Alternative models of estimating the Stop-Signal Reaction Time in the Stop-Signal Paradigm and their differential associations with self-reports of impulsivity domains

Giulia Gialdi, Antonella Somma, Claudia Virginia Manara, Andrea Fossati

Accepted April 30, 2020

First published April 30, 2020

Abstract

The Stop-Signal Reaction Time (SSRT) as a measure of impulsive behavior has been called into question. The aim of the present study was to assess the relationship between self-report measure of risk-taking and impulsivity and
different SSRT estimation methods. Fifty Italian university students (male participants = 15, 30.0%, female participants = 35, 70.0%; mean age = 22.64 years, SD = 2.63 years) agreed to participate in the study. Roughly 49 participants were
required to allow .80 power for detecting a Spearman r value of .40 with p<.05. Participants were administered the SST using a laptop computer in individual sessions and completed the Italian versions of the UPPS-P Impulsivity Scale, Barratt Impulsiveness Scale-11, and Impulsive-Unsocialized Sensation-Seeking Scale. Spearman r values suggested that all SSRT
models were significantly associated with self-report measures of sensation-seeking/risk taking behaviors. However, only BEEST estimates were non-trivially associated also with measures of core features of impulsivity (i.e., lack of premeditation).
Our findings seemed to suggest that adopting a Bayesian perspective on SSRT estimation may allow to obtain experimental measures of both risk-taking and impulsive behaviors.

Alternative models of estimating the Stop-Signal Reaction Time in the Stop-Signal Paradigm and their differential associations with self-reports of impulsivity domains

References

  • AFONSO, A.S. Jr., MACHADO, A.V., CARREIRO, L.R.R. &MACHADO-PINHEIRO, W. (2020). Interaction betweeninhibitory mechanisms involved in stroop-matching and stop-signal tasks, and their association with impulsivity levels.Psychology & Neuroscience. Advance online publication.

    doi.org/10.1037/pne0000203
  • BAKER, M. (2016). 1,500 scientists lift the lid on reproducibility.

  • Nature News, 533 (7604), 452.

  • BAND, G., VAN DER MOLEN, M. & LOGAN, G. (2003). Horse-race model simulations of the stop-signal procedure. ActaPsychologica, 112 (2), 105-142.

  • BARI, A. & ROBBINS, T.W. (2013). Inhibition and impulsivity:Behavioral and neural basis of response control. Progress inNeurobiology, 108, 44-79.

  • CARLOTTA, D., BORRONI, S., MAFFEI, C. & FOSSATI, A. (2011).The role of impulsivity, sensation seeking and aggression in therelationship between childhood AD/HD symptom and antisocialbehavior in adolescence. Neurology, Psychiatry and BrainResearch, 17 (4), 89-98.

  • COHEN, J. (1988). Statistical power analysis (2nd ed.). Hillsdale NJ:Erlbaum.

  • CYDERS, M.A. & SMITH, G.T. (2007). Mood-based rash action andits components: Positive and negative urgency. Personality andIndividual Differences, 43, 839-850.

  • DALLEY, J.W. & ROBBINS, T.W. (2017). Fractionating impulsivity:Neuropsychiatric implications. Nature Reviews Neuroscience, 18(3), 158-171.

  • DAVISON, A.C. & HINKLEY, D.V. (1997). Bootstrap methods andtheir application. Cambridge, UK: Cambridge University Press.

  • DE PASCALIS, V. & RUSSO, P.M. (2003). Zuckerman-KuhlmanPersonality Questionnaire: Preliminary results of the Italianversion. Psychological Reports, 92, 965-974.

  • EASTERBROOK, S.M. (2014). Open code for open science? NatureGeoscience, 7, 779-781.

  • EFRON, B. & TIBSHIRANI, R.J. (1998). An introduction to thebootstrap. New York: Chapman & Hall/CRC.

  • FARRELL, S. & LUDWIG, C.J.H. (2008). Bayesian and maximumlikelihood estimation of hierarchical response time models.Psychonomic Bulletin & Review, 15 (6), 1209-1217.

  • FOSSATI, A., DI CEGLIE, A., ACQUARINI, E. & BARRATT, E.S.(2001). Psychometric properties of an Italian version of theBarratt Impulsiveness Scale‐11 (BIS‐11) in nonclinical subjects.Journal of Clinical Psychology, 57 (6), 815-828.

  • GLEESON, P., DAVISON, A.P., SILVER, R.A. & ASCOLI, G.A.(2017). A commitment to open source in neuroscience. Neuron,96 (5), 964-965.

  • HEATHCOTE, A., LIN, Y.S., REYNOLDS, A., STRICKLAND, L.,GRETTON, M. & MATZKE, D. (2019). Dynamic models ofchoice. Behavior Research Methods, 51 (2), 961-985.

  • ITALIAN ASSOCIATION OF PSYCHOLOGY (2015). Ethical Code.Retrieved from https://aipass.org/node/11560

  • KIM, S., POTTER, K., CRAIGMILE, P.F., PERUGGIA, M. & VANZANDT, T. (2017). A Bayesian race model for recognitionmemory. Journal of the American Statistical Association, 112(517), 77-91.

  • LOGAN, G.D. & COWAN, W.B. (1984). On the ability to inhibitthought and action: A theory of an act of control. PsychologicalReview, 91 (3), 295-327.

  • MATZKE, D., CURLEY, S., GONG, C.Q. & HEATHCOTE, A. (2019).Inhibiting responses to difficult choices. Journal of ExperimentalPsychology: General, 148 (1), 124-142.

  • MATZKE, D., DOLAN, C.V., LOGAN, G.D., BROWN, S.D. &WAGENMAKERS, E.J. (2013). Bayesian parametric estimationofstop-signalreactiontimedistributions.JournalofExperimentalPsychology: General, 142, 1047-1073.

  • MATZKE, D., HUGHES, M., BADCOCK, J.C., MICHIE, P.T. &HEATHCOTE, A. (2017). Failures of cognitive control orattention? The case of stop-signal deficits in schizophrenia.Attention, Perception, & Psychophysics, 79 (4), 1078-1086.

  • MATZKE, D., LOVE, J. & HEATHCOTE, A. (2017). A Bayesianapproach for estimating the probability of trigger failures in thestop-signal paradigm. Behavior Research Methods, 49, 267-281.

  • MATZKE, D., LOVE, J., WIECKI, T.V., BROWN, S.D., LOGAN,G.D. & WAGENMAKERS, E.J. (2013). Release the BEESTS:Bayesian estimation of ex-Gaussian stop-signal reaction timedistributions. Frontiers in Psychology, 4, 918.

  • MATZKE, D., VERBRUGGEN, F. & LOGAN, G. (2018). The stop-signal paradigm. In E.-J. Wagenmakers & J.T. Wixted (Eds.),Methodology: Volume 5. John Wiley & Sons, Inc.

  • MIŁKOWSKI, M., HENSEL, W.M. & HOHOL, M. (2018).Replicability or reproducibility? On the replication crisis incomputational neuroscience and sharing only relevant detail.Journal of Computational Neuroscience, 45 (3), 163-172.

  • MIYAKE, A., FRIEDMAN, N.P., EMERSON, M.J., WITZKI, A.H.,HOWERTER, A. & WAGER, T.D. (2000). The unity and diversityofexecutivefunctionsandtheircontributionstocomplex“frontallobe” tasks: A latent variable analysis. Cognitive Psychology, 41(1), 49-100.

  • NEDERKOORN, C., JANSEN, E., MULKENS, S. & JANSEN,A. (2007). Impulsivity predicts treatment outcome in obesechildren. Behaviour Research and Therapy, 45 (5), 1071-1075.

  • OPEN SCIENCE COLLABORATION (2015). Estimating the

  • reproducibility of psychological science. Science, 349 (6251).

  • PATTON, J.H., STANFORD, M.S. & BARRATT, E.S. (1995). Factorstructure of the Barratt impulsiveness scale. Journal of ClinicalPsychology, 51 (6), 768-774.

  • SHARMA, L., MARKON, K.E. & CLARK, L.A. (2014). Toward atheory of distinct types of “impulsive” behaviors: A meta-analysisof self-report and behavioral measures. Psychological Bulletin,140 (2), 374-408.

  • SHULMAN, E.P., SMITH, A.R., SILVA, K., ICENOGLE, G., DUELL,N., CHEIN, J. & STEINBERG, L. (2016). The dual systems model:Review, reappraisal, and reaffirmation. Developmental CognitiveNeuroscience, 17, 103-117.

  • SKIPPEN, P., MATZKE, D., HEATHCOTE, A., FULHAM, W. R.,MICHIE, P. & KARAYANIDIS, F. (2019). Reliability of triggeringinhibitory process is a better predictor of impulsivity than SSRT.Acta Psychologica, 192, 104-117.

  • STAHL, C., VOSS, A., SCHMITZ, F., NUSZBAUM, M., TÜSCHER,O., LIEB, K. & KLAUER, K.C. (2014). Behavioral componentsof impulsivity. Journal of Experimental Psychology: General, 143(2), 850-886.

  • VAN DE LAAR, M.C., VAN DEN WILDENBERG, W.P., VANBOXTEL, G. & VAN DER MOLEN, M. (2011). Lifespan changesin global and selective stopping and performance adjustments.Frontiers in Psychology, 2, 357.

  • VARGHA,A.&DELANEY,H.D.(2000).Acritiqueandimprovementof the CL common language effect size statistics of McGraw andWong. Journal of Educational and Behavioral Statistics, 25 (2),101-132.

  • VERBRUGGEN, F., ARON, A.R., BAND, G.P., BESTE, C., BISSETT,P.G.,BROCKETT,A.T.,…&COLZATO,L.S.(2019).Aconsensusguide to capturing the ability to inhibit actions and impulsivebehaviors in the stop-signal task. Elife, 8, e46323.

  • WHITESIDE, S.P. & LYNAM, D.R. (2001). The five factors modeland impulsivity: Using a structural model of personality tounderstand impulsivity. Personality and Individual Differences,30 (4), 669-689.

  • WÖSTMANN, N.M., AICHERT, D.S., COSTA, A., RUBIA, K.,MÖLLER, H.J. & ETTINGER, U. (2013). Reliability andplasticity of response inhibition and interference control. Brainand Cognition, 81 (1), 82-94.

  • ZUCKERMAN, M., KUHLMAN, D.M., THORNQUIST, M. &KIERS, H. (1991). Five (or three) robust questionnaire scalefactors of personality without culture. Personality and IndividualDifferences, 12 (9), 929-941.

SHOW ALL REFERENCES (42)HIDE REFERENCES

Article info

Cite the article:

Author Surname Author Initial. Title. Publication Title. Year Published;Volume number(Issue number):Pages Used. doi:DOI Number.


Gialdi Giulia . Somma Antonella . Manara Claudia Virginia . Fossati Andrea . Alternative models of estimating the Stop-Signal Reaction Time in the Stop-Signal Paradigm and their differential associations with self-reports of impulsivity domains. BPA Applied Psychology Bulletin. 2020;287(1):19-29.

Citation tool

How to cite this article

Author Surname Author Initial. Title. Publication Title. Year Published;Volume number(Issue number):Pages Used. doi:DOI Number.


Gialdi Giulia . Somma Antonella . Manara Claudia Virginia . Fossati Andrea . Alternative models of estimating the Stop-Signal Reaction Time in the Stop-Signal Paradigm and their differential associations with self-reports of impulsivity domains. BPA Applied Psychology Bulletin. 2020;287(1):19-29.