Ex-Gaussian vector metric and similarity index applied to reaction time analysis

Abstract

In psychology and cognitive neuroscience, reaction time (RT) series and their ex-Gaussian distributions are commonly used as scalar quantities to explore the time course of attentional processes. However, we propose that such attentional processes can also be analyzed using an “ex-Gaussian vector”, defined by successive triads of ex-Gaussian sigma, tau, and mu parameters from RT series. This geometrical object may help characterize interindividual differences between congruent and incongruent stimuli in the attentional Stroop task within a group of participants. To test these hypotheses, we calculated the similarity index of these geometrical objects in young adults without detectable neurological disorders. Our findings show that during two weeks of continuous Stroop task application, each participant displayed distinct ex-Gaussian RT vectors in a Cartesian 3D plot. Furthermore, our study found that the similarity index between ex-Gaussian RT vectors was significantly higher for incongruent stimuli than for congruent stimuli.

Keywords

ex-Gaussian parameters attention cognitive processes Stroop vectors psychophysical vectors congruent incongruent

Get full access to this article

View all access options for this article.

References

Adamo, N., Baumeister, S., Hohmann, S., Wolf, I., Holz, N., Boecker, R., Laucht, M., Banaschewski, T., & Brandeis, D. (2015). Frequency-specific coupling between trial-to-trial fluctuations of neural responses and response-time variability. Journal of Neural Transmission (Vienna, Austria : 1996), 122(8), 1197–1202. https://doi.org/10.1007/s00702-015-1382-8

Aguilar-Lacasaña

Vilor-Tejedor

Jansen

P. R.

López-Vicente

Bustamante

Burgaleta

Sunyer

& Alemany

(2022). Polygenic risk for ADHD and ASD and their relation with cognitive measures in school children. Psychological Medicine, 52(7), 1356–1364. https://doi.org/10.1017/s0033291720003189

Algom

Fitousi

Chajut

(2022). Can the Stroop effect serve as the gold standard of conflict monitoring and control? A conceptual critique. Memory & Cognition, 50(5), 883–897. https://doi.org/10.3758/s13421-021-01251-5

Baldwin, H., Radua, J., Antoniades, M., Haas, S. S., Frangou, S., Agartz, I., Allen, P., Andreassen, O. A., Atkinson, K., Bachman, P., Baeza, I., Bartholomeusz, C. F., Chee, M. W. L., Colibazzi, T., Cooper, R. E., Corcoran, C. M., Cropley, V. L., Ebdrup, B. H., Fortea, A., … ENIGMA Clinical High Risk for Psychosis Working Group. (2022). Neuroanatomical heterogeneity and homogeneity in individuals at clinical high risk for psychosis. Translational Psychiatry, 12(1), 297. https://doi.org/10.1038/s41398-022-02057-y

Balota

D. A.

Spieler

D. H.

(1999). Word frequency, repetition, and lexicality effects in word recognition tasks: Beyond measures of central tendency. Journal of Experimental Psychology: General, 128(1), 32–55. https://doi.org/10.1037//0096-3445.128.1.32

Banich

M. T.

(2019). The Stroop effect occurs at multiple points along a cascade of control: Evidence from cognitive neuroscience approaches. Frontiers in Psychology, 10, 2164. https://doi.org/10.3389/fpsyg.2019.02164

Barca

Candidi

Lancia

G. L.

Maglianella

Pezzulo

(2023). Mapping the mental space of emotional concepts through kinematic measures of decision uncertainty. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 378(1870). https://doi.org/10.1098/rstb.2021.0367

Bella-Fernández

Martin-Moratinos

Wang

Blasco-Fontecilla

. (2024). Differences in ex-Gaussian parameters from response time distributions between individuals with and without attention deficit/hyperactivity disorder: A meta-analysis. Neuropsychology Review, 34(1), 320–337. https://doi.org/10.1007/s11065-023-09587-2

Bender

Banaschewski

Roessner

Klein

Rietschel

Feige

Brandeis

& Laucht

(2015). Variability of single trial brain activation predicts fluctuations in reaction time. Biological Psychology, 106, 50–60. https://doi.org/10.1016/j.biopsycho.2015.01.013

10.

Bouts

M. J.

Tiebosch

I. A.

Rudrapatna

U. S.

van der Toorn

Dijkhuizen

R. M.

(2017). Prediction of hemorrhagic transformation after experimental ischemic stroke using MRI-based algorithms. Journal of Cerebral Blood Flow and Metabolism: Official Journal of the International Society of Cerebral Blood Flow and Metabolism, 37(8), 3065–3076. https://doi.org/10.1177/0271678 × 16683692

11.

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C,… Amodei, D. (2020). Language models are few-shot learners. http://arxiv.org/abs/2005.14165

12.

Burgess

J. L.

Bradley

A. J.

Anderson

K. N.

Gallagher

McAllister-Williams

R. H.

(2022). The relationship between physical activity, BMI, circadian rhythm, and sleep with cognition in bipolar disorder. Psychological Medicine, 52(3), 467–475. https://doi.org/10.1017/s003329172000210x

13.

Cain

S. W.

Silva

E. J.

Chang

A. M.

Ronda

J. M.

Duffy

J. F

. (2011). One night of sleep deprivation affects reaction time, but not interference or facilitation in a Stroop task. Brain and Cognition, 76(1), 37–42. https://doi.org/10.1016/j. bandc.2011.03.005

14.

Chang

C.-C.

Lee

Y.-H.

Lin

C. J.

Liu

B.-S.

Shih

Y.-C.

(2012). Visual assessment of camouflaged targets with different background similarities. Perceptual and Motor Skills, 114(2), 527–541. https://doi.org/10.2466/24.pms.114.2.527-541

15.

Chiang

H.-L.

Lin

H.-Y.

Tseng

W.-Y. I.

Hwang-Gu

S.-L.

Shang

C.-Y.

Gau

S. S.-F

. (2022). Neural substrates underpinning intra-individual variability in children with ADHD: A voxel-based morphometry study. Taiwan Yi Zhi [Journal of the Formosan Medical Association], 121(2), 546–556. https://doi.org/10.1016/j.jfma.2021.06.003

16.

Cousineau

Brown

Heathcote

. (2004). Fitting distributions using maximum likelihood: Methods and packages. Behavior Research Methods, Instruments, & Computers: A Journal of the Psychonomic Society, Inc., 36(4), 742–756. https://doi.org/10.3758/bf03206555

17.

Dawson

M. R. W.

(1988). Fitting the ex-Gaussian equation to reaction time distributions. Behavior Research Methods, Instruments, & Computers: A Journal of the Psychonomic Society, Inc., 20(1), 54–57. https://doi.org/10.3758/bf03202603

18.

de Leeuw

J. R.

(2015). Jspsych: A JavaScript library for creating behavioral experiments in a web browser. Behavior Research Methods, 47(1), 1–12. https://doi.org/10.3758/s13428-014-0458-y

19.

Doucet

G. E.

Lin

Glahn

D. C.

Calhoun

V. D.

Turner

& Frangou

(2020). Personalized estimates of morphometric similarity in bipolar disorder and schizophrenia. Npj Schizophrenia, 6(1). https://doi.org/10.1038/s41537-020-00128-x

20.

Fish

Toumaian

Pappa

Davies

T. J.

Tanti

Saville

C. W. N.

Theleritis

Economou

Klein

& Smyrnis

(2018). Modelling reaction time distribution of fast decision tasks in schizophrenia: Evidence for novel candidate endophenotypes. Psychiatry Research, 269, 212–220. https://doi.org/10.1016/j.psychres.2018.08.067

21.

Fitousi

(2020). Linking the ex-Gaussian parameters to cognitive stages: Insights from the linear ballistic accumulator (LBA) model. The Quantitative Methods for Psychology, 16(2), 91–106. https://doi.org/10.20982/tqmp.16.2.p091

22.

Gallagher, P., Nilsson, J., Finkelmeyer, A., Goshawk, M., Macritchie, K. A., Lloyd, A. J., Thompson, J. M., Porter, R. J., Young, A. H., Ferrier, I. N., McAllister-Williams, R. H., & Watson, S. (2015). Neurocognitive intra-individual variability in mood disorders: Effects on attentional response time distributions. Psychological Medicine, 45(14), 2985–2997. https://doi.org/10.1017/s0033291715000926

23.

Galloway-Long

Huang-Pollock

Neely

(2022). Ahead of the (ROC) curve: A statistical approach to utilizing ex-Gaussian parameters of reaction time in diagnosing ADHD across three developmental periods. Journal of the International Neuropsychological Society: JINS, 28(8), 821–834. https://doi.org/10.1017/s1355617721000990

24.

Garcia-Marques

Fernandes

A. C.

(2024). Meta-analysis of social presence effects on Stroop task performance. Psychological Reports. https://doi.org/10.1177/00332941241227150

25.

Gebuis

Kenemans

J. L.

de Haan

E. H. F.

van der Smagt

M. J.

(2010). Conflict processing of symbolic and non-symbolic numerosity. Neuropsychologia, 48(2), 394–401. https://doi.org/10.1016/j.neuropsychologia.2009.09.027

26.

Gerstorf

Windsor

T. D.

Hoppmann

C. A.

Butterworth

(2013). Longitudinal change in spousal similarities in mental health: Between-couple and within-couple perspectives. Psychology and Aging, 28(2), 540–554. https://doi.org/10.1037/a0032902

27.

Ging-Jehli

N. R.

Ratcliff

Arnold

L. E.

(2021). Improving neurocognitive testing using computational psychiatry—A systematic review for ADHD. Psychological Bulletin, 147(2), 169–231. https://doi.org/10.1037/bul0000319

28.

Harrison, B. J., Shaw, M., Yücel, M., Purcell, R., Brewer, W. J., Strother, S. C., Egan, G. F., Olver, J. S., Nathan, P. J., & Pantelis, C. (2005). Functional connectivity during Stroop task performance. NeuroImage, 24(1), 181–191. https://doi.org/10.1016/j.neuroimage.2004.08.033

29.

Heathcote

Popiel

S. J.

Mewhort

D. J.

(1991). Analysis of response time distributions: An example using the Stroop task. Psychological Bulletin, 109(2), 340–347. https://doi.org/10.1037/0033-2909.109.2.340

30.

Heidlmayr

Kihlstedt

Isel

(2020). A review on the electroencephalography markers of Stroop executive control processes. Brain and Cognition, 146(105637), 105637. https://doi.org/10.1016/j.bandc.2020.105637

31.

Hervey, A. S., Epstein, J. N., Curry, J. F., Tonev, S., Eugene Arnold, L., Keith Conners, C., Hinshaw, S. P., Swanson, J. M., & Hechtman, L. (2006). Reaction time distribution analysis of neuropsychological performance in an ADHD sample. Child Neuropsychology: A Journal on Normal and Abnormal Development in Childhood and Adolescence, 12(2), 125–140. https://doi.org/10.1080/09297040500499081

32.

Hohle

R. H.

(1965). Inferred components of reaction times as functions of foreperiod duration. Journal of Experimental Psychology, 69(4), 382–386. https://doi.org/10.1037/h0021740

33.

Huebner

G. M.

Gegenfurtner

K. R.

(2012). Conceptual and visual features contribute to visual memory for natural images. PloS One, 7(6), e37575. https://doi.org/10.1371/journal.pone.0037575

34.

Hwang-Gu

S.L.

Gau

S. S.F.

Tzang

S.W.

& Hsu

W.Y

. (2013). The ex-Gaussian distribution of reaction times in adolescents with attention-deficit/hyperactivity disorder. Research in Developmental Disabilities, 34(11), 3709–3719. https://doi.org/10.1016/j.ridd.2013.07.025

35.

Jalalvandi

., ZahediNiya

., Kargar

., Karimi

S. A

., Sharini

., & Goodarzi

. (2020). Brain functional mechanisms in attentional processing following modified conflict Stroop task. Journal of Biomedical Physics & Engineering, 10(4), 493. https://doi.org/10.31661/jbpe.v0i0.2003-1084

36.

Janssen, J., Díaz-Caneja, C. M., Alloza, C., Schippers, A., de Hoyos, L., Santonja, J., Gordaliza, P. M., Buimer, E. E. L., van Haren, N. E. M., Cahn, W., Arango, C., Kahn, R. S., Hulshoff Pol, H. E., & Schnack, H. G. (2021). Dissimilarity in sulcal width patterns in the cortex can be used to identify patients with schizophrenia with extreme deficits in cognitive performance. Schizophrenia Bulletin, 47(2), 552–561. https://doi.org/10.1093/schbul/sbaa131

37.

Johansson

(2023) exgfit—Fit exGaussian distribution to data. https://www.mathworks.com/matlabcentral/fileexchange/70225-exgfit-fit-exgaussian-distribution-to-data

38.

Karalunas

S. L.

Geurts

H. M.

Konrad

Bender

Nigg

J. T.

(2014). Annual research review: Reaction time variability in ADHD and autism spectrum disorders: Measurement and mechanisms of a proposed trans-diagnostic phenotype. Journal of Child Psychology and Psychiatry, 55(6), 685–710. https://doi.org/10.1111/jcpp.12217

39.

Kinney

A. R.

Eakman

A. M.

Graham

J. E.

(2020). Novel effect size interpretation guidelines and an evaluation of statistical power in rehabilitation research. Archives Of Physical Medicine And Rehabilitation, 101(12), 2219–2226. https://doi.org/10.1016/j.apmr.2020.02.017

40.

Kóbor

Takács

Á.

Bryce

Szűcs

Honbolygó

Nagy

Csépe

(2015). Children with ADHD show impairments in multiple stages of information processing in a Stroop task: An ERP study. Developmental Neuropsychology, 40(6), 329–347. https://doi.org/10.1080/87565641.2015.1086770

41.

Kofler

M. J.

Rapport

M. D.

Sarver

D. E.

Raiker

J. S.

Orban

S. A.

Friedman

L. M.

Kolomeyer

E. G.

(2013). Reaction time variability in ADHD: A meta-analytic review of 319 studies. Clinical Psychology Review, 33(6), 795–811. https://doi.org/10.1016/j.cpr.2013.06.001

42.

Kölle

Mackert

Heckel

Philipsen

Ulrich

Grön

(2022). Lower fractional anisotropy of the corticothalamic tract and increased response time variability in adult patients with ADHD. Journal of Psychiatry & Neuroscience: JPN, 47(2), E99–E108. https://doi.org/10.1503/jpn.210135

43.

Lacouture

Cousineau

(2008). How to use MATLAB to fit the ex-Gaussian and other probability functions to a distribution of response times. Tutorials in Quantitative Methods for Psychology, 4(1), 35–45. https://doi.org/10.20982/tqmp.04.1.p035

44.

Leth-Steensen

King Elbaz

Douglas

V. I.

(2000). Mean response times, variability, and skew in the responding of ADHD children: A response time distributional approach. Acta Psychologica, 104(2), 167–190. https://doi.org/10.1016/s0001-6918(00)00019-6

45.

Liu

Lane

S. D.

Schmitz

J. M.

Green

C. E.

Cunningham

K. A.

Moeller

F. G.

(2012). Increased intra-individual reaction time variability in cocaine-dependent subjects: Role of cocaine-related cues. Addictive Behaviors, 37(2), 193–197. https://doi.org/10.1016/j.addbeh.2011.10.003

46.

Liu, Z. Z., Qu, H. J., Tian, Z. L., Han, M. J., Fan, Y., Ge, L. Z., Zang, Y. F., & Zhang, H. (2017). Reproducibility of frequency-dependent low frequency fluctuations in reaction time over time and across tasks. PloS One, 12(9), e0184476. https://doi.org/10.1371/journal.pone.0184476

47.

Lü

Jin

C.-H.

Zhou

(2009). Similarity index based on local paths for link prediction of complex networks. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics, 80(4 Pt 2), 046122. https://doi.org/10.1103/physreve.80.046122

48.

Luce

R. D.

(1986a). Response times: Their role in inferring elementary mental organization. Oxford University Press.

49.

Luce

R. D.

(1986b). Response time distributions in memory search: a caution. In Klix

Hagendorf

(eds), Human memory and cognitive capabilities: Mechanisms and Performances. Elsevier Science Publishers BV (North-Holland.

50.

MacLeod

C. M.

(1991). Half a century of research on the Stroop effect: An integrative review. Psychological Bulletin, 109(2), 163–203. https://doi.org/10.1037/0033-2909.109.2.163

51.

Matzke

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. https://doi.org/10.3758/pbr.16.5.798

52.

Mead

L. A.

Mayer

A. R.

Bobholz

J. A.

Woodley

S. J.

Cunningham

J. M.

Hammeke

T. A.

Rao

S. M.

(2002). Neural basis of the Stroop interference task: Response competition or selective attention? Journal of the International Neuropsychological Society: JINS, 8(6), 735–742. https://doi.org/10.1017/s1355617702860015

53.

Meijer

K. A.

Steenwijk

M. D.

Douw

Schoonheim

M. M.

Geurts

J. J. G.

(2020). Long-range connections are more severely damaged and relevant for cognition in multiple sclerosis. Brain: A Journal of Neurology, 143(1), 150–160. https://doi.org/10.1093/brain/awz355

54.

Mofidi

Perez

Kornerup

Levin

Ortiz

Lai

Green

Kim

& Gibson

M. P.

(2022). Dental students’ knowledge, confidence, ability, and self-reported difficulties in periodontal education: A mixed method pilot study. Dentistry Journal, 10(4), 63. https://doi.org/10.3390/dj10040063

55.

Moor

Banerjee

Abad

Z. S. H.

Krumholz

H. M.

Leskovec

Topol

E. J.

Rajpurkar

(2023). Foundation models for generalist medical artificial intelligence. Nature, 616(7956), 259–265. https://doi.org/10.1038/s41586-023-05881-4

56.

Mordkoff

J. T.

(2012). Observation: Three reasons to avoid having half of the trials be congruent in a four-alternative forced-choice experiment on sequential modulation. Psychonomic Bulletin & Review, 19(4), 750–757. https://doi.org/10.3758/s13423-012-0257-3

57.

Nath

Majumder

Sen

Rahman

M. M.

(2021). Risk analysis of COVID-19 infections in Kolkata metropolitan city: A GIS-based study and policy implications. GeoHealth, 5(4), e2020GH000368. https://doi.org/10.1029/2020gh000368

58.

Nawaz

Nisar

Voon

Y. V

. (2021). Analyses of Stroop EEG using event related potentials and time-frequency decomposition. In 2020 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES). IEEE.

59.

Panagiotaropoulou

Thrapsanioti

Pappa

Grigoras

Mylonas

Karavasilis

Velonakis

Kelekis

& Smyrnis

(2019). Hypo-activity of the dorsolateral prefrontal cortex relates to increased reaction time variability in patients with schizophrenia. NeuroImage Clinical, 23, 101853. https://doi.org/10.1016/j.nicl.2019.101853

60.

Peterson

B. S.

Skudlarski

Gatenby

J. C.

Zhang

Anderson

A. W.

Gore

J. C.

(1999). An fMRI study of Stroop word-color interference: Evidence for cingulate subregions subserving multiple distributed attentional systems. Biological Psychiatry, 45(10), 1237–1258. https://doi.org/10.1016/s0006-3223(99)00056-6

61.

Pitkänen, J., Koikkalainen, J., Nieminen, T., Marinkovic, I., Curtze, S., Sibolt, G., Jokinen, H., Rueckert, D., Barkhof, F., Schmidt, R., Pantoni, L., Scheltens, P., Wahlund, L. O., Korvenoja, A., Lötjönen, J., Erkinjuntti, T., & Melkas, S. (2020). Evaluating severity of white matter lesions from computed tomography images with convolutional neural network. Neuroradiology, 62(10), 1257–1263. https://doi.org/10.1007/s00234-020-02410-2

62.

Ratcliff

(1979). Group reaction time distributions and an analysis of distribution statistics. Psychological Bulletin, 86(3), 446–461. https://doi.org/10.1037/0033-2909.86.3.446

63.

Ratcliff

(1993). Methods for dealing with reaction time outliers. Psychological Bulletin, 114(3), 510–532. https://doi.org/10.1037/0033-2909.114.3.510

64.

Ratcliff

Murdock

B. B.

(1976). Retrieval processes in recognition memory. Psychological Review, 83(3), 190–214. https://doi.org/10.1037/0033-295X.83.3.190

65.

Sari

Eriksson

Mingels

Alberts

Casey

M. E.

Afshar-Oromieh

Conti

Cumming

Shi

& Rominger

(2023). Feasibility of using abbreviated scan protocols with population-based input functions for accurate kinetic modeling of [18F]-FDG datasets from a long axial FOV PET scanner. European Journal of Nuclear Medicine and Molecular Imaging, 50(2), 257–265. https://doi.org/10.1007/s00259-022-05983-7

66.

Schmidt

J. R.

(2016). Context-specific proportion congruency effects: An episodic learning account and computational model. Frontiers in Psychology, 2016(7), 1806. https://doi.org/10.3389/fpsyg.2016.01806

67.

Spangler

D. P.

Cox

K. R.

Thayer

J. F.

Brooks

J. R.

Friedman

B. H.

(2021). Interplay between state anxiety, heart rate variability, and cognition: An ex-Gaussian analysis of response times. International Journal of Psychophysiology: Official Journal of the International Organization of Psychophysiology, 159, 60–70. https://doi.org/10.1016/j.ijpsycho.2020.08.018

68.

Spangler

D. P.

Williams

D. P.

Speller

L. F.

Brooks

J. R.

Thayer

J. F.

(2018). Resting heart rate variability is associated with ex-Gaussian metrics of intra-individual reaction time variability. International Journal of Psychophysiology: Official Journal of the International Organization of Psychophysiology, 125, 10–16. https://doi.org/10.1016/j.ijpsycho.2018.01.009

69.

Spangler

D. P.

Yang

Weidler

B. J.

Thayer

J. F.

McGinley

J. J.

(2022). Unraveling the cognitive correlates of heart rate variability with the drift diffusion model. International Journal of Psychophysiology: Official Journal of the International Organization of Psychophysiology, 181, 73–84. https://doi.org/10.1016/j.ijpsycho.2022.08.003

70.

Spano

Giannico

Elia

Bosco

Lafortezza

Sanesi

(2020). Human health–environment interaction science: An emerging research paradigm. The Science of the Total Environment, 704(135358), 135358. https://doi.org/10.1016/j.scitotenv.2019.135358

71.

Steinhauser

Hübner

(2009). Distinguishing response conflict and task conflict in the Stroop task: Evidence from ex-Gaussian distribution analysis. Journal of Experimental Psychology. Human Perception and Performance, 35(5), 1398–1412. https://doi.org/10.1037/a0016467

72.

Stroop

J. R.

(1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18(6), 643–662. https://doi.org/10.1037/h0054651

73.

Tian

Zaretskaya

Fan

Ngamsombat

Bilgic

Polimeni

J. R.

Huang

S. Y.

(2021). Improved cortical surface reconstruction using sub-millimeter resolution MPRAGE by image denoising. NeuroImage, 233(117946), 117946. https://doi.org/10.1016/j.neuroimage.2021.117946

74.

Urriola

Bollmann

Tremayne

Burianová

Marstaller

Reutens

(2020). Functional connectivity of the irritative zone identified by electrical source imaging, and EEG-correlated fMRI analyses. NeuroImage. Clinical, 28(102440), 102440. https://doi.org/10.1016/j.nicl.2020.102440

75.

Vainieri, I., Adamo, N., Michelini, G., Kitsune, V., Asherson, P., & Kuntsi, J. (2020) Attention regulation in women with ADHD and women with bipolar disorder: An ex-Gaussian approach. Psychiatry Research, 285(2012), 112729. https://doi.org/10.1016/j.psychres.2019.112729

76.

Vinding, M. S., Brenner, D., Tse, D. H., Vellmer, S., Vosegaard, T., Suter, D., Stöcker, T., & Maximov, I. I. (2017). Application of the limited-memory quasi-Newton algorithm for multi-dimensional, large flip-angle RF pulses at 7 T. Magma (New York, N.Y.), 30(1), 29–39. https://doi.org/10.1007/s10334-016-0580-1

77.

Voorneveld

Muralidharan

Hope

Vos

H. J.

Kruizinga

van der Steen

A. F. W.

Gijsen

F. J. H.

Kenjeres

de Jong

& Bosch

J. G.

(2018). High frame rate ultrasound particle image velocimetry for estimating high velocity flow patterns in the left ventricle. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 65(12), 2222–2232. https://doi.org/10.1109/tuffc.2017.2786340

78.

Vurdah

Vidal

Viarouge

(2023). Event-related potentials reveal the impact of conflict strength in a numerical Stroop paradigm. Brain Sciences, 13(4), 586. https://doi.org/10.3390/brainsci13040586

79.

Wang

(2016). CD-based indices for link prediction in complex network. PloS One, 11(1), e0146727. https://doi.org/10.1371/journal.pone.0146727

80.

West

Hamlin

Frangou

Wilson

T. W.

Doucet

G. E

. (2022). Person-based similarity index for cognition and its neural correlates in late adulthood: Implications for cognitive reserve. Cerebral Cortex (New York, N.Y.: 1991), 32(2), 397–407. https://doi.org/10.1093/cercor/bhab215

81.

Xuan-Tran

B. X.

Latkin

C. A.

Thanh-Phan

Thi-Nguyen

H. L.

Linh-Hoang

C. S. H.

R. C. M.

(2022). Smoking among people living with HIV/AIDS: A bibliometric analysis (GAP _RESEARCH). AIDS Reviews, 25(3), 105–114. https://doi.org/10.24875/aidsrev.19000098

82.

Yamashita

Rothlein

Kucyi

Valera

E. M.

Germine

Wilmer

DeGutis

& Esterman

(2021). Variable rather than extreme slow reaction times distinguish brain states during sustained attention. Scientific Reports, 11(1), 14883. https://doi.org/10.1038/s41598-021-94161-0

83.

Zhou

Liu

Dong

Yang

Hou

, … Tian

(2012). Interindividual reaction time variability is related to resting-state network topology: An electroencephalogram study. Neuroscience, 202(2012), 276–282. https://doi.org/10.1016/j.neuroscience.2011.11.048