文献资料
DDM 及相关模型的文献资料汇总,共收录 62 篇文献。
Smith, P. L. (2000). Stochastic Dynamic Models of
Smith, P. L. (2000). Stochastic Dynamic Models of Response Time and Accuracy: A Foundational Primer. Journal of Mathematical Psychology, 44(3), 408–463. https://doi.org/10.1006/jmps.1999.1260
Ratcliff, R., & Smith, P. L. (2004). A Comparison
Ratcliff, R., & Smith, P. L. (2004). A Comparison of Sequential Sampling Models for Two-Choice Reaction Time. Psychological Review, 111(2), 333–367. https://doi.org/10.1037/0033-295X.111.2.333
Ratcliff, R., & McKoon, G. (2008). The diffusion d
Ratcliff, R., & McKoon, G. (2008). The diffusion decision model: theory and data for two-choice decision tasks. Neural computation, 20(4), 873-922. https://doi.org/10.1162/neco.2008.12-06-420
Navarro, D. J., & Fuss, I. G. (2009). Fast and acc
Navarro, D. J., & Fuss, I. G. (2009). Fast and accurate calculations for first-passage times in Wiener diffusion models. Journal of Mathematical Psychology, 53(4), 222–230. https://doi.org/10.1016/j.jmp.2009.02.003
Krajbich, I., Armel, C., & Rangel, A. (2010). Visu
Krajbich, I., Armel, C., & Rangel, A. (2010). Visual fixations and the computation and comparison of value in simple choice. Nature Neuroscience, 13(10), 1292–1298. https://doi.org/10.1038/nn.2635
Krajbich, I., & Rangel, A. (2011). Multialternativ
Krajbich, I., & Rangel, A. (2011). Multialternative drift-diffusion model predicts the relationship between visual fixations and choice in value-based decisions. Proceedings of the National Academy of Sciences of the United...
Voss, A., Nagler, M., & Lerche, V. (2013). Diffusi
Voss, A., Nagler, M., & Lerche, V. (2013). Diffusion Models in Experimental Psychology: A Practical Introduction. Experimental Psychology, 60(6), 385–402. https://doi.org/10.1027/1618-3169/a000218
Wiecki, T. V., Sofer, I., & Frank, M. J. (2013). H
Wiecki, T. V., Sofer, I., & Frank, M. J. (2013). HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python. Frontiers in Neuroinformatics, 7(JULY 2013), 1–10. https://doi.org/10.3389/fninf.2013.00014
Lerche, V., Voss, A., & Nagler, M. (2017). How man
Lerche, V., Voss, A., & Nagler, M. (2017). How many trials are required for parameter estimation in diffusion modeling? A comparison of different optimization criteria. Behavior Research Methods, 49(2), 513–537....
Pleskac, T. J., Joseph, C., & David J, J. (2018).
Pleskac, T. J., Joseph, C., & David J, J. (2018). How race affects evidence accumulation during the decision to shoot. Psychonomic Bulletin & Review, 30. https://doi.org/10.3758/s13423-017-1369-6
Krajbich, I. (2019). Accounting for attention in s
Krajbich, I. (2019). Accounting for attention in sequential sampling models of decision making. Current Opinion in Psychology, 29, 6–11. https://doi.org/10.1016/j.copsyc.2018.10.008
Roberts, I. D., & Hutcherson, C. A. (2019). Affect
Roberts, I. D., & Hutcherson, C. A. (2019). Affect and decision making: Insights and predictions from computational models. Trends in cognitive sciences, 23(7), 602-614.
Alan N, T., Timothy J, P., & Ralf H. J. M, K. (202
Alan N, T., Timothy J, P., & Ralf H. J. M, K. (2020). Wise or mad crowds? The cognitive mechanisms underlying information cascades. Science Advances, 6(29), 1–11. https://doi.org/10.1126/sciadv.abb0266 Tump, Pleskac,...
Evans, N. J., Hawkins, G. E., & Brown, S. D. (2020
Evans, N. J., Hawkins, G. E., & Brown, S. D. (2020). The Role of Passing Time in Decision-Making. Journal of Experimental Psychology: Learning, Memory, and Cognition, 46(2), 316–326. https://doi.org/10.1037/xlm0000725
Konovalov, A., & Krajbich, I. (2020). Mouse tracki
Konovalov, A., & Krajbich, I. (2020). Mouse tracking reveals structure knowledge in the absence of model-based choice. Nature Communications, 11(1), 1–9. https://doi.org/10.1038/s41467-020-15696-w
Pedersen, M. L., & Frank, M. J. (2020). Simultaneo
Pedersen, M. L., & Frank, M. J. (2020). Simultaneous Hierarchical Bayesian Parameter Estimation for Reinforcement Learning and Drift Diffusion Models: A Tutorial and Links to Neural Data. Computational Brain &...
Zhao, W. J., Walasek, L., & Bhatia, S. (2020). Psy
Zhao, W. J., Walasek, L., & Bhatia, S. (2020). Psychological mechanisms of loss aversion: A drift-diffusion decomposition. Cognitive Psychology, 123, 101331. https://doi.org/10.1016/j.cogpsych.2020.101331
Mormann, M., & Russo, J. E. (2021). Does Attention
Mormann, M., & Russo, J. E. (2021). Does Attention Increase the Value of Choice Alternatives? Trends in Cognitive Sciences, 25(4), 305–315. https://doi.org/10/gjhp7w
O’Connell, R. G., & Kelly, S. P. (2021). Neurophys
O’Connell, R. G., & Kelly, S. P. (2021). Neurophysiology of Human Perceptual Decision-Making. Annual Review of Neuroscience, 44(1), 495–516. https://doi.org/10.1146/annurev-neuro-092019-100200
Boelts, Lueckman, Gao & Macke. (2022). "Flexible a"
Boelts, Lueckman, Gao & Macke. (2022). “Flexible and efficient simulation-based inference for models of decision-making.” Elife 11: e77220. Flexible and efficient simulation-based inference for models of decision-making Flexible and efficient...
Cendri A Hutcherson, Anita Tusche (2022) Evidence
Cendri A Hutcherson, Anita Tusche (2022) Evidence accumulation, not ‘self-control’, explains dorsolateral prefrontal activation during normative choice eLife 11:e65661 https://doi.org/10.7554/eLife.65661
Glickman, M., et al. (2022). "Evidence integration"
Glickman, M., et al. (2022). “Evidence integration and decision confidence are modulated by stimulus consistency.” Nature Human Behaviour.
Hutcherson, C. A., & Tusche, A. (2022). Evidence a
Hutcherson, C. A., & Tusche, A. (2022). Evidence accumulation, not ‘self-control’, explains dorsolateral prefrontal activation during normative choice. ELife, 11, e65661. https://doi.org/10.7554/eLife.65661
Kaanders, P., et al. (2022). "Humans actively samp"
Kaanders, P., et al. (2022). “Humans actively sample evidence to support prior beliefs.” Elife 11.
Lei, Y. and A. Solway (2022). "Conflict and compet"
Lei, Y. and A. Solway (2022). “Conflict and competition between model-based and model-free control.” PLoS Comput Biol 18(5): e1010047.
Luo, J., Yang, M., & Wang, L. (2022). Learned irre
Luo, J., Yang, M., & Wang, L. (2022). Learned irrelevant stimulus-response associations and proportion congruency effect: A diffusion model account. Journal of Experimental Psychology: Learning, Memory, and Cognition. https://doi.org/10.1037/xlm0001158 JEP...
Shevlin, B. R. K., et al. (2022). "High-value deci"
Shevlin, B. R. K., et al. (2022). “High-value decisions are fast and accurate, inconsistent with diminishing value sensitivity.” Proceedings of the National Academy of Sciences 119(6).
Spektor, M. S., et al. (2022). "The repulsion effe"
Spektor, M. S., et al. (2022). “The repulsion effect in preferential choice and its relation to perceptual choice.” Cognition 225: 105164.
von Krause, M., Radev, S. T., & Voss, A. (2022). M
von Krause, M., Radev, S. T., & Voss, A. (2022). Mental speed is high until age 60 as revealed by analysis of over a million participants. Nature Human Behaviour, 6(5),...
习得的无关刺激反应联结与比例一致性效应
习得的无关刺激反应联结与比例一致性效应:基于扩散模型的研究 发表时间: 2022年8月25日 期刊: Journal of Experimental Psychology: Learning, Memory, and Cognition 第一作者: 罗娇容(华南师范大学) 研究简介 2022年8月25日,华南师范大学心理学院王凌课题组在认知心理学权威期刊Journal of Experimental Psychology: Learning, Memory, and Cognition上发表题为“Learned Irrelevant Stimulus-Response Associations and Proportion Congruency Effect:...
Boehm, U., Annis, J., Frank, M. J., Hawkins, G. E.
Boehm, U., Annis, J., Frank, M. J., Hawkins, G. E., Heathcote, A., Kellen, D., … Wagenmakers, E.-J. (2018). Estimating across-trial variability parameters of the Diffusion Decision Model: Expert advice and...
Bogacz, R., Brown, E., Moehlis, J., Holmes, P., &
Bogacz, R., Brown, E., Moehlis, J., Holmes, P., & Cohen, J. D. (2006). The physics of optimal decision making: A formal analysis of models of performance in two-alternative forced-choice tasks....
Busemeyer, J. R., Gluth, S., Rieskamp, J., & Turne
Busemeyer, J. R., Gluth, S., Rieskamp, J., & Turner, B. M. (2019). Cognitive and Neural Bases of Multi-Attribute, Multi-Alternative, Value-based Decisions. Trends in Cognitive Sciences, 23(3), 251–263. https://doi.org/10.1016/j.tics.2018.12.003
Cao, Y., Luo, J., Li, J., Wang, L., & Ma, N. (2025
Cao, Y., Luo, J., Li, J., Wang, L., & Ma, N. (2025, in press). Commonality and diversity of impairments in inhibitory tasks after total sleep deprivation: Evidence from drift diffusion...
Chu, S., Hutcherson, C., Ito, R., & Lee, A. C. H.
Chu, S., Hutcherson, C., Ito, R., & Lee, A. C. H. (2023). Elucidating medial temporal and frontal lobe contributions to approach-avoidance conflict decision-making using functional MRI and the hierarchical drift...
Donkin, C., Nosofsky, R. M., Gold, J. M., & Shiffr
Donkin, C., Nosofsky, R. M., Gold, J. M., & Shiffrin, R. M. (2013). Discrete-slots models of visual working-memory response times. Psychological Review, 120(4), 873–902. https://doi.org/10.1037/a0034247
Evidence accumulation, not ‘self-control’, explain
Evidence accumulation, not ‘self-control’, explains dorsolateral prefrontal activation during normative choice Cendri A Hutcherson, Anita Tusche (2022) Evidence accumulation, not ‘self-control’, explains dorsolateral prefrontal activation during normative choice eLife 11:e65661...
Fengler, A., Govindarajan, L. N., Chen, T., & Fran
Fengler, A., Govindarajan, L. N., Chen, T., & Frank, M. J. (2021). Likelihood approximation networks (LANs) for fast inference of simulation models in cognitive neuroscience. ELife, 10, e65074. https://doi.org/10.7554/eLife.65074
Forstmann, B. U., Ratcliff, R., & Wagenmakers, E.-
Forstmann, B. U., Ratcliff, R., & Wagenmakers, E.-J. (2016). Sequential Sampling Models in Cognitive Neuroscience: Advantages, Applications, and Extensions. Annual Review of Psychology, 67(1), 641–666. https://doi.org/10/gf6bzg
Ghaderi-Kangavari, A., Rad, J. A., & Nunez, M. D.
Ghaderi-Kangavari, A., Rad, J. A., & Nunez, M. D. (2023). A General Integrative Neurocognitive Modeling Framework to Jointly Describe EEG and Decision-making on Single Trials. Computational Brain & Behavior. https://doi.org/10.1007/s42113-023-00167-4...
Ging-Jehli, N. R., Ratcliff, R., & Arnold, L. E. (
Ging-Jehli, N. R., Ratcliff, R., & Arnold, L. E. (2020). Improving neurocognitive testing using computational psychiatry—A systematic review for ADHD. Psychological Bulletin, No Pagination Specified-No Pagination Specified. https://doi.org/10.1037/bul0000319
Huang, J., Busemeyer, J., Ebelt, Z., & Pothos, E.
Huang, J., Busemeyer, J., Ebelt, Z., & Pothos, E. (2024). Bridging the gap between subjective probability and probability judgments: the Quantum Sequential Sampler. Psychological Review.
Johnson, D. J., Hopwood, C. J., Cesario, J., & Ple
Johnson, D. J., Hopwood, C. J., Cesario, J., & Pleskac, T. J. (2017). Advancing Research on Cognitive Processes in Social and Personality Psychology: A Hierarchical Drift Diffusion Model Primer. Social...
Kaanders, P., Sepulveda, P., Folke, T., Ortoleva,
Kaanders, P., Sepulveda, P., Folke, T., Ortoleva, P., & De Martino, B. (2022). Humans actively sample evidence to support prior beliefs. ELife, 11, e71768. https://doi.org/10.7554/eLife.71768
Kelly SP, Corbett EA, O'Connell RG. Neurocomputati
Kelly SP, Corbett EA, O’Connell RG. Neurocomputational mechanisms of prior-informed perceptual decision-making in humans. Nat Hum Behav. 2021 Apr;5(4):467-481. doi: 10.1038/s41562-020-00967-9. Epub 2020 Dec 14. PMID: 33318661.
Lawlor VM, Webb CA, Wiecki TV, Frank MJ, Trivedi M
Lawlor VM, Webb CA, Wiecki TV, Frank MJ, Trivedi M, Pizzagalli DA, Dillon DG (2019). Dissecting the impact of depression on decision-making. Psychological Medicine 1–10. https://doi.org/10.1017/ S0033291719001570
Leong, Y. C., Hughes, B. L., Wang, Y., & Zaki, J.
Leong, Y. C., Hughes, B. L., Wang, Y., & Zaki, J. (2019). Neurocomputational mechanisms underlying motivated seeing. Nature Human Behaviour, 3(9), 962–973. https://doi.org/10.1038/s41562-019-0637-z
Nak, J., Volz, L., Verbrugh, V., Borsboom, D., & v
Nak, J., Volz, L., Verbrugh, V., Borsboom, D., & van Dongen, N. N. N. (2025, May 19). The PsychoModels database: Facilitating the exchange of formal models. https://doi.org/10.31234/osf.io/ptzdg_v1
Polanía, R., Moisa, M., Opitz, A., Grueschow, M.,
Polanía, R., Moisa, M., Opitz, A., Grueschow, M., & Ruff, C. C. (2015). The precision of value-based choices depends causally on fronto-parietal phase coupling. Nature Communications, 6(1), 8090. https://doi.org/10.1038/ncomms9090
Ratcliff, R., Smith, P. L., Brown, S. D.,& McKoon,
Ratcliff, R., Smith, P. L., Brown, S. D.,& McKoon, G. (2016). Diffusion Decision Model: Current Issues and History.Trends in Cognitive Sciences, 20(4), 260–281.
Shevlin, B. R. K., Smith, S. M., Hausfeld, J., & K
Shevlin, B. R. K., Smith, S. M., Hausfeld, J., & Krajbich, I. (2022). High-value decisions are fast and accurate, inconsistent with diminishing value sensitivity. Proceedings of the National Academy of...
Thomas, A. W., Molter, F., Krajbich, I., Heekeren,
Thomas, A. W., Molter, F., Krajbich, I., Heekeren, H. R., & Mohr, P. N. C. (2019). Gaze bias differences capture individual choice behaviour. Nature Human Behaviour, 3(6), 625–635. https://doi.org/10.1038/s41562-019-0584-8
Turner, B. M., van Maanen, L., & Forstmann, B. U.
Turner, B. M., van Maanen, L., & Forstmann, B. U. (2015). Informing cognitive abstractions through neuroimaging: The neural drift diffusion model. Psychological Review, 122(2), 312–336. https://doi.org/10/f67stz
Van Marcke, H., Denmat, P. L., Verguts, T., & Dese
Van Marcke, H., Denmat, P. L., Verguts, T., & Desender, K. (2024). Manipulating Prior Beliefs Causally Induces Under- and Overconfidence. Psychological Science, 35(4), 358-375. https://doi.org/10.1177/09567976241231572
Weindel, G., gajdos, thibault, Burle, B., & Alari
Weindel, G., gajdos, thibault, Burle, B., & Alario, F. X. (2021). The Decisive Role of Non-Decision Time for Interpreting the Parameters of Decision Making Models [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/gewb3
Zhu, J.-Q., Sundh, J., Spicer, J., Chater, N., & S
Zhu, J.-Q., Sundh, J., Spicer, J., Chater, N., & Sanborn, A. N. (2023, June 8). The Autocorrelated Bayesian Sampler: A Rational Process for Probability Judgments, Estimates, Confidence Intervals, Choices, Confidence...
Luo, J., Hao, C., Ma, N., & Wang, L. (2024). Sleep
Luo, J., Hao, C., Ma, N., & Wang, L. (2024). Sleep deprivation affects interference control: A diffusion model analysis. Journal of Experimental Psychology: Human Perception and Performance, 50(2), 193–215. https://doi.org/10.1037/xhp0001180...
Wu, Y., Radev, S., & Tuerlinckx, F. (2024). Testin
Wu, Y., Radev, S., & Tuerlinckx, F. (2024). Testing and improving the robustness of amortized bayesian inference for cognitive models (No. arXiv:2412.20586). arXiv. https://doi.org/10.48550/arXiv.2412.20586
Lu, W., & Wan, X. (2025). Closing the loop: A dyna
Lu, W., & Wan, X. (2025). Closing the loop: A dynamic neural network model integrating decision making and metacognition. bioRxiv : The Preprint Server for Biology. https://doi.org/10.1101/2025.03.06.641797
Luo, T., Xu, M., Zheng, Z., & Okazawa, G. (2025).
Luo, T., Xu, M., Zheng, Z., & Okazawa, G. (2025). Limitation of switching sensory information flow in flexible perceptual decision making. Nature Communications, 16(1), 172. https://doi.org/10.1038/s41467-024-55686-w
Zhan, B., Chen, Y., Wang, R., & Jiang, Y. (2025).
Zhan, B., Chen, Y., Wang, R., & Jiang, Y. (2025). Prolonged visual perceptual changes induced by short-term dyadic training: The roles of confidence and autistic traits in social learning. iScience,...
Chen, H., Heathcote, A., PhD, & Osth, A. F. (2026)
Chen, H., Heathcote, A., PhD, & Osth, A. F. (2026). Linear ballistic accumulator models of confidence and response time (Jsqx5_v1). PsyArXiv. https://osf.io/preprints/psyarxiv/jsqx5_v1/
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在 _papers/ 目录下创建新的 Markdown 文件。