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Annals of Data Science

27-02-2024

A Joint Cognitive Latent Variable Model for Binary Decision-making Tasks and Reaction Time Outcomes

Authors: Mahdi Mollakazemiha, Ehsan Bahrami Samani

Published in: Annals of Data Science

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Abstract

Traditionally, in cognitive modeling for binary decision-making tasks, stochastic differential equations, particularly a family of diffusion decision models, are applied. These models suffer from difficulties in parameter estimation and forecasting due to the non-existence of analytical solutions for the differential equations. In this paper, we introduce a joint latent variable model for binary decision-making tasks and reaction time outcomes. Additionally, accelerated Failure Time models can be used for the analysis of reaction time to estimate the effects of covariates on acceleration/deceleration of the survival time. A full likelihood-based approach is used to obtain maximum likelihood estimates of the parameters of the model.To illustrate the utility of the proposed models, a simulation study and real data are analyzed.

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Shi Y (2022) Advances in big data analytics: theory, algorithm and practice. Springer, SingaporeCrossRef

Ratcliff R, Smith PL (2004) A comparison of sequential sampling models for two-choice reaction time. Psychol Rev 111(2):333–367. https://doi.org/10.1037/0033-295X.111.2.333CrossRefPubMedPubMedCentral

Ratcliff R (1978) A theory of memory retrieval. Psychol Rev 85(2):59–108CrossRef

Ratcliff R (1981) A theory of order relations in perceptual matching. Psychol Rev 88(6):552–572. https://doi.org/10.1037/0033-295X.88.6.552CrossRef

Ratcliff R, Van Zandt T, McKoon G (1999) Connectionist and diffusion models of reaction time. Psychol Rev 106(2):261–300. https://doi.org/10.1037/0033-295x.106.2.261CrossRefPubMed

Ratcliff R, Rouder JN (1998) Modeling response times for two-choice decisions. Psychol Sci 9(5):347–356. https://doi.org/10.1111/1467-9280.00067CrossRef

Ratcliff R, Tuerlinckx F (2002) Estimating parameters of the diffusion model: approaches to dealing with contaminant reaction times and parameter variability. Psychon Bull Rev 9(3):438–481. https://doi.org/10.3758/BF03196302CrossRefPubMedPubMedCentral

Nunez MD, Vandekerckhove J, Srinivasan R (2017) How attention influences perceptual decision making: single-trial EEG correlates of drift-diffusion model parameters. J Math Psychol 76(Pt B):117–130. https://doi.org/10.1016/j.jmp.2016.03.003MathSciNetCrossRefPubMed

Tien JM (2017) Internet of things, real-time decision making, and artificial intelligence. Ann Data Sci 4(2):149–178CrossRef

10.

Ratcliff R, Thapar A, McKoon G (2001) The effects of aging on reaction time in a signal detection task. Psychol Aging 16(2):323–341. https://doi.org/10.1037/0882-7974.16.2.323CrossRefPubMed

11.

Ratcliff R, Thapar A, McKoon G (2003) A diffusion model analysis of the effects of aging on brightness discrimination. Percept Psychophys. https://doi.org/10.3758/BF03194580CrossRefPubMedPubMedCentral

12.

Rinkenauer G, Osman A, Ulrich R, Muller-Gethmann H, Mattes S (2004) On the locus of speed-accuracy trade-off in reaction time: inferences from the lateralized readiness potential. J Exp Psychol Gen 133(2):261–282. https://doi.org/10.1037/0096-3445.133.2.261CrossRefPubMed

13.

Rae B, Heathcote A, Donkin C, Averell L, Brown S (2014) The hare and the tortoise: emphasizing speed can change the evidence used to make decisions. J Exp Psychol Learn Mem Cogn 40(5):1226–1243. https://doi.org/10.1037/a0036801CrossRefPubMed

14.

Starns JJ, Ratcliff R, McKoon G (2012) Evaluating the unequal-variance and dual-process explanations of zROC slopes with response time data and the diffusion model. Cogn Psychol 64(1):1–34. https://doi.org/10.1016/j.cogpsych.2011.10.002CrossRefPubMed

15.

Ratcliff R, Smith PL, McKoon G (2015) Modeling regularities in response time and accuracy data with the diffusion model. Curr Dir Psychol Sci 24(6):458–470. https://doi.org/10.1177/0963721415596228CrossRefPubMedPubMedCentral

16.

Shi Y, Tian YJ, Kou G, Peng Y, Li JP (2011) Optimization based data mining: theory and applications. Springer, BerlinCrossRef

17.

Voss A, Voss J (2008) A fast numerical algorithm for the estimation of diffusion model parameters. J Math Psychol 52(1):1–9. https://doi.org/10.1016/j.jmp.2007.09.005MathSciNetCrossRef

18.

Ratcliff R (1985) Theoretical interpretations of the speed and accuracy of positive and negative responses. Psychol Rev 92(2):212–225. https://doi.org/10.1037/0033-295X.92.2.212CrossRefPubMed

19.

Dunovan KE, Tremel JJ, Wheeler ME (2014) Prior probability and feature predictability interactively bias perceptual decisions. Neuropsychologia 61:210–221. https://doi.org/10.1016/j.neuropsychologia.2014.06.024CrossRefPubMedPubMedCentral

20.

Edwards W (1965) Optimal strategies for seeking information: models for statistics, choice reaction times, and human information processing. J Math Psychol 2(2):312–329. https://doi.org/10.1016/0022-2496(65)90007-6CrossRef

21.

Hanks TD, Mazurek ME, Kiani R, Hopp E, Shadlen MN (2011) Elapsed decision time affects the weighting of prior probability in a perceptual decision task. J Neurosci 31(17):6339–6352. https://doi.org/10.1523/JNEUROSCI.5613-10.2011CrossRefPubMedPubMedCentral

22.

Ratcliff R (2013) Parameter variability and distributional assumptions in the diffusion model. Psychol Rev 120(1):281–292. https://doi.org/10.1037/a0030775CrossRefPubMed

23.

Brown SD, Heathcote A (2008) The simplest complete model of choice response time: linear ballistic accumulation. Cogn Psychol 57(3):153–178. https://doi.org/10.1016/j.cogpsych.2007.12.002CrossRefPubMed

24.

Corbett AT, Wickelgren WA (1978) Semantic memory retrieval: analysis by speed accuracy tradeoff functions. Q J Exp Psychol 30(1):1–15. https://doi.org/10.1080/14640747808400648CrossRefPubMed

25.

Reed AV (1973) Speed-accuracy trade-off in recognition memory. Science (New York, N.Y.) 181(4099):574–576. https://doi.org/10.1126/science.181.4099.574ADSCrossRefPubMed

26.

Schouten JF, Bekker JAM (1967) Reaction time and accuracy. Acta Physiol (Oxf) 27:143–153. https://doi.org/10.1016/0001-6918(67)90054-6CrossRef

27.

Wickelgren WA (1977) Speed-accuracy tradeoff and information processing dynamics. Acta Physiol (Oxf) 41(1):67–85. https://doi.org/10.1016/0001-6918(77)90012-9CrossRef

28.

Bennur S, Gold J (2011) Distinct representations of a perceptual decision and the associated oculomotor plan in the monkey lateral intraparietal area. J Neurosci. https://doi.org/10.1523/JNEUROSCI.4417-10.2011CrossRefPubMedPubMedCentral

29.

Brunton BW, Botvinick MM, Brody CD (2013) Rats and humans can optimally accumulate evidence for decision-making. Science (New York, N.Y.) 340(6128):95–98. https://doi.org/10.1126/science.1233912ADSCrossRefPubMed

30.

Rao R (2010) Decision making under uncertainty: a neural model based on partially observable markov decision processes. Front Comput Neurosci 4:146. https://doi.org/10.3389/fncom.2010.00146CrossRefPubMedPubMedCentral

31.

Voss A, Lerche V, Mertens U, Voss J (2019) Sequential sampling models with variable boundaries and non-normal noise: a comparison of six models. Psychon Bull Rev 26(3):813–832. https://doi.org/10.3758/s13423-018-1560-4CrossRefPubMedPubMedCentral

32.

Hawkins GE, Forstmann BU, Wagenmakers E-J, Ratcliff R, Brown SD (2015) Revisiting the evidence for collapsing boundaries and urgency signals in perceptual decision-making. J Neurosci Off J Soc Neurosci 35(6):2476–2484. https://doi.org/10.1523/JNEUROSCI.2410-14.2015CrossRef

33.

Wieschen EM, Voss A, Radev S (2020) Jumping to conclusion? A lévy flight model of decision making. Quant Methods Psychol 16(2):120–132. https://doi.org/10.20982/tqmp.16.2.p120CrossRef

34.

Olkin I, Tate RF (1961) Multivariate correlation models with mixed discrete and continuous variables. Ann Math Stat 32(2):448–465. https://doi.org/10.1214/aoms/1177705052MathSciNetCrossRef

35.

Heckman JJ (1978) Dummy endogenous variables in a simultaneous equation system. Econometrica 46(4):931–959. https://doi.org/10.2307/1909757MathSciNetCrossRef

36.

Cox DR, Wermuth N (1992) Response models for mixed binary and quantitative variables. Biometrika 79(3):441–461. https://doi.org/10.2307/2336778MathSciNetCrossRef

37.

Catalano PJ, Ryan LM (1992) Bivariate latent variable models for clustered discrete and continuous outcomes. J Am Stat Assoc 87(419):651–658. https://doi.org/10.2307/2290200CrossRef

38.

Olson DL, Shi Y (2007) Introduction to business data mining. McGraw-Hill/Irwin, New York

39.

Joe H (1995) Approximations to multivariate normal rectangle probabilities based on conditional expectations. J Am Stat Assoc 90(431):957–964. https://doi.org/10.1080/01621459.1995.10476596MathSciNetCrossRef

40.

Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7(4):308–313. https://doi.org/10.1093/comjnl/7.4.308MathSciNetCrossRef

41.

Forstmann BU, Dutilh G, Brown S, Neumann J, von Cramon DY, Ridderinkhof KR, Wagenmakers E-J (2008) Striatum and pre-SMA facilitate decision-making under time pressure. Proc Natl Acad Sci 105(45):17538–17542. https://doi.org/10.1073/pnas.0805903105ADSCrossRefPubMedPubMedCentral

Title: A Joint Cognitive Latent Variable Model for Binary Decision-making Tasks and Reaction Time Outcomes
Authors: Mahdi Mollakazemiha
Ehsan Bahrami Samani
Publication date: 27-02-2024
Publisher: Springer Berlin Heidelberg
Published in: Annals of Data Science
Print ISSN: 2198-5804
Electronic ISSN: 2198-5812
DOI: https://doi.org/10.1007/s40745-024-00519-2

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