. R., Polanía, R. H., Ruff, C. C., & Hare, T. A. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0893-y · Cited by 93 · see citing works
Advances in modeling learning and decision-making in neuroscience
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“One of the most popular classes of models is the drift diffusion model (DDM [115, 119]), which assumes that decisions are made by continuously accumulating the relative evidence for one of two responses (i.”
BackgroundMethodHogarth, L., & Field, M. (2020) · Behavioural Brain Research · doi:10.1016/j.bbr.2020.112815 · Cited by 82 · see citing works
“their stated preferences during the initial rating task) are then fitted using drift diffusion models [116,117].”
Subcritical escape waves in schooling fish
Poel, W., Daniels, B. C., Sosna, M. M. G., Twomey, C. R., Leblanc, S. P., Couzin, I., & Romanczuk, P. (2021) · Science Advances · doi:10.1126/sciadv.abm6385 · Cited by 72
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Recovery from addiction: Behavioral economics and value-based decision making.
Field, M., Heather, N., Murphy, J. G., Stafford, T., Tucker, J., & Witkiewitz, K. (2019) · Psychology of Addictive Behaviors · doi:10.31234/osf.io/rfjwu · Cited by 69 · see citing works
Response Times and Decision‐Making
Donkin, C., & Brown, S. D. (2018) · Wiley eBooks · doi:10.1002/9781119170174.epcn509 · Cited by 70 · see citing works
Self-citationLerche, V., von Krause, M., Voß, A., Frischkorn, G. T., Schubert, A., & Hagemann, D. (2020) · Journal of Experimental Psychology General · doi:10.1037/xge0000774 · Cited by 81 · see citing works
Feeling a Touch to the Hand on the Foot.
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Learning and Choice in Mood Disorders: Searching for the Computational Parameters of Anhedonia
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“There are a number of different frameworks, but most share the fundamental concept that information is accumulated (they are sometimes referred to as accumulator models) until a threshold is reached and a decision is made (Ratcliff, Smith, Brown, & McKoon, 2016; Tsetsos et al., 2012).”
Value-Based Choice, Contingency Learning, and Suicidal Behavior in Mid- and Late-Life Depression
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“How might these deficits facilitate suicidal behavior? We view the choice between suicide and its alternatives as a temporally unfolding decision process (30,31).”
BackgroundGaze bias differences capture individual choice behaviour
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Cortical beta power reflects decision dynamics and uncovers multiple facets of post-error adaptation
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Shared Song Detector Neurons in Drosophila Male and Female Brains Drive Sex-Specific BehaviorsPreprint
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Confidence in masked orientation judgments is informed by both evidence and visibility
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Variable-Drift Diffusion Models of Pedestrian Road-Crossing Decisions
Pekkanen, J., Giles, O. T., Lee, Y. M., Madigan, R., Daimon, T., Merat, N., & Markkula, G. M. (2021) · Computational Brain & Behavior · doi:10.1007/s42113-021-00116-z · Cited by 61 · see citing works
“…humans and non-human primates, across a range of laboratory paradigms on especially perceptual decision-making (e.g., discrimination of random dot motion direction) and value-based choice (e.g., between different food items) (Brosnan et al., 2020; Busemeyer et al., 2019; Ratcliff et al., 2016).”
BackgroundStrategy-dependent effects of working-memory limitations on human perceptual decision-making
Schapiro, K., Josić, K., Kilpatrick, Z., & Gold, J. I. (2021) · bioRxiv · doi:10.7554/elife.73610 · Cited by 64 · see citing works
“Many perceptual, memory-based, and reward-based decisions depend on an accumulation of evidence over time ( Brody and Hanks, 2016 ; Gold and Shadlen, 2007 ; Ratcliff et al., 2016 ; Shadlen and Shohamy, 2016 ; Summerfield and Tsetsos, 2012 ).”
- Authors
- Ratcliff, R., Smith, P. L., Brown, S. D., & McKoon, G.
- Affiliations
- The Ohio State University (US)
- The University of Melbourne (AU)
- University of Newcastle Australia (AU)
- Corresponding author
- Ratcliff, Roger
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2016-04
- Volume
- 20
- Issue
- 4
- Pages
- 260-281
- ISSN
- 1364-6613 (ISSN-L)
- PMC
- PMC4928591
- OpenAlex
- W2290798512
- Cited by
- 1,630
- References
- 216
- Citations by year
- 2026: 186 · 2025: 209 · 2024: 181 · 2023: 206 · 2022: 188 · 2021: 197 · 2020: 142 · 2019: 137 · 2018: 98 · 2017: 70 · 2016: 13 · 2015: 1
- FWCI
- 11.19
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Trends in Cognitive Sciences (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- NOVA (University of Newcastle Australia) (submittedVersion)
- Funders
- National Institute on Aging (R01-AG041176)
- Department of Education/Institute of Educational Sciences (R305A120189)
- Australian Research Council (DP140102970, FT120100244)
- 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, sequential sampling models, speeded decision making, evidence accumulation, neuronal populations, Optimality Theory, individual differences, psychometrics
- MeSH terms
- Cognition, Decision Making, Electroencephalography, Humans, Magnetic Resonance Imaging, Models, Psychological, Brain Waves
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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