S. Allain, L. Carbonnell, B. Burle, T. Hasbroucq, and F. Vidal, On-line executive control: An electromyographic study, Psychophysiology, vol.41, issue.1, pp.113-116, 2004.
URL : https://hal.archives-ouvertes.fr/hal-01384902

R. Anders, F. X. Alario, and L. Van-maanen, The shifted Wald distribution for response time data analysis., Psychological Methods, vol.21, issue.3, pp.309-327, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01432292

R. Anders, Z. Oravecz, and F. Alario, Improved information pooling for hierarchical cognitive models through multiple and covaried regression, Behavior Research Methods, vol.50, issue.3, pp.989-1010, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02012383

R. Anders, S. Riès, L. Van-maanen, and F. X. Alario, Lesions to the left lateral prefrontal cortex impair decision threshold adjustment for lexical selection, Cognitive Neuropsychology, vol.34, issue.1-2, pp.1-20, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01619526

R. Anders, L. Van-maanen, and F. Alario, Multi-factor analysis in language production: Sequential sampling models mimic and extend regression results, Cognitive Neuropsychology, vol.36, issue.5-6, pp.234-264, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02380640

T. Ando, Bayesian predictive information criterion for the evaluation of hierarchical Bayesian and empirical Bayes models, Biometrika, vol.94, issue.2, pp.443-458, 2007.

D. J. Barr, Random effects structure for testing interactions in linear mixed-effects models, Frontiers in Psychology, vol.4, pp.3-4, 2013.

N. R. Bartlett, A comparison of manual reaction times as measured by three sensitive indices, The Psychological Record, vol.13, issue.1, pp.51-56, 1963.

U. Boehm, J. Annis, M. J. Frank, G. E. Hawkins, A. Heathcote et al., Estimating across-trial variability parameters of the Diffusion Decision Model: Expert advice and recommendations, Journal of Mathematical Psychology, vol.87, pp.46-75, 2018.

J. Botwinick and L. W. Thompson, Premotor and motor components of reaction time., Journal of Experimental Psychology, vol.71, issue.1, pp.9-15, 1966.

S. D. Brown and A. Heathcote, The simplest complete model of choice response time: Linear ballistic accumulation, Cognitive Psychology, vol.57, issue.3, pp.153-178, 2008.

B. W. Brunton, M. M. Botvinick, and C. D. Brody, Rats and Humans Can Optimally Accumulate Evidence for Decision-Making, Science, vol.340, issue.6128, pp.95-98, 2013.

C. Buc-calderon, T. Verguts, and W. Gevers, Losing the boundary: Cognition biases action well after action selection., Journal of Experimental Psychology: General, vol.144, issue.4, pp.737-743, 2015.

B. Burle, C. Possamaï, F. Vidal, M. Bonnet, and T. Hasbroucq, Executive control in the Simon effect: an electromyographic and distributional analysis, Psychological Research, vol.66, issue.4, pp.324-336, 2002.
URL : https://hal.archives-ouvertes.fr/hal-01384935

B. Burle, C. Roger, F. Vidal, and T. Hasbroucq, Spatiotemporal dynamics of information processing in the brain: Recent advances, current limitations and future challenges, International Journal of Bioelectromagnetism, vol.10, pp.17-21, 2008.
URL : https://hal.archives-ouvertes.fr/hal-02871994

E. Callaway, R. Halliday, H. Naylor, and D. Thouvenin, The latency of the average is not the average of the latencies, Psychophysiology, vol.21, p.571, 1984.

M. G. Coles, Modern Mind-Brain Reading: Psychophysiology, Physiology, and Cognition, Psychophysiology, vol.26, issue.3, pp.251-269, 1989.

D. Cousineau, Confidence intervals in within-subject designs: A simpler solution to Loftus and Masson's method, Tutorials in Quantitative Methods for Psychology, vol.1, issue.1, pp.42-45, 2005.

V. De-lafuente and R. Romo, Neural correlate of subjective sensory experience gradually builds up across cortical areas, Proceedings of the National Academy of Sciences, vol.103, issue.39, pp.14266-14271, 2006.

C. Donkin, S. Brown, A. Heathcote, and E. J. Wagenmakers, Diffusion versus linear ballistic accumulation: different models but the same conclusions about psychological processes?, Psychonomic Bulletin & Review, vol.18, issue.1, pp.61-69, 2010.

T. H. Donner, M. Siegel, P. Fries, and A. K. Engel, Buildup of Choice-Predictive Activity in Human Motor Cortex during Perceptual Decision Making, Current Biology, vol.19, issue.18, pp.1581-1585, 2009.

D. Dotan, F. Meyniel, and S. Dehaene, Corrigendum to ?On-line confidence monitoring during decision making? [Cognition 171 (2018) 112?121], Cognition, vol.176, p.269, 2018.

A. Dubarry, A. Llorens, A. Trébuchon, R. Carron, C. Liégeois-chauvel et al., Estimating Parallel Processing in a Language Task Using Single-Trial Intracerebral Electroencephalography, Psychological Science, vol.28, issue.4, pp.414-426, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01495041

G. Dutilh and C. .. Donkin, The Quality of Response Time Data Inference: A Blinded, Collaborative Assessment of the Validity of Cognitive Models, Psychonomic Bulletin & Review, vol.26, issue.4, pp.1051-1069, 2016.

G. Dutilh, E. Wagenmakers, I. Visser, and H. L. Van-der-maas, A Phase Transition Model for the Speed-Accuracy Trade-Off in Response Time Experiments, Cognitive Science, vol.35, issue.2, pp.211-250, 2010.

F. Fluchère, B. Burle, F. Vidal, W. Van-den-wildenberg, T. Witjas et al., Subthalamic nucleus stimulation, dopaminergic treatment and impulsivity in Parkinson's disease, Neuropsychologia, vol.117, pp.167-177, 2018.

A. Gelman and D. B. Rubin, Inference from Iterative Simulation Using Multiple Sequences, Statistical Science, vol.7, issue.4, pp.457-472, 1992.

P. Gomez, M. Perea, and R. Ratcliff, A diffusion model account of masked versus unmasked priming: Are they qualitatively different?, Journal of Experimental Psychology: Human Perception and Performance, vol.39, issue.6, pp.1731-1740, 2013.

P. Gomez, R. Ratcliff, and R. Childers, Pointing, looking at, and pressing keys: A diffusion model account of response modality., Journal of Experimental Psychology: Human Perception and Performance, vol.41, issue.6, pp.1515-1523, 2015.

A. Gramfort, M. Luessi, E. Larson, D. A. Engemann, D. Strohmeier et al., MEG and EEG data analysis with MNE-Python, Frontiers in Neuroscience, vol.7, 2013.

D. Grayson, The Role of the Response Stage in Stochastic Models of Simple Reaction Time (Unpublished doctoral dissertation), 1983.

G. R. Grice and V. A. Spiker, Speed-accuracy tradeoff in choice reaction time: Within conditions, between conditions, and between subjects, Perception & Psychophysics, vol.26, issue.2, pp.118-126, 1979.

T. Hasbroucq, C. Possamaï, M. Bonnet, and F. Vidal, Effect of the irrelevant location of the response signal on choice reaction time: An electromyographic study in humans, Psychophysiology, vol.36, issue.4, pp.522-526, 1999.

A. Heathcote, S. D. Brown, and E. Wagenmakers, An Introduction to Good Practices in Cognitive Modeling, An Introduction to Model-Based Cognitive Neuroscience, pp.25-48, 2015.

A. Heathcote, Y. Lin, A. Reynolds, L. Strickland, M. Gretton et al., Dynamic models of choice, Behavior Research Methods, vol.51, issue.2, pp.961-985, 2018.

A. Heathcote and J. Love, Linear Deterministic Accumulator Models of Simple Choice, Frontiers in Psychology, vol.3, pp.1-19, 2012.

R. P. Heitz, The speed-accuracy tradeoff: history, physiology, methodology, and behavior, Frontiers in Neuroscience, vol.8, pp.1-19, 2014.

D. M. Herz, R. Bogacz, and P. Brown, Neuroscience: Impaired Decision-Making in Parkinson?s Disease, Current Biology, vol.26, issue.14, pp.R671-R673, 2016.

R. Kumar, C. Carroll, A. Hartikainen, and O. A. Martin, ArviZ a unified library for exploratory analysis of Bayesian models in Python, Journal of Open Source Software, vol.4, issue.33, p.1143, 2019.

R. Kumar, C. Carroll, A. Hartikainen, and O. Martin, ArviZ a unified library for exploratory analysis of Bayesian models in Python, Journal of Open Source Software, vol.4, issue.33, p.1143, 2019.

K. W. Latimer, J. L. Yates, M. L. Meister, A. C. Huk, and J. W. Pillow, Single-trial spike trains in parietal cortex reveal discrete steps during decision-making, Science, vol.349, issue.6244, pp.184-187, 2015.

V. M. Lawlor, C. A. Webb, T. V. Wiecki, M. J. Frank, M. Trivedi et al., T139. Dissecting the Impact of Depression on Decision-Making During a Probabilistic Reward Task, Biological Psychiatry, vol.85, issue.10, p.S183, 2019.

V. Lerche and A. Voss, Speed?accuracy manipulations and diffusion modeling: Lack of discriminant validity of the manipulation or of the parameter estimates?, Behavior Research Methods, vol.50, issue.6, pp.2568-2585, 2018.

V. Lerche, A. Voss, and M. Nagler, How many trials are required for parameter estimation in diffusion modeling? A comparison of different optimization criteria, Behavior Research Methods, vol.49, issue.2, pp.513-537, 2016.

D. Lewandowski, D. Kurowicka, and H. Joe, Generating random correlation matrices based on vines and extended onion method, Journal of Multivariate Analysis, vol.100, issue.9, pp.1989-2001, 2009.

J. Liu and Q. Liu, Use of the integrated profile for voluntary muscle activity detection using EMG signals with spurious background spikes: A study with incomplete spinal cord injury, Biomedical Signal Processing and Control, vol.24, pp.19-24, 2016.

R. Luce, Response Times, vol.562, 1991.

A. Ly, U. Boehm, A. Heathcote, B. M. Turner, B. Forstmann et al., A Flexible and Efficient Hierarchical Bayesian Approach to the Exploration of Individual Differences in Cognitive-model-based Neuroscience, Computational Models of Brain and Behavior, pp.467-479, 2017.

D. Matzke and E. Wagenmakers, Psychological interpretation of the ex-Gaussian and shifted Wald parameters: A diffusion model analysis, Psychonomic Bulletin & Review, vol.16, issue.5, pp.798-817, 2009.

J. L. Mcclelland, On the time relations of mental processes: An examination of systems of processes in cascade., Psychological Review, vol.86, issue.4, pp.287-330, 1979.

J. Miller, Discrete and continuous models of human information processing: Theoretical distinctions and empirical results, Acta Psychologica, vol.67, issue.3, pp.191-257, 1988.

J. Miller, R. Ulrich, and G. Rinkenauer, Effects of stimulus intensity on the lateralized readiness potential., Journal of Experimental Psychology: Human Perception and Performance, vol.25, issue.5, pp.1454-1471, 1999.

A. A. Moustafa, S. Kéri, Z. Somlai, T. Balsdon, D. Frydecka et al., Drift diffusion model of reward and punishment learning in schizophrenia: Modeling and experimental data, Behavioural Brain Research, vol.291, pp.147-154, 2015.

B. Nicenboim, S. Vasishth, F. Engelmann, and K. Suckow, Exploratory and Confirmatory Analyses in Sentence Processing: A Case Study of Number Interference in German, Cognitive Science, vol.42, pp.1075-1100, 2018.

M. D. Nunez, A. Gosai, J. Vandekerckhove, and R. Srinivasan, The latency of a visual evoked potential tracks the onset of decision making, NeuroImage, vol.197, pp.93-108, 2019.

R. G. O'connell, P. M. Dockree, and S. P. Kelly, A supramodal accumulation-to-bound signal that determines perceptual decisions in humans, Nature Neuroscience, vol.15, issue.12, pp.1729-1735, 2012.

T. E. Oliphant, Python for Scientific Computing, Computing in Science & Engineering, vol.9, issue.3, pp.10-20, 2007.
URL : https://hal.archives-ouvertes.fr/hal-02520043

R. Ollman, Fast guesses in choice reaction time, Psychonomic Science, vol.6, issue.4, pp.155-156, 1966.

A. Osman, L. Lou, H. Muller-gethmann, G. Rinkenauer, S. Mattes et al., Mechanisms of speed-accuracy tradeoff: Evidence from covert motor processes, Biological Psychology, vol.51, issue.2-3, pp.45-54, 2000.

J. Palmer, A. C. Huk, and M. N. Shadlen, The effect of stimulus strength on the speed and accuracy of a perceptual decision, Journal of Vision, vol.5, issue.5, p.1, 2005.

M. L. Pe, J. Vandekerckhove, and P. Kuppens, A diffusion model account of the relationship between the emotional flanker task and rumination and depression., Emotion, vol.13, issue.4, pp.739-747, 2013.

J. W. Peirce, PsychoPy?Psychophysics software in Python, Journal of Neuroscience Methods, vol.162, issue.1-2, pp.8-13, 2007.

M. G. Philiastides, R. Ratcliff, and P. Sajda, Neural Representation of Task Difficulty and Decision Making during Perceptual Categorization: A Timing Diagram, Journal of Neuroscience, vol.26, issue.35, pp.8965-8975, 2006.

J. P. Pieters, Sternberg's additive factor method and underlying psychological processes: Some theoretical considerations., Psychological Bulletin, vol.93, issue.3, pp.411-426, 1983.

C. Possama??, B. Burle, A. Osman, and T. Hasbroucq, Partial advance information, number of alternatives, and motor processes: an electromyographic study, Acta Psychologica, vol.111, issue.1, pp.125-139, 2002.

B. A. Purcell, R. P. Heitz, J. Y. Cohen, J. D. Schall, G. D. Logan et al., Neurally constrained modeling of perceptual decision making., Psychological Review, vol.117, issue.4, pp.1113-1143, 2010.

B. Rae, A. Heathcote, C. Donkin, L. Averell, and S. Brown, The hare and the tortoise: Emphasizing speed can change the evidence used to make decisions., Journal of Experimental Psychology: Learning, Memory, and Cognition, vol.40, issue.5, pp.1226-1243, 2014.

R. Ratcliff, A theory of memory retrieval., Psychological Review, vol.85, issue.2, pp.59-108, 1978.

R. Ratcliff, Modeling response signal and response time data?, Cognitive Psychology, vol.53, issue.3, pp.195-237, 2006.

R. Ratcliff, A. Cherian, and M. Segraves, A Comparison of Macaque Behavior and Superior Colliculus Neuronal Activity to Predictions From Models of Two-Choice Decisions, Journal of Neurophysiology, vol.90, issue.3, pp.1392-1407, 2003.

R. Ratcliff and R. Childers, Individual differences and fitting methods for the two-choice diffusion model of decision making., Decision, vol.2, issue.4, pp.237-279, 2015.

R. Ratcliff and G. Mckoon, The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks, Neural Computation, vol.20, issue.4, pp.873-922, 2008.

R. Ratcliff and J. N. Rouder, Modeling Response Times for Two-Choice Decisions, Psychological Science, vol.9, issue.5, pp.347-356, 1998.

R. Ratcliff, P. L. Smith, S. D. Brown, and G. Mckoon, Diffusion Decision Model: Current Issues and History, Trends in Cognitive Sciences, vol.20, issue.4, pp.260-281, 2016.

R. Ratcliff, A. Thapar, and G. Mckoon, The effects of aging on reaction time in a signal detection task., Psychology and Aging, vol.16, issue.2, pp.323-341, 2001.

R. Ratcliff and F. Tuerlinckx, Estimating parameters of the diffusion model: Approaches to dealing with contaminant reaction times and parameter variability, Psychonomic Bulletin & Review, vol.9, issue.3, pp.438-481, 2002.

J. Requin, A. Riehle, and J. Seal, Neuronal activity and information processing in motor control: From stages to continuous flow, Biological Psychology, vol.26, issue.1-3, pp.179-198, 1988.

A. Resulaj, R. Kiani, D. M. Wolpert, and M. N. Shadlen, Changes of mind in decision-making, Nature, vol.461, issue.7261, pp.263-266, 2009.

G. Rinkenauer, A. Osman, R. Ulrich, H. Müller-gethmann, and S. Mattes, On the Locus of Speed-Accuracy Trade-Off in Reaction Time: Inferences From the Lateralized Readiness Potential., Journal of Experimental Psychology: General, vol.133, issue.2, pp.261-282, 2004.

S. Roberts and H. Pashler, How persuasive is a good fit? A comment on theory testing., Psychological Review, vol.107, issue.2, pp.358-367, 2000.

N. Rochet, L. Spieser, L. Casini, T. Hasbroucq, and B. Burle, Detecting and correcting partial errors: Evidence for efficient control without conscious access, Cognitive, Affective, & Behavioral Neuroscience, vol.14, issue.3, pp.970-982, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01292369

C. Roger, E. Núñez-castellar, G. Pourtois, and W. Fias, Changing your mind before it is too late: The electrophysiological correlates of online error correction during response selection, Psychophysiology, vol.51, issue.8, pp.746-760, 2014.

J. D. Roitman and M. N. Shadlen, Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task, The Journal of Neuroscience, vol.22, issue.21, pp.9475-9489, 2002.

A. Sanders, Towards a model of stress and human performance, Acta Psychologica, vol.53, issue.1, pp.90016-90017, 1983.

M. Santello and M. J. Mcdonagh, The control of timing and amplitude of EMG activity in landing movements in humans, Experimental Physiology, vol.83, issue.6, pp.857-874, 1998.

D. J. Schad, M. Betancourt, and S. Vasishth, Toward a principled Bayesian workflow in cognitive science., Psychological Methods, 2020.

J. D. Schall, On Building a Bridge Between Brain and Behavior, Annual Review of Psychology, vol.55, issue.1, pp.23-50, 2004.

J. D. Schall, Accumulators, Neurons, and Response Time, Trends in Neurosciences, vol.42, issue.12, pp.848-860, 2019.

A. Schmied, J. P. Vedel, and S. Pagni, Human spinal lateralization assessed from motoneurone synchronization: dependence on handedness and motor unit type., The Journal of Physiology, vol.480, issue.2, pp.369-387, 1994.

S. Seabold and J. Perktold, Statsmodels: Econometric and Statistical Modeling with Python, Proceedings of the 9th Python in Science Conference, vol.57, p.61, 2010.

M. Servant, C. White, A. Montagnini, and B. Burle, Using Covert Response Activation to Test Latent Assumptions of Formal Decision-Making Models in Humans, Journal of Neuroscience, vol.35, issue.28, pp.10371-10385, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01310094

M. Servant, C. White, A. Montagnini, and B. Burle, Linking Theoretical Decision-making Mechanisms in the Simon Task with Electrophysiological Data: A Model-based Neuroscience Study in Humans, Journal of Cognitive Neuroscience, vol.28, issue.10, pp.1501-1521, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01401203

K. ?migasiewicz, S. Ambrosi, A. Blaye, and B. Burle, Inhibiting errors while they are produced: Direct evidence for error monitoring and inhibitory control in children, Developmental Cognitive Neuroscience, vol.41, p.100742, 2020.

P. L. Smith, Psychophysically principled models of visual simple reaction time., Psychological Review, vol.102, issue.3, pp.567-593, 1995.

P. L. Smith and S. D. Lilburn, Vision for the blind: visual psychophysics and blinded inference for decision models, Psychonomic Bulletin & Review, vol.27, issue.5, pp.882-910, 2020.

L. Spieser, M. Servant, T. Hasbroucq, and B. Burle, Beyond decision! Motor contribution to speed?accuracy trade-off in decision-making, Psychonomic Bulletin & Review, vol.24, issue.3, pp.950-956, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01401200

, STAN R&D: Research and Development Expenditure in Industry - Rev. 2, 2009.

J. J. Starns and R. Ratcliff, Validating the unequal-variance assumption in recognition memory using response time distributions instead of ROC functions: A diffusion model analysis, Journal of Memory and Language, vol.70, pp.36-52, 2014.

J. J. Starns, R. Ratcliff, and G. Mckoon, Evaluating the unequal-variance and dual-process explanations of zROC slopes with response time data and the diffusion model, Cognitive Psychology, vol.64, issue.1-2, pp.1-34, 2012.

N. A. Steinemann, R. G. O?connell, and S. P. Kelly, Decisions are expedited through multiple neural adjustments spanning the sensorimotor hierarchy, Nature Communications, vol.9, issue.1, p.3627, 2017.

S. Sternberg, The discovery of processing stages: Extensions of Donders' method, Acta Psychologica, vol.30, pp.276-315, 1969.

M. Stone, Models for choice-reaction time, Psychometrika, vol.25, issue.3, pp.251-260, 1960.

C. Tandonnet, B. Burle, F. Vidal, and T. Hasbroucq, The influence of time preparation on motor processes assessed by surface Laplacian estimation, Clinical Neurophysiology, vol.114, issue.12, pp.2376-2384, 2003.
URL : https://hal.archives-ouvertes.fr/hal-01384926

C. Tandonnet, B. Burle, F. Vidal, and T. Hasbroucq, Knowing when to respond and the efficiency of the cortical motor command: A Laplacian ERP study, Brain Research, vol.1109, issue.1, pp.158-163, 2006.
URL : https://hal.archives-ouvertes.fr/hal-01384879

D. Y. Teller, Linking propositions, Vision Research, vol.24, issue.10, pp.1233-1246, 1984.

G. Tillman, T. Van-zandt, and G. D. Logan, Sequential sampling models without random between-trial variability: the racing diffusion model of speeded decision making, Psychonomic Bulletin & Review, vol.27, issue.5, pp.911-936, 2020.

B. M. Turner, L. Van-maanen, B. U. Forstmann, R. Ulrich, and K. H. Stapf, A double-response paradigm to study stimulus intensity effects upon the motor system in simple reaction time experiments, Perception & Psychophysics, vol.122, issue.2, pp.545-558, 1984.

M. Usher and J. L. Mcclelland, The time course of perceptual choice: The leaky, competing accumulator model., Psychological Review, vol.108, issue.3, pp.550-592, 2001.

J. Vandekerckhove and F. Tuerlinckx, Fitting the ratcliff diffusion model to experimental data, Psychonomic Bulletin & Review, vol.14, issue.6, pp.1011-1026, 2007.

D. Van-ravenzwaaij, A. Provost, and S. D. Brown, A confirmatory approach for integrating neural and behavioral data into a single model, Journal of Mathematical Psychology, vol.76, pp.131-141, 2017.

A. Voss, K. Rothermund, A. Gast, and D. Wentura, Cognitive processes in associative and categorical priming: A diffusion model analysis., Journal of Experimental Psychology: General, vol.142, issue.2, pp.536-559, 2013.

A. Voss, K. Rothermund, and J. Voss, Interpreting the parameters of the diffusion model: An empirical validation, Memory & Cognition, vol.32, issue.7, pp.1206-1220, 2004.

C. N. White, R. Ratcliff, M. W. Vasey, and G. Mckoon, Anxiety enhances threat processing without competition among multiple inputs: A diffusion model analysis., Emotion, vol.10, issue.5, pp.662-677, 2010.

T. V. Wiecki, I. Sofer, and M. J. Frank, HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python, Frontiers in Neuroinformatics, vol.7, p.14, 2013.

T. V. Wiecki, I. Sofer, and M. J. Frank, HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python, Frontiers in Neuroinformatics, vol.7, 2013.