@article{srivastava2016explicit,
  title={Explicit moments of decision times for single-and double-threshold drift-diffusion processes},
  author={Srivastava, Vaibhav and Holmes, Philip and Simen, Patrick},
  journal={Journal of Mathematical Psychology},
  volume={75},
  pages={96--109},
  year={2016},
  publisher={Elsevier}
}
@article{roxin2019drift,
  title={Drift--diffusion models for multiple-alternative forced-choice decision making},
  author={Roxin, Alex},
  journal={The Journal of Mathematical Neuroscience},
  volume={9},
  number={1},
  pages={5},
  year={2019},
  publisher={Springer}
}
@article{frank2015fmri,
  title={{fMRI} and {EEG} predictors of dynamic decision parameters during human reinforcement learning},
  author={Frank, Michael J. and Gagne, Chris and Nyhus, Erika and Masters, Sean and Wiecki, Thomas V. and Cavanagh, James F. and Badre, David},
  journal={Journal of Neuroscience},
  volume={35},
  number={2},
  pages={485--494},
  year={2015},
}
@article{matzke2009psychological,
  title={Psychological interpretation of the ex-{G}aussian and shifted {W}ald parameters: A diffusion model analysis},
  author={Matzke, Dora and Wagenmakers, Eric-Jan},
  journal={Psychonomic Bulletin \& Review},
  volume={16},
  number={5},
  pages={798--817},
  year={2009},
}
@article{rangel2008framework,
  title={A framework for studying the neurobiology of value-based decision making},
  author={Rangel, Antonio and Camerer, Colin and Montague, P. Read},
  journal={Nature Reviews Neuroscience},
  volume={9},
  number={7},
  pages={545--556},
  year={2008},
}
@article{brignone2021moment,
  title={Moment-matching approximations for stochastic sums in non-{G}aussian {O}rnstein-{U}hlenbeck models},
  author={Brignone, Riccardo and Kyriakou, Ioannis and Fusai, Gianluca},
  journal={Insurance: Mathematics and Economics},
  volume={96},
  pages={232--247},
  year={2021},
}
@article{Hensman2015bb,
author = {Hensman, James and Matthews, Alexander G. and Ghahramani, Zoubin},
journal = {Journal of Machine Learning Research},
pages = {351--360},
title = {{Scalable variational Gaussian process classification}},
volume = {38},
year = {2015}
}
@article{lo2014momenta,
  title={Moment-matching approximations for {A}sian options},
  author={Lo, Chien-Ling and Palmer, Kenneth J and Yu, Min-Teh},
  journal={The Journal of Derivatives},
  volume={21},
  number={4},
  pages={103--122},
  year={2014},
}
@article{murata2014stochastic,
  title={Stochastic process underlying emergent recognition of visual objects hidden in degraded images},
  author={Murata, Tsutomu and Hamada, Takashi and Shimokawa, Tetsuya and Tanifuji, Manabu and Yanagida, Toshio},
  journal={PLoS One},
  volume={9},
  number={12},
  pages={e115658},
  year={2014},
  publisher={Public Library of Science San Francisco, USA}
}
@article{brand2002efficient,
  title={Efficient adaptive procedures for threshold and concurrent slope estimates for psychophysics and speech intelligibility tests},
  author={Brand, Thomas and Kollmeier, Birger},
  journal={The Journal of the Acoustical Society of America},
  volume={111},
  number={6},
  pages={2801--2810},
  year={2002},
  publisher={Acoustical Society of America}
}
@article{ratcliff2008diffusion,
  title={The diffusion decision model: theory and data for two-choice decision tasks},
  author={Ratcliff, Roger and McKoon, Gail},
  journal={Neural Computation},
  volume={20},
  number={4},
  pages={873--922},
  year={2008},
}


@MISC {mathof_normallimit,
    TITLE = {Normal approximation to the pointwise/Hadamard/Schur product of two multivariate Gaussian/normal random variables},
    AUTHOR = {Iosif Pinelis (https://mathoverflow.net/users/36721/iosif-pinelis)},
    HOWPUBLISHED = {MathOverflow},
    NOTE = {URL:https://mathoverflow.net/q/293985 (version: 2018-02-27)},
    EPRINT = {https://mathoverflow.net/q/293985},
    URL = {https://mathoverflow.net/q/293985}
}

@article{gardner2018gpytorch,
  title={Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu acceleration},
  author={Gardner, Jacob R. and Pleiss, Geoff and Bindel, David and Weinberger, Kilian Q and Wilson, Andrew Gordon},
  journal={arXiv preprint arXiv:1809.11165},
  year={2018}
}
@inproceedings{balandat2020botorch,
  title = {{BoTorch}: a Framework for Efficient {Monte-Carlo Bayesian} Optimization},
  author = {Balandat, Maximilian and Karrer, Brian and Jiang, Daniel R. and Daulton, Samuel and Letham, Benjamin and Wilson, Andrew Gordon and Bakshy, Eytan},
  booktitle = {Advances in Neural Information Processing Systems 33},
  year = 2020,
  series={NeurIPS},
  pages = {21524--21538},
}
@article{schutt2016painfree,
  title={Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data},
  author={Sch{\"u}tt, Heiko H and Harmeling, Stefan and Macke, Jakob H and Wichmann, Felix A},
  journal={Vision research},
  volume={122},
  pages={105--123},
  year={2016},
  publisher={Elsevier}
}
@article{neelon2004bayesian,
  title={Bayesian isotonic regression and trend analysis},
  author={Neelon, Brian and Dunson, David B},
  journal={Biometrics},
  volume={60},
  number={2},
  pages={398--406},
  year={2004},
  publisher={Wiley Online Library}
}
@article{lin2014bayesian,
  title={Bayesian monotone regression using Gaussian process projection},
  author={Lin, Lizhen and Dunson, David B},
  journal={Biometrika},
  volume={101},
  number={2},
  pages={303--317},
  year={2014},
  publisher={Oxford University Press}
}
@article{watson2017quest+,
  title={QUEST+: A general multidimensional Bayesian adaptive psychometric method},
  author={Watson, Andrew B},
  journal={Journal of Vision},
  volume={17},
  number={3},
  pages={10--10},
  year={2017},
  publisher={The Association for Research in Vision and Ophthalmology}
}
@inproceedings{letham2022look,
  title={Look-Ahead Acquisition Functions for {B}ernoulli Level Set Estimation},
  author={Letham, Benjamin and Guan, Phillip and Tymms, Chase and Bakshy, Eytan and Shvartsman, Michael},
  booktitle={Proceedings of the 25th International Conference on Artificial Intelligence and Statistics},
  series={AISTATS},
  year={2022},
  pages={8493--8513},
}
@article{brier1950verification,
  title={Verification of forecasts expressed in terms of probability},
  author={Brier, Glenn W.},
  journal={Monthly Weather Review},
  volume={78},
  number={1},
  pages={1--3},
  year={1950}
}
@article{may2013four,
  title={Four theorems on the psychometric function},
  author={May, Keith A and Solomon, Joshua A},
  journal={PloS one},
  volume={8},
  number={10},
  pages={e74815},
  year={2013},
  publisher={Public Library of Science San Francisco, USA}
}
@article{strasburger2001converting,
  title={Converting between measures of slope of the psychometric function},
  author={Strasburger, Hans},
  journal={Perception \& psychophysics},
  volume={63},
  number={8},
  pages={1348--1355},
  year={2001},
  publisher={Springer}
}
@book{fechner1966elements,
  title={Elements of psychophysics},
  author={Fechner, Gustav Theodor and Howes, Davis H and Boring, Edwin Garrigues},
  volume={1},
  year={1966},
  publisher={Holt, Rinehart and Winston New York}
}
@article{shen2012maximum,
  title={A maximum-likelihood procedure for estimating psychometric functions: Thresholds, slopes, and lapses of attention},
  author={Shen, Yi and Richards, Virginia M},
  journal={The Journal of the Acoustical Society of America},
  volume={132},
  number={2},
  pages={957--967},
  year={2012},
  publisher={Acoustical Society of America}
}
@article{pisupati2021lapses,
  title={Lapses in perceptual decisions reflect exploration},
  author={Pisupati, Sashank and Chartarifsky-Lynn, Lital and Khanal, Anup and Churchland, Anne K},
  journal={Elife},
  volume={10},
  pages={e55490},
  year={2021},
  publisher={eLife Sciences Publications Limited}
}
@inproceedings{gardner2015bayesian,
  title={Bayesian active model selection with an application to automated audiometry},
  author={Gardner, Jacob R. and Malkomes, Gustavo and Garnett, Roman and Weinberger, Kilian Q. and Barbour, Dennis and Cunningham, John P.},
  booktitle={Advances in Neural Information Processing Systems 28},
  series={NIPS},
  pages={2386--2394},
  year={2015}
}
@article{shively2009bayesian,
  title={A Bayesian approach to non-parametric monotone function estimation},
  author={Shively, Thomas S and Sager, Thomas W and Walker, Stephen G},
  journal={Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  volume={71},
  number={1},
  pages={159--175},
  year={2009},
  publisher={Wiley Online Library}
}
@article{keeley2019firing,
  title={Firing rate models for gamma oscillations},
  author={Keeley, Stephen and Byrne, {\'A}ine and Fenton, Andr{\'e} and Rinzel, John},
  journal={Journal of neurophysiology},
  year={2019},
  publisher={American Physiological Society Bethesda, MD}
}
@article{wilson1972excitatory,
  title={Excitatory and inhibitory interactions in localized populations of model neurons},
  author={Wilson, Hugh R and Cowan, Jack D},
  journal={Biophysical journal},
  volume={12},
  number={1},
  pages={1--24},
  year={1972},
  publisher={Elsevier}
}
@article{sobol1967distribution,
  title={On the distribution of points in a cube and the approximate evaluation of integrals},
  author={Sobol', Il'ya Meerovich},
  journal={Zhurnal Vychislitel'noi Matematiki i Matematicheskoi Fiziki},
  volume={7},
  number={4},
  pages={784--802},
  year={1967},
  publisher={Russian Academy of Sciences, Branch of Mathematical Sciences}
}
@article{shively2011nonparametric,
  title={Nonparametric function estimation subject to monotonicity, convexity and other shape constraints},
  author={Shively, Thomas S and Walker, Stephen G and Damien, Paul},
  journal={Journal of Econometrics},
  volume={161},
  number={2},
  pages={166--181},
  year={2011},
  publisher={Elsevier}
}
@article{settles2009active,
  title={Active learning literature survey},
  author={Settles, Burr},
  year={2009},
  publisher={University of Wisconsin-Madison Department of Computer Sciences}
}
@inproceedings{riihimaki2010gaussian,
  title={Gaussian processes with monotonicity information},
  author={Riihim{\"a}ki, Jaakko and Vehtari, Aki},
  booktitle={Proceedings of the thirteenth international conference on artificial intelligence and statistics},
  pages={645--652},
  year={2010},
  organization={JMLR Workshop and Conference Proceedings}
}
@inproceedings{KeeleySemiP,
    title={A Semi-Parametric Model for Decision Making in High-Dimensional Sensory Discrimination Tasks},
    author  = {Keeley, Stephen and Letham, Benjamin and  Tymms, Chase and Sanders, Craig and Shvartsman, Michael},
    year={2023},
    booktitle={Proceedings of the {AAAI} Conference on Artificial Intelligence},
    series={AAAI}
}
@article{neelon2004bayesian,
  title={Bayesian isotonic regression and trend analysis},
  author={Neelon, Brian and Dunson, David B},
  journal={Biometrics},
  volume={60},
  number={2},
  pages={398--406},
  year={2004},
  publisher={Wiley Online Library}
}
@book{feller1967introduction,
  title={An Introduction to Probability Theory and Its Applications},
  author={Feller, William},
  volume={1},
  year={1966},
  publisher={John Wiley and Sons},
  address={New York},
}
@article{navarro2009fast,
  title={Fast and accurate calculations for first-passage times in {W}iener diffusion models},
  author={Navarro, Daniel J. and Fuss, Ian G.},
  journal={Journal of Mathematical Psychology},
  volume={53},
  number={4},
  pages={222--230},
  year={2009},
}
@article{lo2014moment,
  title={Moment-matching approximations for Asian options},
  author={Lo, Chien-Ling and Palmer, Kenneth J. and Yu, Min-Teh},
  journal={The Journal of Derivatives},
  volume={21},
  number={4},
  pages={103--122},
  year={2014},
  publisher={Institutional Investor Journals Umbrella}
}
@inproceedings{gotovos2013active,
  title={Active learning for level set estimation},
  author={Gotovos, Alkis and Casati, Nathalie and Hitz, Gregory and Krause, Andreas},
  booktitle={Twenty-Third International Joint Conference on Artificial Intelligence},
  year={2013}
}
@article{owen2021adaptive,
  title={Adaptive Nonparametric Psychophysics},
  author={Owen, Lucy and Browder, Jonathan and Letham, Benjamin and Stocek, Gideon and Tymms, Chase and Shvartsman, Michael},
  journal={arXiv preprint arXiv:2104.09549},
  year={2021}
}
@inproceedings{gardner2015psychophysical,
author = {Gardner, Jacob R. and Song, Xinyu D. and Weinberger, Kilian Q. and Barbour, Dennis and Cunningham, John P.},
booktitle={Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence},
series={UAI},
pages = {286--297},
title = {Psychophysical detection testing with {B}ayesian active learning},
year = {2015},
}
@article{houlsby2011a,
  title={Bayesian active learning for classification and preference learning},
  author={Houlsby, Neil and Husz{\'a}r, Ferenc and Ghahramani, Zoubin and Lengyel, M{\'a}t{\'e}},
  journal={arXiv preprint arXiv:1112.5745},
  year={2011}
}

@article{Bogacz2006a,
author = {Bogacz, Rafal and Brown, Eric and Moehlis, Jeff and Holmes, Philip and Cohen, Jonathan D.},
journal = {Psychological Review},
number = {4},
pages = {700--765},
title = {{The physics of optimal decision making: A formal analysis of models of performance in two-alternative forced-choice tasks}},
volume = {113},
year = {2006}
}
@article{Siivola2018,
abstract = {Bayesian optimization (BO) is a global optimization strategy designed to find the minimum of an expensive black-box function, typically defined on a compact subset of d, by using a Gaussian process (GP) as a surrogate model for the objective. Although currently available acquisition functions address this goal with different degree of success, an over-exploration effect of the contour of the search space is typically observed. However, in problems like the configuration of machine learning algorithms, the function domain is conservatively large and with a high probability the global minimum does not sit on the boundary of the domain. We propose a method to incorporate this knowledge into the search process by adding virtual derivative observations in the GP at the boundary of the search space. We use the properties of GPs to impose conditions on the partial derivatives of the objective. The method is applicable with any acquisition function, it is easy to use and consistently reduces the number of evaluations required to optimize the objective irrespective of the acquisition used. We illustrate the benefits of our approach in an extensive experimental comparison.},
archivePrefix = {arXiv},
arxivId = {1704.00963},
author = {Siivola, Eero and Vehtari, Aki and Vanhatalo, Jarno and Gonzalez, Javier and Andersen, Michael Riis},
doi = {10.1109/MLSP.2018.8516936},
eprint = {1704.00963},
file = {:Users/mshvarts/Library/Application Support/Mendeley Desktop/Downloaded/Siivola et al. - 2018 - Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observat.pdf:pdf},
isbn = {9781538654774},
issn = {21610371},
journal = {IEEE International Workshop on Machine Learning for Signal Processing, MLSP},
keywords = {Bayesian optimization,Gaussian process,Virtual derivative sign observation},
title = {{Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observations}},
volume = {2018-Septe},
year = {2018}
}
@article{wuerger2020spatio,
  title={Spatio-chromatic contrast sensitivity under mesopic and photopic light levels},
  author={Wuerger, Sophie and Ashraf, Maliha and Kim, Minjung and Martinovic, Jasna and P{\'e}rez-Ortiz, Mar{\'\i}a and Mantiuk, Rafa{\l} K},
  journal={Journal of Vision},
  volume={20},
  number={4},
  pages={23--23},
  year={2020},
  publisher={The Association for Research in Vision and Ophthalmology}
}

@inproceedings{furnkranz03,
  title={Pairwise preference learning and ranking},
  author={F{\"u}rnkranz, Johannes and H{\"u}llermeier, Eyke},
  booktitle={Proceedings of the European Conference on Machine Learning},
  pages={145--156},
  year={2003},
  series={ECML},
}

@inproceedings{chu05,
  title={Preference learning with {G}aussian processes},
  author={Chu, Wei and Ghahramani, Zoubin},
  booktitle={Proceedings of the 22nd International Conference on Machine Learning},
  pages={137--144},
  year={2005},
  series={ICML},
}
@book{fechner1860,
  title={Elemente der Psychophysik},
  author={Fechner, Gustav Theodor},
  volume={1},
  year={1860},
  publisher={Breitkopf und H{\"a}rtel},
}

@inproceedings{biyik20,
  title={Active preference-based {G}aussian process regression for reward learning},
  author={Biyik, Erdem and Huynh, Nicolas and Kochenderfer, Mykel J. and Sadigh, Dorsa},
  booktitle={Proceedings of Robotics: Science and Systems XVI},
  series={RSS},
  year={2020},
}

@article{polar,
  title={{POLAR}: Preference Optimization and Learning Algorithms for Robotics},
  author={Tucker, Maegan and Li, Kejun  and Yue, Yisong and Ames, Aaron D.},
  journal={arXiv preprint arXiv:2208.04404},
  year={2022}
}

@inproceedings{tucker21,
  title={Preference-Based Learning for User-Guided {HZD} Gait Generation on Bipedal Walking Robots},
  author={Tucker, Maegan and Csomay-Shanklin, Noel and Ma, Wen-Loong and Ames, Aaron D.},
  booktitle={Proceedings of the {IEEE} International Conference on Robotics and Automation},
  series={ICRA},
  year={2021},
}

@inproceedings{cosner22,
  title={Safety-Aware Preference-Based Learning for Safety-Critical Control},
  author={Cosner, Ryan K. and Tucker, Maegan and Taylor, Andrew J. and Li, Kejun and Molnar, Tamas G. and Ubellacker, Wyatt and Alan, Anil and Orosz, G{\'a}bor and Yue, Yisong and Ames, Aaron D.},
  booktitle={Proceedings of the 4th Annual Conference on Learning for Dynamics and Control},
  year={2022},
  series={L4DC},
}

@article{thatte17,
  title={A sample-efficient black-box optimizer to train policies for human-in-the-loop systems with user preferences},
  author={Thatte, Nitish and Duan, Helei and Geyer, Hartmut},
  journal={{IEEE} Robotics \& Automation Letters},
  year={2017},
  volume={2},
  number={2},
  pages={993--1000},
}

@inproceedings{tucker20,
  title={Preference-based learning for exoskeleton gait optimization},
  author={Tucker, Maegan and Novoseller, Ellen and Kann, Claudia and Sui, Yanan and Yue, Yisong and Burdick, Joel W. and Ames, Aaron D.},
  booktitle={Proceedings of the {IEEE} International Conference on Robotics and Automation},
  series={ICRA},
  year={2020},
  pages={2351--2357},
}

@inproceedings{guan22,
	author = {Guan, Phillip and Mercier, Olivier and Shvartsman, Michael and Lanman, Douglas},
	title = {Perceptual Requirements for Eye-Tracked Distortion Correction in {VR}},
	year = {2022},
	booktitle = {ACM SIGGRAPH 2022 Conference Proceedings},
	series = {SIGGRAPH}
}

@inproceedings{guan23,
	author = {Guan, Phillip and Penner, Eric and Hegland, Joel and Letham, Benjamin and Lanman, Douglas},
	title = {Perceptual Requirements for World-Locked Rendering in {AR} and {VR}},
	year = {2023},
	booktitle = {SIGGRAPH Asia 2023 Conference Papers},
	series = {SA}
}

@inproceedings{bope,
  title={Preference exploration for efficient {B}ayesian optimization with multiple outcomes},
  author={Lin, Zhiyuan Jerry and Astudillo, Raul and Frazier, Peter I. and Bakshy, Eytan},
  booktitle={Proceedings of the 25th International Conference on Artificial Intelligence and Statistics},
  series={AISTATS},
  year={2022},
  pages={4235--4258},
}
@article{strasburger2001converting,
  title={Converting between measures of slope of the psychometric function},
  author={Strasburger, Hans},
  journal={Perception \& Psychophysics},
  volume={63},
  number={8},
  pages={1348--1355},
  year={2001},
  publisher={Springer}
}
@article{drugowitsch2012cost,
  title={The cost of accumulating evidence in perceptual decision making},
  author={Drugowitsch, Jan and Moreno-Bote, Rub{\'e}n and Churchland, Anne K and Shadlen, Michael N and Pouget, Alexandre},
  journal={Journal of Neuroscience},
  volume={32},
  number={11},
  pages={3612--3628},
  year={2012},
  publisher={Soc Neuroscience}
}
@article{kiani2014choice,
  title={Choice certainty is informed by both evidence and decision time},
  author={Kiani, Roozbeh and Corthell, Leah and Shadlen, Michael N},
  journal={Neuron},
  volume={84},
  number={6},
  pages={1329--1342},
  year={2014},
  publisher={Elsevier}
}
@article{van2016common,
  title={A common mechanism underlies changes of mind about decisions and confidence},
  author={van Den Berg, Ronald and Anandalingam, Kavitha and Zylberberg, Ariel and Kiani, Roozbeh and Shadlen, Michael N and Wolpert, Daniel M},
  journal={Elife},
  volume={5},
  pages={e12192},
  year={2016},
  publisher={eLife Sciences Publications, Ltd}
}
@misc{spotminimini2020github,
  author = {Maurice Rahme and Ian Abraham and Matthew Elwin and Todd Murphey},
  title = {{SpotMiniMini}: {Pybullet} {Gym} Environment for Gait Modulation with {Bezier} Curves},
  url = {https://github.com/moribots/spot_mini_mini},
  version = {2.1.0},
  year = {2020},
}
@book{Laming1968a,
   author = {D. R. J Laming},
   city = {New York},
   publisher = {Academic Press},
   title = {Information Theory of Choice-Reaction Times},
   year = {1968},
}
@article{voss2015assessing,
  title={Assessing cognitive processes with diffusion model analyses: A tutorial based on fast-dm-30},
  author={Voss, Andreas and Voss, Jochen and Lerche, Veronika},
  journal={Frontiers in Psychology},
  volume={6},
  pages={336},
  year={2015},
}
@article{van2017ez,
  title={The {EZ} diffusion model provides a powerful test of simple empirical effects},
  author={van Ravenzwaaij, Don and Donkin, Chris and Vandekerckhove, Joachim},
  journal={Psychonomic Bulletin \& Review},
  volume={24},
  number={2},
  pages={547--556},
  year={2017},
}

@article{gym,
  Title = {{OpenAI} Gym},
  Author = {Greg Brockman and Vicki Cheung and Ludwig Pettersson and Jonas Schneider and John Schulman and Jie Tang and Wojciech Zaremba},
  journal={arXiv preprint arXiv:1606.01540},
  year={2016}
}
@article{Clithero2018a,
   author = {John A. Clithero},
   journal = {Journal of Economic Behavior \& Organization},
   pages = {344--375},
   title = {Improving out-of-sample predictions using response times and a model of the decision process},
   volume = {148},
   year = {2018},
}
@inproceedings{Kingma2014a,
   author = {Diederik P. Kingma and Danilo J. Rezende and Shakir Mohamed and Max Welling},
   title = {Semi-Supervised Learning with Deep Generative Models},
  booktitle={Advances in Neural Information Processing Systems 27},
  series={NIPS},
  year={2014}
}
@article{Voss2008,
   author = {Andreas Voss and Jochen Voss},
   title = {A Fast Numerical Algorithm for the Estimation of Diffusion Model Parameters},
   year = {2008},
   journal={Journal of Mathematical Psychology},
   volume=52,
   number=1,
   pages={1--9},
}
@article{Ashby1983a,
   author = {F. Gregory Ashby},
   number = {3},
   journal = {Journal of Mathematical Psychology},
   pages = {277--297},
   title = {A biased random walk model for two choice reaction times},
   volume = {27},
   year = {1983},
}
@article{Donders1969a,
   author = {F. C. Donders},
   journal = {Acta Psychologica},
   pages = {412--431},
   title = {On the speed of mental processes},
   volume = {30},
   year = {1969},
}
@article{Gold2002a,
   author = {Joshua I. Gold and Michael N. Shadlen},
   number = {2},
   journal = {Neuron},
   pages = {299--308},
   title = {Banburismus and the Brain},
   volume = {36},
   year = {2002},
}
@article{Gold2007a,
   author = {Joshua I. Gold and Michael N. Shadlen},
   number = {1},
   journal = {Annual Review of Neuroscience},
   pages = {535--574},
   title = {The Neural Basis of Decision Making},
   volume = {30},
   year = {2007},
}
@article{Griffith2021a,
   author = {Thom Griffith and Sophie-Anne Baker and Nathan F. Lepora},
   journal = {Journal of Mathematical Psychology},
   pages = {102544},
   title = {The statistics of optimal decision making: Exploring the relationship between signal detection theory and sequential analysis},
   volume = {103},
   year = {2021},
}
@book{Kingdom2016a,
   author = {Frederick Kingdom and Nicholas Prins},
   publisher = {Elsevier Academic Press},
   title = {Psychophysics: A Practical Introduction},
   year = {2016},
   edition={2nd},
}
@article{Ratcliff1978a,
   author = {Roger Ratcliff},
   number = {2},
   journal = {Psychological Review},
   pages = {59--108},
   title = {A theory of memory retrieval},
   volume = {85},
   year = {1978},
}
@article{Sternberg1969a,
   author = {S Sternberg},
   number = {4},
   journal = {American Scientist},
   pages = {421--457},
   title = {Memory-scanning: mental processes revealed by reaction-time experiments},
   volume = {57},
   year = {1969},
}
@article{Wiecki2013a,
   author = {Thomas V. Wiecki and Imri Sofer and Michael J. Frank},
   journal = {Frontiers in Neuroinformatics},
   title = {{HDDM}: Hierarchical {B}ayesian estimation of the Drift-Diffusion Model in {P}ython},
   volume = {7},
   year = {2013},
   pages={14},
}
@article{Shinn2020,
   author = {Maxwell Shinn and Norman H. Lam and John D. Murray},
   journal = {eLife},
   title = {A flexible framework for simulating and fitting generalized drift-diffusion models},
   volume = {9},
   year = {2020},
   pages={e56938},
}

@inproceedings{mchutchon2011gaussian,
  title={Gaussian process training with input noise},
  author={McHutchon, Andrew and Rasmussen, Carl},
  booktitle={Advances in Neural Information Processing Systems 24},
  series={NIPS},
  pages={2386--2394},
  year={2011}
}

@article{ratcliff2002estimating,
  title={Estimating parameters of the diffusion model: Approaches to dealing with contaminant reaction times and parameter variability},
  author={Ratcliff, Roger and Tuerlinckx, Francis},
  journal={Psychonomic Bulletin \& Review},
  volume={9},
  number={3},
  pages={438--481},
  year={2002},
  publisher={Springer}
}
@article{krajbich2011multialternative,
  title={Multialternative drift-diffusion model predicts the relationship between visual fixations and choice in value-based decisions},
  author={Krajbich, Ian and Rangel, Antonio},
  journal={Proceedings of the National Academy of Sciences},
  volume={108},
  number={33},
  pages={13852--13857},
  year={2011},
  publisher={National Acad Sciences}
}
@article{tsetsos2011testing,
  title={Testing multi-alternative decision models with non-stationary evidence},
  author={Tsetsos, Konstantinos and Usher, Marius and McClelland, James L.},
  journal={Frontiers in Neuroscience},
  volume={5},
  pages={8457},
  year={2011},
  publisher={Frontiers}
}
@article{barthelme2014expectation,
  title={Expectation propagation for likelihood-free inference},
  author={Barthelm{\'e}, Simon and Chopin, Nicolas},
  journal={Journal of the American Statistical Association},
  volume={109},
  number={505},
  pages={315--333},
  year={2014},
  publisher={Taylor \& Francis}
}
@article{beaumont2019approximate,
  title={Approximate {B}ayesian computation},
  author={Beaumont, Mark A.},
  journal={Annual Review of Statistics and its Application},
  volume={6},
  pages={379--403},
  year={2019},
  publisher={Annual Reviews}
}
@article{tran2017variational,
  title={Variational {B}ayes with intractable likelihood},
  author={Tran, Minh-Ngoc and Nott, David J. and Kohn, Robert},
  journal={Journal of Computational and Graphical Statistics},
  volume={26},
  number={4},
  pages={873--882},
  year={2017},
  publisher={Taylor \& Francis}
}
@article{villacampa2021multi,
  title={Multi-class {G}aussian process classification with noisy inputs},
  author={Villacampa-Calvo, Carlos and Zald{\'\i}var, Bryan and Garrido-Merch{\'a}n, Eduardo C. and Hern{\'a}ndez-Lobato, Daniel},
  journal={Journal of Machine Learning Research},
  volume={22},
  number={36},
  pages={1--52},
  year={2021}
}