Abstract
Rationale
Cigarette smokers often experience cognitive decrements during abstinence from tobacco, and these decrements may have clinical relevance in the context of smoking cessation interventions. However, limitations of the behavioral summary statistics used to measure cognitive effects of abstinence, response times (RT) and accuracy rates, may restrict the field’s ability to identify robust abstinence effects on task performance and test mechanistic hypotheses about the etiology of these cognitive changes.
Objectives
The current study explored whether a measurement approach based on mathematical models of cognition, which make the cognitive mechanisms necessary to perform choice RT tasks explicit, would be able to address these limitations.
Methods
The linear ballistic accumulator model (LBA: Brown and Heathcote, Cogn Psychol 57(3):153-178, 2008) was fit to an existing data set from a study that evaluated the impact of overnight abstinence on flanker task performance.
Results
The model-based analysis provided evidence that smokers’ rates of mind wandering increased during abstinence, and was able to index this effect while controlling for participants’ strategy changes that were related to the specific experimental paradigm used.
Conclusion
Mind wandering is a putative explanation for cognitive withdrawal symptoms during smoking cessation and may be indexed using the LBA. More broadly, the use of formal model-based analyses in future research on this topic has the potential to allow for strong and specific tests of mechanistic explanations for these symptoms.
Similar content being viewed by others
Notes
The participant with missing PANAS data in the smoking condition was also excluded from the analysis using Δdeadline as a covariate so results from this analysis could be properly compared with the one using ΔPANAS-NA.
References
Ashare RL, Hawk LW (2012) Effects of smoking abstinence on impulsive behavior among smokers high and low in ADHD-like symptoms. Psychopharmacology 219(2):537–547
Ashare RL, Schmidt HD (2014) Optimizing treatments for nicotine dependence by increasing cognitive performance during withdrawal. Expert Opin Drug Discovery 9(6):579–594
Ashare RL, Falcone M, Lerman C (2014) Cognitive function during nicotine withdrawal: implications for nicotine dependence treatment. Neuropharmacology 76:581–591
Bastian M, Sackur J (2013) Mind wandering at the fingertips: automatic parsing of subjective states based on response time variability. Front Psychol 4
Benowitz NL (2010) Nicotine addiction. N Engl J Med 362(24):2295–2303
Boehm, U., Marsman, M., Matzke, D., & Wagenmakers, E.-J. (under review). On the importance of avoiding shortcuts in modelling hierarchical data. Manuscript submitted for publication
Bogacz R, Hu PT, Holmes PJ, Cohen JD (2010) Do humans produce the speed–accuracy trade-off that maximizes reward rate? Q J Exp Psychol 63(5):863–891
Brown SD, Heathcote A (2008) The simplest complete model of choice response time: linear ballistic accumulation. Cogn Psychol 57(3):153–178
Centers for Disease Control and Prevention (2011) Quitting smoking among adults—United States, 2001-2010. MMWR. Morb Mortal Wkly Rep 60(44):1513
Christoff K, Gordon AM, Smallwood J, Smith R, Schooler JW (2009) Experience sampling during fMRI reveals default network and executive system contributions to mind wandering. Proc Natl Acad Sci 106(21):8719–8724
Donkin C, Brown SD, Heathcote A (2009) The overconstraint of response time models: rethinking the scaling problem. Psychon Bull Rev 16(6):1129–1135
Ester EF, Ho TC, Brown SD, Serences JT (2014) Variability in visual working memory ability limits the efficiency of perceptual decision making. J Vis 14(4):2–2
Evans DE, Drobes DJ (2009) Nicotine self-medication of cognitive-attentional processing. Addict Biol 14(1):32–42
Forstmann BU, Wagenmakers EJ (2015) Model-based cognitive neuroscience: A conceptual introduction. In: Forstmann B, Wagenmakers E-J (eds) An introduction to model-based cognitive neuroscience. Springer, New York, pp 139–156
Fosco WD, White CN, Hawk LW (2017) Acute stimulant treatment and reinforcement increase the speed of information accumulation in children with ADHD. J Abnorm Child Psychol 45(5):911–920
Hawkins G, Mittner M, Boekel W, Heathcote A, Forstmann BU (2015) Toward a model-based cognitive neuroscience of mind wandering. Neuroscience 310:290–305
Heathcote A, Hannah K (2013) A two-phase theory of choice conflict tasks. Paper presented at the The 36th Annual Conference of the Cognitive Science Society, Berlin
Heathcote A, Suraev A, Curley S, Love J, Michie P (2015) Decision processes and the slowing of simple choices in schizophrenia. J Abnorm Psychol 124(4):967–974
Heathcote A, Lin Y, Gretton M (2017) DMC: dynamic models of choice. Retrieved from osf.io/pbwx8
Heathcote A, Lin YS, Reynolds A, Strickland L, Gretton M, Matzke D (2018) Dynamic models of choice. Behav Res Methods https://doi.org/10.3758/s13428-018-1067-y
Heishman SJ, Kleykamp BA, Singleton EG (2010) Meta-analysis of the acute effects of nicotine and smoking on human performance. Psychopharmacology 210(4):453–469
Holmes WR, Trueblood JS, Heathcote A (2016) A new framework for modeling decisions about changing information: the piecewise linear ballistic accumulator model. Cogn Psychol 85:1–29
Huang-Pollock CL, Karalunas SL, Tam H, Moore AN (2012) Evaluating vigilance deficits in ADHD: a meta-analysis of CPT performance. J Abnorm Psychol 121(2):360
Johns G (1981) Difference score measures of organizational behavior variables: a critique. Organ Behav Hum Perform 27(3):443–463
Karalunas SL, Geurts HM, Konrad K, Bender S, Nigg JT (2014) Annual research review: reaction time variability in ADHD and autism spectrum disorders: measurement and mechanisms of a proposed trans-diagnostic phenotype. J Child Psychol Psychiatry 55(6):685–710
Kassel JD, Stroud LR, Paronis CA (2003) Smoking, stress, and negative affect: correlation, causation, and context across stages of smoking. Psychol Bull 129(2):270
Kollins SH, McClernon FJ, Epstein JN (2009) Effects of smoking abstinence on reaction time variability in smokers with and without ADHD: an ex-Gaussian analysis. Drug Alcohol Depend 100(1):169–172
Kollins SH, English JS, Roley ME, O’Brien B, Blair J, Lane SD, McClernon FJ (2013) Effects of smoking abstinence on smoking-reinforced responding, withdrawal, and cognition in adults with and without attention deficit hyperactivity disorder. Psychopharmacology 227(1):19–30
Levin ED, McClernon FJ, Rezvani AH (2006) Nicotinic effects on cognitive function: behavioral characterization, pharmacological specification, and anatomic localization. Psychopharmacology 184(3–4):523–539
Ly A, Boehm U, Heathcote A, Turner BM, Forstmann B, Marsman M, Matzke D (2017) A flexible and efficient hierarchical Bayesian approach to the exploration of individual differences in cognitive-modelbased neuroscience. In: Moustafa AA (ed) Computational models of brain and behavior. Wiley Blackwell, Hoboken, pp. 467–480
Ly A, Marsman M, Wagenmakers EJ (2018) Analytic posteriors for Pearson’s correlation coefficient. Statistica Neerlandica 72(1):4–13
Marsman M, Maris G, Bechger T, Glas C (2016) What can we learn from plausible values? psychometrika 81(2):274–289
Matzke D, Wagenmakers E-J (2009) Psychological interpretation of the ex-Gaussian and shifted Wald parameters: a diffusion model analysis. Psychon Bull Rev 16(5):798–817
McClernon FJ, Addicott MA, Sweitzer MM (2015) Smoking abstinence and neurocognition: implications for cessation and relapse. In: Balfour D, Munafo M (eds) The Neurobiology and Genetics of Nicotine and Tobacco. Springer International Publishing, Basel, pp 193–227
McVay JC, Kane MJ (2009) Conducting the train of thought: working memory capacity, goal neglect, and mind wandering in an executive-control task. J Exp Psychol Learn Mem Cogn 35(1):196
McVay JC, Kane MJ (2012) Drifting from slow to “d’oh!”: working memory capacity and mind wandering predict extreme reaction times and executive control errors. J Exp Psychol Learn Mem Cogn 38(3):525
Millar RB (2018) Conditional vs marginal estimation of the predictive loss of hierarchical models using WAIC and cross-validation. Stat Comput 28(2):375–385
Mittner M, Boekel W, Tucker AM, Turner BM, Heathcote A, Forstmann BU (2014) When the brain takes a break: a model-based analysis of mind wandering. J Neurosci 34(49):16286–16295
Piasecki TM (2006) Relapse to smoking. Clin Psychol Rev 26(2):196–215
R Core Team (2013) R: A language and environment for statistical computing. [Computer Software] Retrieved from www.r-project.org Accessed 1 Dec 2013
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
Ratcliff R, McKoon G (2008) The diffusion decision model: theory and data for two-choice decision tasks. Neural Comput 20(4):873–922
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
Rhodes JD, Hawk LW (2016) Smoke and mirrors: the overnight abstinence paradigm as an index of disrupted cognitive function. Psychopharmacology 233(8):1395–1404
Sayette MA, Schooler JW, Reichle ED (2010) Out for a smoke the impact of cigarette craving on zoning out during reading. Psychol Sci 21(1):26–30
Schlienz NJ, Hawk LW, Rosch KS (2013) The effects of acute abstinence from smoking and performance-based rewards on performance monitoring. Psychopharmacology 229(4):701–711
Services, U. D. o. H. a. H (2014) The health consequences of smoking—50 years of progress: a report of Surgeon General. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 17, Atlanta
Shahar N, Teodorescu AR, Usher M, Pereg M, Meiran N (2014) Selective influence of working memory load on exceptionally slow reaction times. J Exp Psychol Gen 143(5):1837
Shiffman S, Paty JA, Gnys M, Kassel JA, Hickcox M (1996) First lapses to smoking: within-subjects analysis of real-time reports. J Consult Clin Psychol 64(2):366
Smallwood J, Schooler JW (2006) The restless mind. Psychol Bull 132(6):946
Smallwood J, Schooler JW (2015) The science of mind wandering: empirically navigating the stream of consciousness. Annu Rev Psychol 66:487–518
Smallwood J, Fitzgerald A, Miles LK, Phillips LH (2009) Shifting moods, wandering minds: negative moods lead the mind to wander. Emotion 9(2):271
Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A (2002) Bayesian measures of model complexity and fit. J R Stat Soc Ser B (Stat Methodol) 64(4):583–639
Spiegelhalter DJ, Best NG, Carlin BP, Linde A (2014) The deviance information criterion: 12 years on. J R Stat Soc Ser B Stat Methodol 76(3):485–493
Turner BM, Sederberg PB, Brown SD, Steyvers M (2013) A method for efficiently sampling from distributions with correlated dimensions. Psychol Methods 18(3):368
van Ravenzwaaij D, Brown S, Wagenmakers E-J (2011) An integrated perspective on the relation between response speed and intelligence. Cognition 119(3):381–393
Vehtari A, Gelman A, Gabry J (2016) Practical Bayesian model evaluation using leave-one-out crossvalidation and WAIC. Stat Comput 27(5):1413–1432
Vehtari A, Gelman A, Gabry J (2016a) loo: Efficient leave-one-out cross-validation and WAIC for Bayesian models (Version R package version 1.1.0 ). Retrieved from https://CRAN.R-project.org/package=loo
Voss A, Nagler M, Lerche V (2013) Diffusion models in experimental psychology. Exp Psychol
Watanabe S (2010) Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J Mach Learn Res 11(Dec):3571–3594
Weigard A, Huang-Pollock C (2017) The role of speed in ADHD-related working memory deficits: a time-based resource-sharing and diffusion model account. Clin Psychol Sci 5(2):195–211
Weigard A, Huang-Pollock C, Brown S (2016) Evaluating the consequences of impaired monitoring of learned behavior in attention-deficit/hyperactivity disorder using a Bayesian hierarchical model of choice response time. Neuropsychology 30(4):502
White CN, Ratcliff R, Vasey MW, McKoon G (2010) Using diffusion models to understand clinical disorders. J Math Psychol 54(1):39–52
White CN, Ratcliff R, Starns JJ (2011) Diffusion models of the flanker task: discrete versus gradual attentional selection. Cogn Psychol 63(4):210–238
World Health Organization (2015) WHO global report on trends in prevalence of tobacco smoking 2015. World Health Organization, Geneve
Zelle SL, Gates KM, Fiez JA, Sayette MA, Wilson SJ (2017) The first day is always the hardest: functional connectivity during cue exposure and the ability to resist smoking in the initial hours of a quit attempt. NeuroImage 151:24–32
Acknowledgements
Andrew Heathcote would like to acknowledge the Australian Research Council grant DP160101891 for supporting his work on this project.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Electronic supplementary material
ESM 1
(DOCX 533 kb)
Rights and permissions
About this article
Cite this article
Weigard, A., Huang-Pollock, C., Heathcote, A. et al. A cognitive model-based approach to testing mechanistic explanations for neuropsychological decrements during tobacco abstinence. Psychopharmacology 235, 3115–3124 (2018). https://doi.org/10.1007/s00213-018-5008-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00213-018-5008-3