An extension of the shifted Wald model of human response times: Capturing the time dynamic properties of human cognition : Trial-varying Wald model.
Adaptive automation
Bayesian
Cognitive model
Workload
Journal
Psychonomic bulletin & review
ISSN: 1531-5320
Titre abrégé: Psychon Bull Rev
Pays: United States
ID NLM: 9502924
Informations de publication
Date de publication:
04 Dec 2023
04 Dec 2023
Historique:
accepted:
21
10
2023
medline:
5
12
2023
pubmed:
5
12
2023
entrez:
4
12
2023
Statut:
aheadofprint
Résumé
Despite the ubiquitous nature of evidence accumulation models in cognitive and experimental psychology, there has been a comparatively limited uptake of such techniques in the applied literature. While quantifying latent cognitive processing properties has significant potential for applied domains such as adaptive work systems, accumulator models often fall short in practical applications. Two primary reasons for these shortcomings are the complexities and time needed for the application of cognitive models, and the failure of current models to capture systematic trial-to-trial variability in parameters. In this manuscript, we develop a novel, trial-varying extension of the shifted Wald model to address these concerns. By leveraging conjugate properties of the Wald distribution, we derive computationally efficient solutions for threshold and drift parameters which can be updated instantaneously with new data. The resulting model allows the quantification of systematic variation in latent cognitive parameters across trials and we demonstrate the utility of such analyses through simulations and an exemplar application to an existing data set. The analytic nature of our solutions opens the door for real-world applications, significantly extending the reach of computational models of behavioral responses.
Identifiants
pubmed: 38049574
doi: 10.3758/s13423-023-02418-8
pii: 10.3758/s13423-023-02418-8
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Australian Research Council
ID : DE200101130, DE200101130
Informations de copyright
© 2023. Crown.
Références
Alister, M., & Evans, N. J. (2023). A model to describe how cognitive processes vary over time: The across trial diffusion model. In Proceedings of the annual meeting of the cognitive science society (Vol. 45).
Alouini, M.-S., Abdi, A., & Kaveh, M. (2001). Sum of gamma variates and performance of wireless communication systems over nakagami-fading channels. IEEE Transactions on Vehicular Technology, 50(6), 1471–1480.
doi: 10.1109/25.966578
Anders, R., Alario, F., & Van Maanen, L. (2016). The shifted wald distribution for response time data analysis. Psychological Methods, 21(3), 309.
pubmed: 26867155
doi: 10.1037/met0000066
Arciszewski, H. F., De Greef, T. E., & Van Delft, J. H. (2009). Adaptive automation in a naval combat management system. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 39(6), 1188–1199.
doi: 10.1109/TSMCA.2009.2026428
Banerjee, A. K., & Bhattacharyya, G. (1979). Bayesian results for the inverse gaussian distribution with an application. Technometrics, 21(2), 247–251.
doi: 10.1080/00401706.1979.10489756
Boag, R. J., Strickland, L., Heathcote, A., Neal, A., Palada, H., & Loft, S. (2023). Evidence accumulation modelling in the wild: Understanding safety-critical decisions. Trends in Cognitive Sciences
Boag, R. J., Strickland, L., Heathcote, A., Neal, A., & Loft, S. (2019). Cognitive control and capacity for prospective memory in complex dynamic environments. Journal of Experimental Psychology: General, 148(12), 2181.
Boag, R. J., Strickland, L., Loft, S., & Heathcote, A. (2019). Strategic attention and decision control support prospective memory in a complex dual-task environment. Cognition, 191, 103974.
pubmed: 31234118
doi: 10.1016/j.cognition.2019.05.011
Boehm, U., Annis, J., Frank, M. J., Hawkins, G. E., Heathcote, A., Kellen, D., Palmeri, T. J., et al. (2018). Estimating across-trial variability parameters of the diffusion decision model: Expert advice and recommendations. Journal of Mathematical Psychology, 87, 46–75.
doi: 10.1016/j.jmp.2018.09.004
Boehm-Davis, D. A., Durso, F. T., & Lee, J. D. (2015). Apa handbook of human systems integration. American Psychological Association.
doi: 10.1037/14528-000
Boehm, U., Van Maanen, L., Forstmann, B., & van Rijn, H. (2014). Trial-by-trial fluctuations in cnv amplitude reflect anticipatory adjustment of response caution. NeuroImage, 96, 95–105.
pubmed: 24699015
doi: 10.1016/j.neuroimage.2014.03.063
Bowden, V. K., Loft, S., Wilson, M. D., Howard, J., & Visser, T. A. (2019). The long road home from distraction: Investigating the time-course of distraction recovery in driving. Accident Analysis & Prevention, 124, 23–32.
doi: 10.1016/j.aap.2018.12.012
Brown, S. D., & Heathcote, A. (2008). The simplest complete model of choice response time: Linear ballistic accumulation. Cognitive Psychology, 57(3), 153–178.
pubmed: 18243170
doi: 10.1016/j.cogpsych.2007.12.002
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., . . . Riddell, A. (2017). Stan: A probabilistic programming language. Journal of Statistical Software, 76(1).
Castro, S. C., Heathcote, A., Cooper, J. M., & Strayer, D. L. (2022). Dynamic workload measurement and modeling: Driving and conversing. Journal of Experimental Psychology: Applied.
Castro, S. C., Strayer, D. L., Matzke, D., & Heathcote, A. (2019). Cognitive workload measurement and modeling under divided attention. Journal of Experimental Psychology: Human Perception and Performance, 45(6), 826.
pubmed: 30998070
Charles, R. L., & Nixon, J. (2019). Measuring mental workload using physiological measures: A systematic review. Applied Ergonomics, 74, 221–232.
pubmed: 30487103
doi: 10.1016/j.apergo.2018.08.028
Chen, S. I., Visser, T. A., Huf, S., & Loft, S. (2017). Optimizing the balance between task automation and human manual control in simulated submarine track management. Journal of Experimental Psychology: Applied, 23(3), 240.
pubmed: 28604012
Chhikara, R. (1988). The inverse gaussian distribution: Theory: Methodology, and applications. CRC Press.
Crüwell, S., Stefan, A. M., & Evans, N. J. (2019). Robust standards in cognitive science. Computational Brain & Behavior, 2(3), 255–265.
doi: 10.1007/s42113-019-00049-8
Damaso, K. A., Castro, S. C., Todd, J., Strayer, D. L., Provost, A., Matzke, D., & Heathcote, A. (2021). A cognitive model of response omissions in distraction paradigms. Memory & Cognition, 1–17.
Dinges, D. F., & Powell, J. W. (1985). Microcomputer analyses of performance on a portable, simple visual rt task during sustained operations. Behavior Research Methods, Instruments, & Computers, 17(6), 652–655.
doi: 10.3758/BF03200977
Dolan, C. V., Van der Maas, H. L., & Molenaar, P. (2002). A framework for ml estimation of parameters of (mixtures of) common reaction time distributions given optional truncation or censoring. Behavior Research Methods, Instruments, & Computers, 34(3), 304–323.
doi: 10.3758/BF03195458
Donkin, C., & Brown, S. D. (2018). Response times and decision-making. Stevens’ Handbook Of Experimental Psychology And Cognitive Neuroscience, 349–377.
Dutilh, G., Forstmann, B. U., Vandekerckhove, J., & Wagenmakers, E.-J. (2013). A diffusion model account of age differences in posterror slowing. Psychology and Aging, 28(1), 64.
pubmed: 22946524
doi: 10.1037/a0029875
Eidels, A., Townsend, J. T., Hughes, H. C., & Perry, L. A. (2015). Evaluating perceptual integration: Uniting response-time-and accuracy-based methodologies. Attention, Perception, & Psychophysics, 77, 659–680.
doi: 10.3758/s13414-014-0788-y
Endsley, M. R. (2017). From here to autonomy: Lessons learned from human-automation research. Human Factors, 59(1), 5–27.
pubmed: 28146676
doi: 10.1177/0018720816681350
Evans, N. J., & Wagenmakers, E.-J. (2020). Evidence accumulation models: Current limitations and future directions. The Quantitative Methods for Psychology, 16(2), 73–90. https://doi.org/10.20982/tqmp.16.2.p073
Evans, N. J., & Brown, S. D. (2017). People adopt optimal policies in simple decision-making, after practice and guidance. Psychonomic Bulletin & Review, 24(2), 597–606.
doi: 10.3758/s13423-016-1135-1
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.
pubmed: 31180704
Evans, N. J., Tillman, G., & Wagenmakers, E.-J. (2020). Systematic and random sources of variability in perceptual decision-making: Comment on ratcliff, voskuilen, and mckoon (2018). Psychological Review, 127(5), 932–944.
pubmed: 33074702
doi: 10.1037/rev0000192
Faulkenberry, T. J. (2023). A hierarchical bayesian shifted wald model with censoring. Paper presented at Virtual /ICCM 2023. Via mathpsych.org/presentation/1283. MathPsych.
Faulkenberry, T. J. (2017). A single-boundary accumulator model of response times in an addition verification task. Frontiers in Psychology, 8, 1225.
pubmed: 28769853
pmcid: 5513980
doi: 10.3389/fpsyg.2017.01225
Feigh, K. M., Dorneich, M. C., & Hayes, C. C. (2012). Toward a characterization of adaptive systems: A framework for researchers and system designers. Human Factors, 54(6), 1008–1024.
pubmed: 23397810
doi: 10.1177/0018720812443983
Fink, D. (1997). A compendium of conjugate priors. Citeseer
Folks, J. L., & Chhikara, R. S. (1978). The inverse gaussian distribution and its statistical application-a review. Journal of the Royal Statistical Society: Series B (Methodological), 40(3), 263–275.
Fox, E. L., & Houpt, J. W. (2021). A bayesian model of capacity across trials. Journal of Mathematical Psychology, 105, 102604.
doi: 10.1016/j.jmp.2021.102604
Garrett, P. M., Howard, Z., Houpt, J. W., Landy, D., & Eidels, A. (2019). Comparative estimation systems perform under severely limited workload capacity. Journal of Mathematical Psychology, 92, 102255.
doi: 10.1016/j.jmp.2019.02.006
Glavan, J. J., Fox, E. L., Fifić, M., & Houpt, J. W. (2019). Adaptive design for systems factorial technology experiments. Journal of Mathematical Psychology, 92, 102278.
doi: 10.1016/j.jmp.2019.102278
Gunawan, D., Hawkins, G. E., Kohn, R., Tran, M.-N., & Brown, S. D. (2022). Time-evolving psychological processes over repeated decisions. Psychological Review, 129(3), 438.
pubmed: 35727307
doi: 10.1037/rev0000351
Gunawan, D., Hawkins, G. E., Tran, M.-N., Kohn, R., & Brown, S. (2020). New estimation approaches for the hierarchical linear ballistic accumulator model. Journal of Mathematical Psychology, 96, 102368.
doi: 10.1016/j.jmp.2020.102368
Hancock, P. A., Jagacinski, R. J., Parasuraman, R., Wickens, C. D., Wilson, G. F., & Kaber, D. B. (2013). Human-automation interaction research: Past, present, and future. Ergonomics in Design, 21(2), 9–14.
doi: 10.1177/1064804613477099
Hancock, P. A., & Matthews, G. (2019). Workload and performance: Associations, insensitivities, and dissociations. Human Factors, 61(3), 374–392.
pubmed: 30521400
doi: 10.1177/0018720818809590
Hawkins, G. E., Forstmann, B. U., Wagenmakers, E.-J., Ratcliff, R., & Brown, S. D. (2015). Revisiting the evidence for collapsing boundaries and urgency signals in perceptual decision-making. Journal of Neuroscience, 35(6), 2476–2484.
pubmed: 25673842
doi: 10.1523/JNEUROSCI.2410-14.2015
Heathcote, A. (2004). Fitting wald and ex-wald distributions to response time data: An example using functions for the s-plus package. Behavior Research Methods, Instruments, & Computers, 36(4), 678–694.
doi: 10.3758/BF03206550
Heathcote, A., Lin, Y.-S., Reynolds, A., Strickland, L., Gretton, M., & Matzke, D. (2019). Dynamic models of choice. Behavior Research Methods, 51(2), 961–985.
pubmed: 29959755
doi: 10.3758/s13428-018-1067-y
Howard, Z. L., Belevski, B., Eidels, A., & Dennis, S. (2020). What do cows drink? a systems factorial technology account of processing architecture in memory intersection problems. Cognition, 202, 104294.
pubmed: 32504858
doi: 10.1016/j.cognition.2020.104294
Howard, Z. L., Evans, N. J., Innes, R. J., Brown, S. D., & Eidels, A. (2020). How is multi-tasking different from increased difficulty? Psychonomic Bulletin & Review, 27(5), 937–951.
doi: 10.3758/s13423-020-01741-8
Howard, Z. L., Garrett, P., Little, D. R., Townsend, J. T., & Eidels, A. (2021). A show about nothing: No-signal processes in systems factorial technology. Psychological Review, 128(1), 187.
pubmed: 32881552
doi: 10.1037/rev0000256
Howard, Z. L., Innes, R., Eidels, A., & Loft, S. (2021). Using past and present indicators of human workload to explain variance in human performance. Psychonomic Bulletin & Review, 28(6), 1923–1932.
doi: 10.3758/s13423-021-01961-6
Huang, L.-F. (2016). The nakagami and its related distributions. WSEAS Trans. Math, 15(44), 477–485.
Innes, R. J., Evans, N. J., Howard, Z. L., Eidels, A., & Brown, S. D. (2021). A broader application of the detection response task to cognitive tasks and online environments. Human Factors, 63(5), 896–909.
pubmed: 32749155
doi: 10.1177/0018720820936800
Innes, R. J., Howard, Z. L., Thorpe, A., Eidels, A., & Brown, S. D. (2021). The effects of increased visual information on cognitive workload in a helicopter simulator. Human Factors, 63(5), 788–803.
pubmed: 32783536
doi: 10.1177/0018720820945409
International Standards Organization. (2016). Road vehicle-transport information and control system-detection response task (drt) for assessing attentional effects of cognitive load in driving (ISO 17488). Switzerland: Author Geneva.
Jones, M., & Dzhafarov, E. N. (2014). Unfalsifiability and mutual translatability of major modeling schemes for choice reaction time. Psychological Review, 121(1), 1.
pubmed: 24079307
doi: 10.1037/a0034190
Kaber, D. B. (2018). Issues in human-automation interaction modeling: Presumptive aspects of frameworks of types and levels of automation. Journal of Cognitive Engineering and Decision Making, 12(1), 7–24.
doi: 10.1177/1555343417737203
Kutilek, P., Volf, P., Viteckova, S., Smrcka, P., Krivanek, V., Lhotska, L., & Stefek, A. (2017). Wearable systems and methods for monitoring psychological and physical condition of soldiers. Advances in Military Technology, 12(2), 259–280.
doi: 10.3849/aimt.01186
Leite, F. P., & Ratcliff, R. (2010). Modeling reaction time and accuracy of multiple-alternative decisions. Attention, Perception, & Psychophysics, 72(1), 246–273.
doi: 10.3758/APP.72.1.246
Lewandowsky, S., & Farrell, S. (2010). Computational modeling in cognition: Principles and practice. SAGE publications
Link, S., & Heath, R. (1975). A sequential theory of psychological discrimination. Psychometrika, 40(1), 77–105.
doi: 10.1007/BF02291481
Loft, S., Sanderson, P., Neal, A., & Mooij, M. (2007). Modeling and predicting mental workload in en route air traffic control: Critical review and broader implications. Human Factors, 49(3), 376–399.
pubmed: 17552304
doi: 10.1518/001872007X197017
Logan, G. D. (1992). Shapes of reaction-time distributions and shapes of learning curves: A test of the instance theory of automaticity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 18(5), 883.
pubmed: 1402715
Luce, R. D. (1986). Response times: Their role in inferring elementary mental organization. Oxford University Press on Demand.
Luce, R. D., & Green, D. M. (1972). A neural timing theory for response times and the psychophysics of intensity. Psychological Review, 79(1), 14.
pubmed: 5008127
doi: 10.1037/h0031867
Matzke, D., & Wagenmakers, E.-J. (2009). Psychological interpretation of the ex-gaussian and shifted wald parameters: A diffusion model analysis. Psychonomic Bulletin & Review, 16(5), 798–817.
doi: 10.3758/PBR.16.5.798
Moss, J. (2021). Nakagami: Functions for the nakagami distribution. R package version 1.1.0. Retrieved from https://CRAN.R-project.org/package=nakagami
Münsterer, T., Schafhitzel, T., Strobel, M., Völschow, P., Klasen, S., & Eisenkeil, F. (2014). Sensor-enhanced 3d conformal cueing for safe and reliable hc operation in dve in all flight phases. In Degraded visual environments: Enhanced, synthetic, and external vision solutions 2014(Vol. 9087, pp. 145–155). SPIE.
Nakagami, M. (1960). The m-distribution—a general formula of intensity distribution of rapid fading. In Statistical methods in radio wave propagation(pp. 3–36)). Elsevier.
Palmer, E. M., Horowitz, T. S., Torralba, A., & Wolfe, J. M. (2011). What are the shapes of response time distributions in visual search? Journal of Experimental Psychology: Human Perception and Performance, 37(1), 58.
pubmed: 21090905
Ratcliff, R. (2013). Parameter variability and distributional assumptions in the diffusion model. Psychological Review, 120(1), 281.
pubmed: 23148742
doi: 10.1037/a0030775
Ratcliff, R., & Van Dongen, H. P. (2011). Diffusion model for one-choice reaction-time tasks and the cognitive effects of sleep deprivation. Proceedings of the National Academy of Sciences, 108(27), 11285–11290.
doi: 10.1073/pnas.1100483108
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.
pubmed: 26952739
pmcid: 4928591
doi: 10.1016/j.tics.2016.01.007
Ratcliff, R., & Strayer, D. (2014). Modeling simple driving tasks with a one-boundary diffusion model. Psychonomic Bulletin & Review, 21(3), 577–589.
doi: 10.3758/s13423-013-0541-x
Ratcliff, R., & Tuerlinckx, F. (2002). Estimating parameters of the diffusion model: Approaches to dealing with contaminant reaction times and parameter variability. Psychonomic Bulletin & Review, 9(3), 438–481.
doi: 10.3758/BF03196302
Rouder, J. N., Province, J. M., Morey, R. D., Gomez, P., & Heathcote, A. (2015). The lognormal race: A cognitive-process model of choice and latency with desirable psychometric properties. Psychometrika, 80(2), 491–513.
pubmed: 24522340
doi: 10.1007/s11336-013-9396-3
Rouse, W. B. (1981). Experimental studies and mathematical models of human problem solving performance in fault diagnosis tasks. In Human detection and diagnosis of system failures (pp. 199–216). Springer.
Rouse, W. B. (1988). Adaptive aiding for human/computer control. Human Factors, 30(4), 431–443.
doi: 10.1177/001872088803000405
Schwarz, W. (2001). The ex-wald distribution as a descriptive model of response times. Behavior Research Methods, Instruments, & Computers, 33(4), 457–469.
doi: 10.3758/BF03195403
Singmann, H., Brown, S., Gretton, M., Heathcote, A., Voss, A., Voss, J., & Terry, A. (2016). Rtdists: Response time distributions. R package version 0.4-9. http://CRAN.R-project.org/package=rtdists
Smith, P. L. (1995). Psychophysically principled models of visual simple reaction time. Psychological Review, 102(3), 567.
doi: 10.1037/0033-295X.102.3.567
Smith, P. L., & Ratcliff, R. (2004). Psychology and neurobiology of simple decisions. Trends in Neurosciences, 27(3), 161–168.
pubmed: 15036882
doi: 10.1016/j.tins.2004.01.006
Sperandio, J. (1971). Variation of operator’s strategies and regulating effects on workload. Ergonomics, 14(5), 571–577.
doi: 10.1080/00140137108931277
Spieler, D. H., Balota, D. A., & Faust, M. E. (2000). Levels of selective attention revealed through analyses of response time distributions. Journal of Experimental Psychology: Human Perception and Performance, 26(2), 506.
pubmed: 10811160
Steingroever, H., Wabersich, D., & Wagenmakers, E.-J. (2021). Modeling across-trial variability in the wald drift rate parameter. Behavior Research Methods, 53(3), 1060–1076.
pubmed: 32948979
doi: 10.3758/s13428-020-01448-7
Stojmenova, K., & Sodnik, J. (2018). Detection-response task-uses and limitations. Sensors, 18(2), 594.
pubmed: 29443949
Strayer, D. L., Castro, S. C., Turrill, J., & Cooper, J. M. (2022). The persistence of distraction: The hidden costs of intermittent multitasking. Journal of Experimental Psychology: Applied.
Strayer, D. L., Cooper, J. M., Turrill, J., Coleman, J., Medeiros-Ward, N., & Biondi, F. (2013). Measuring cognitive distraction in the automobile.
Strayer, D. L., Cooper, J. M., Turrill, J., Coleman, J. R., & Hopman, R. J. (2017). The smartphone and the driver’s cognitive workload: A comparison of apple, google, and microsoft’s intelligent personal assistants. Canadian Journal of Experimental Psychology/Revue Canadienne de Psychologie Expérimentale, 71(2), 93.
pubmed: 28604047
doi: 10.1037/cep0000104
Strayer, D. L., Turrill, J., Cooper, J. M., Coleman, J. R., Medeiros-Ward, N., & Biondi, F. (2015). Assessing cognitive distraction in the automobile. Human Factors, 57(8), 1300–1324.
pubmed: 26534847
doi: 10.1177/0018720815575149
Strickland, L., Elliott, D., Wilson, M. D., Loft, S., Neal, A., & Heathcote, A. (2019). Prospective memory in the red zone: Cognitive control and capacity sharing in a complex, multi-stimulus task. Journal of Experimental Psychology: Applied, 25(4), 695.
pubmed: 30985156
Terry, A., Marley, A., Barnwal, A., Wagenmakers, E.-J., Heathcote, A., & Brown, S. D. (2015). Generalising the drift rate distribution for linear ballistic accumulators. Journal of Mathematical Psychology, 68, 49–58.
doi: 10.1016/j.jmp.2015.09.002
Tillman, G., Strayer, D., Eidels, A., & Heathcote, A. (2017). Modeling cognitive load effects of conversation between a passenger and driver. Attention, Perception, & Psychophysics, 79(6), 1795–1803.
doi: 10.3758/s13414-017-1337-2
Tillman, G., Van Zandt, T., & Logan, G. D. (2020). Sequential sampling models without random between-trial variability: The racing diffusion model of speeded decision making. Psychonomic Bulletin & Review, 27(5), 911–936.
Townsend, J. T. (1990). Truth and consequences of ordinal differences in statistical distributions: Toward a theory of hierarchical inference. Psychological Bulletin, 108(3), 551.
pubmed: 2270240
doi: 10.1037/0033-2909.108.3.551
Townsend, J. T., & Eidels, A. (2011). Workload capacity spaces: A unified methodology for response time measures of efficiency as workload is varied. Psychonomic Bulletin & Review, 18(4), 659–681.
doi: 10.3758/s13423-011-0106-9
Trueblood, J. S., Holmes, W. R., Seegmiller, A. C., Douds, J., Compton, M., Szentirmai, E., & Eichbaum, Q. (2018). The impact of speed and bias on the cognitive processes of experts and novices in medical image decision-making. Cognitive Research: Principles and Implications, 3(1), 1–14.
Tweedie, M. C. (1957a). Statistical properties of inverse gaussian distributions. i. The Annals of Mathematical Statistics, 28(2), 362–377.
Tweedie, M. C. (1957b). Statistical properties of inverse gaussian distributions. ii. The Annals of Mathematical Statistics, 696–705.
Ulrich, R., & Miller, J. (1994). Effects of truncation on reaction time analysis. Journal of Experimental Psychology: General, 123(1), 34.
pubmed: 8138779
doi: 10.1037/0096-3445.123.1.34
Usher, M., & McClelland, J. L. (2001). The time course of perceptual choice: The leaky, competing accumulator model. Psychological Review, 108(3), 550.
pubmed: 11488378
doi: 10.1037/0033-295X.108.3.550
van Maanen, L., Brown, S. D., Eichele, T., Wagenmakers, E.-J., Ho, T., Serences, J., & Forstmann, B. U. (2011). Neural correlates of trial-to-trial fluctuations in response caution. Journal of Neuroscience, 31(48), 17488–17495.
pubmed: 22131410
doi: 10.1523/JNEUROSCI.2924-11.2011
Van Zandt, T. (2002). Analysis of response time distributions. Stevens’ Handbook of Experimental Psychology, 4, 461–516.
Van Zandt, T. (2000). How to fit a response time distribution. Psychonomic Bulletin & Review, 7(3), 424–465.
Vandekerckhove, J., Tuerlinckx, F., & Lee, M. D. (2011). Hierarchical diffusion models for two-choice response times. Psychological Methods, 16(1), 44.
pubmed: 21299302
doi: 10.1037/a0021765
Wagenmakers, E.-J., van der Maas, H. L., Dolan, C. V., & Grasman, R. P. (2008). Ez does it! extensions of the ez-diffusion model. Psychonomic Bulletin & Review, 15(6), 1229–1235.
doi: 10.3758/PBR.15.6.1229
Wagenmakers, E.-J., Van Der Maas, H. L., & Grasman, R. P. (2007). An ez-diffusion model for response time and accuracy. Psychonomic Bulletin & Review, 14(1), 3–22.
doi: 10.3758/BF03194023
Wickens, C. D., Hollands, J. G., Banbury, S., & Parasuraman, R. (2015). Mental workload, stress, and individual differences: Cognitive and neuroergonomic perspectives. Engineering Psychology and Human Performance (International Edition), 346–376.
Wilson, G. F., & Eggemeier, F. T. (2020). Psychophysiological assessment of workload in multi-task environments. Multiple-task Performance, 329–360
Yacoub, M. D., Bautista, J. V., & de Rezende Guedes, L. G. (1999). On higher order statistics of the nakagami-m distribution. IEEE Transactions on Vehicular Technology, 48(3), 790–794.
doi: 10.1109/25.764995
Young, R. A., Hsieh, L., & Seaman, S. (2013). The tactile detection response task: Preliminary validation for measuring the attentional effects of cognitive load. In Driving assesment conference (Vol. 7). University of Iowa.