Advertisement

Modeling the effects of methylphenidate on interference and evidence accumulation processes using the conflict linear ballistic accumulator

  • Alexander WeigardEmail author
  • Andrew Heathcote
  • Chandra Sripada
Original Investigation

Abstract

Rationale

Although methylphenidate and other stimulants have been demonstrated to improve task performance across a variety of domains, a computationally rigorous account of how these drugs alter cognitive processing remains elusive. Recent applications of mathematical models of cognitive processing and electrophysiological methods to this question have suggested that stimulants improve the integrity of evidence accumulation processes for relevant choices, potentially through catecholaminergic modulation of neural signal-to-noise ratios. However, this nascent line of work has thus far been limited to simple perceptual tasks and has largely omitted more complex conflict paradigms that contain experimental manipulations of specific top-down interference resolution processes.

Objectives and methods

To address this gap, this study applied the conflict linear ballistic accumulator (LBA), a newly proposed model designed for conflict tasks, to data from healthy adults who performed the Multi-Source Interference Task (MSIT) after acute methylphenidate or placebo challenge.

Results

Model-based analyses revealed that methylphenidate improved performance by reducing individuals’ response thresholds and by enhancing evidence accumulation processes across all task conditions, either by improving the quality of evidence or by reducing variability in accumulation processes. In contrast, the drug did not reduce bottom-up interference or selectively facilitate top-down interference resolution processes probed by the experimental conflict manipulation.

Conclusions

Enhancement of evidence accumulation is a biologically plausible and task-general mechanism of stimulant effects on cognition. Moreover, the assumption that methylphenidate’s effects on behavior are only visible with complex executive tasks may be misguided.

Keywords

Methylphenidate Stimulants Evidence accumulation Conflict tasks Executive functions Cognitive modeling Computational psychiatry Bayesian 

Notes

Funding

AW was supported by funding from the National Institute on Alcohol Abuse and Alcoholism (T32 AA007477). CS was supported by funding from the National Institute on Alcohol Abuse and Alcoholism (K23-AA-020297).

Compliance with ethical standards

All experimental procedures were in compliance with the laws of the country in which the experiment was performed (USA).

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

213_2019_5316_MOESM1_ESM.docx (47 kb)
ESM 1 (DOCX 46 kb)

References

  1. Aston-Jones G, Cohen JD (2005) An integrative theory of locus coeruleus-norepinephrine function: adaptive gain and optimal performance. Annu Rev Neurosci 28:403–450CrossRefGoogle Scholar
  2. Boehm U, Annis J, Frank MJ, Hawkins GE, Heathcote A, Kellen D et al (2018) Estimating across-trial variability parameters of the diffusion decision model: expert advice and recommendations. J Math Psychol 87:46–75CrossRefGoogle Scholar
  3. Brown SD, Heathcote A (2008) The simplest complete model of choice response time: linear ballistic accumulation. Cogn Psychol 57(3):153–178CrossRefGoogle Scholar
  4. Bush G, Shin LM (2006) The Multi-Source Interference Task: an fMRI task that reliably activates the cingulo-frontal-parietal cognitive/attention network. Nat Protoc 1(1):308–313CrossRefGoogle Scholar
  5. Bush G, Spencer TJ, Holmes J, Shin LM, Valera EM, Seidman LJ, Makris N, Surman C, Aleardi M, Mick E, Biederman J (2008) Functional magnetic resonance imaging of methylphenidate and placebo in attention-deficit/hyperactivity disorder during the multi-source interference task. Arch Gen Psychiatry 65(1):102–114CrossRefGoogle Scholar
  6. Clatworthy PL, Lewis SJ, Brichard L, Hong YT, Izquierdo D, Clark L et al (2009) Dopamine release in dissociable striatal subregions predicts the different effects of oral methylphenidate on reversal learning and spatial working memory. J Neurosci 29(15):4690–4696CrossRefGoogle Scholar
  7. Coghill DR, Seth S, Pedroso S, Usala T, Currie J, Gagliano A (2014) Effects of methylphenidate on cognitive functions in children and adolescents with attention-deficit/hyperactivity disorder: evidence from a systematic review and a meta-analysis. Biol Psychiatry 76(8):603–615CrossRefGoogle Scholar
  8. de Jong R, Liang CC, Lauber E (1994) Conditional and unconditional automaticity: a dual-process model of effects of spatial stimulus-response correspondence. J Exp Psychol Hum Percept Perform 20(4):731–750CrossRefGoogle Scholar
  9. Donkin C, Brown SD, Heathcote A (2009) The overconstraint of response time models: rethinking the scaling problem. Psychon Bull Rev 16(6):1129–1135CrossRefGoogle Scholar
  10. Dutilh G, Vandekerckhove J, Tuerlinckx F, Wagenmakers EJ (2009) A diffusion model decomposition of the practice effect. Psychon Bull Rev 16(6):1026–1036CrossRefGoogle Scholar
  11. Eriksen BA, Eriksen CW (1974) Effects of noise letters upon the identification of a target letter in a nonsearch task. Percept Psychophys 16(1):143–149CrossRefGoogle Scholar
  12. 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):911–920CrossRefGoogle Scholar
  13. Gelman A, Meng XL, Stern H (1996) Posterior predictive assessment of model fitness via realized discrepancies. Stat Sin 6:733−807Google Scholar
  14. Gelman A, Rubin DB (1992) Inference from iterative simulation using multiple sequences. Stat Sci 7(4):457–472CrossRefGoogle Scholar
  15. Gold JI, Shadlen MN (2007) The neural basis of decision making. Annu Rev Neurosci 30:535–574CrossRefGoogle Scholar
  16. Gutenkunst RN, Waterfall JJ, Casey FP, Brown KS, Myers CR, Sethna JP (2007) Universally sloppy parameter sensitivities in systems biology models. PLoS Comput Biol 3(10):e189CrossRefGoogle Scholar
  17. Hanes DP, Schall JD (1996) Neural control of voluntary movement initiation. Science 274(5286):427–430CrossRefGoogle Scholar
  18. Hawk LW Jr, Fosco WD, Colder CR, Waxmonsky JG, Pelham WE Jr, Rosch KS (2018) How do stimulant treatments for ADHD work? Evidence for mediation by improved cognition. J Child Psychol Psychiatry, 59(12), 1271−1281Google Scholar
  19. Heathcote A, Brown SD, Wagenmakers EJ (2015a) An introduction to good practices in cognitive modeling. In: An introduction to model-based cognitive neuroscience. Springer, New York, pp 25–48Google Scholar
  20. Heathcote A, Hannah K, Matzke D (under review) Priming and variable control in choice conflict tasks. Unpublished manuscriptGoogle Scholar
  21. Heathcote A, Lin YS, Reynolds A, Strickland L, Gretton M, Matzke D (2018) Dynamic models of choice. Behav Res Methods: 51(2), 961–985Google Scholar
  22. Heathcote A, Loft S, Remington RW (2015b) Slow down and remember to remember! A delay theory of prospective memory costs. Psychol Rev 122:367–410CrossRefGoogle Scholar
  23. Hedge A, Marsh NWA (1975) The effect of irrelevant spatial correspondences on two-choice response-time. Acta Psychol 39(6):427–439CrossRefGoogle Scholar
  24. Heathcote A, Suraev A, Curley S, Gong Q, Love J, Michie PT (2015c) Decision processes and the slowing of simple choices in schizophrenia. J Abnorm Psychol 124(4):961–974CrossRefGoogle Scholar
  25. Hübner R, Steinhauser M, Lehle C (2010) A dual-stage two-phase model of selective attention. Psychol Rev 117(3):759–784CrossRefGoogle Scholar
  26. 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):685–710CrossRefGoogle Scholar
  27. Kelly SP, O’Connell RG (2013) Internal and external influences on the rate of sensory evidence accumulation in the human brain. J Neurosci 33(50):19434–19441CrossRefGoogle Scholar
  28. Killeen PR, Russell VA, Sergeant JA (2013) A behavioral neuroenergetics theory of ADHD. Neurosci Biobehav Rev 37(4):625–657CrossRefGoogle Scholar
  29. Kolossa A, Kopp B (2018) Data quality over data quantity in computational cognitive neuroscience. NeuroImage 172:775–785CrossRefGoogle Scholar
  30. Loughnane GM, Brosnan MB, Barnes JJ, Dean A, Nandam SL, O’Connell RG, Bellgrove MA (2019) Catecholamine modulation of evidence accumulation during perceptual decision formation: a randomized trial. J Cogn Neurosci 31(7):1044–1053Google Scholar
  31. Pietrzak RH, Mollica CM, Maruff P, Snyder PJ (2006) Cognitive effects of immediate-release methylphenidate in children with attention-deficit/hyperactivity disorder. Neurosci Biobehav Rev 30(8):1225–1245CrossRefGoogle Scholar
  32. Pliszka S, AACAP Work Group on Quality Issues (2007) Practice parameter for the assessment and treatment of children and adolescents with attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry 46(7):894–921CrossRefGoogle Scholar
  33. Ratcliff R, McKoon G (2008) The diffusion decision model: theory and data for two-choice decision tasks. Neural Comput 20(4):873–922CrossRefGoogle Scholar
  34. Reid MK, Borkowski JG (1984) Effects of methylphenidate (Ritalin) on information processing in hyperactive children. J Abnorm Child Psychol 12(1):169–185CrossRefGoogle Scholar
  35. Rosch KS, Fosco WD, Pelham WE, Waxmonsky JG, Bubnik MG, Hawk LW (2016) Reinforcement and stimulant medication ameliorate deficient response inhibition in children with attention-deficit/hyperactivity disorder. J Abnorm Child Psychol 44(2):309–321CrossRefGoogle Scholar
  36. Schlösser RGM, Nenadic I, Wagner G, Zysset S, Koch K, Sauer H (2009) Dopaminergic modulation of brain systems subserving decision making under uncertainty: a study with fMRI and methylphenidate challenge. Synapse 63(5):429–442CrossRefGoogle Scholar
  37. Sikström S, Söderlund G (2007) Stimulus-dependent dopamine release in attention-deficit/hyperactivity disorder. Psychol Rev 114(4):1047–1075CrossRefGoogle Scholar
  38. Smith PL, Ratcliff R (2004) Psychology and neurobiology of simple decisions. Trends Neurosci 27(3):161–168CrossRefGoogle Scholar
  39. Solanto MV (1998) Neuropsychopharmacological mechanisms of stimulant drug action in attention-deficit hyperactivity disorder: a review and integration. Behav Brain Res 94(1):127–152CrossRefGoogle Scholar
  40. Stuhec M, Munda B, Svab V, Locatelli I (2015) Comparative efficacy and acceptability of atomoxetine, lisdexamfetamine, bupropion and methylphenidate in treatment of attention deficit hyperactivity disorder in children and adolescents: a meta-analysis with focus on bupropion. J Affect Disord 178:149–159CrossRefGoogle Scholar
  41. Strand MT, Hawk LW, Bubnik M, Shiels K, Pelham WE, Waxmonsky JG (2012) Improving working memory in children with attention-deficit/hyperactivity disorder: the separate and combined effects of incentives and stimulant medication. J Abnorm Child Psychol 40(7):1193–1207CrossRefGoogle Scholar
  42. Turner BM, Sederberg PB, Brown SD, Steyvers M (2013) A method for efficiently sampling from distributions with correlated dimensions. Psychol Methods 18(3):368–384CrossRefGoogle Scholar
  43. Ulrich R, Schröter H, Leuthold H, Birngruber T (2015) Automatic and controlled stimulus processing in conflict tasks: superimposed diffusion processes and delta functions. Cogn Psychol 78:148–174CrossRefGoogle Scholar
  44. Usher M, McClelland JL (2001) The time course of perceptual choice: the leaky, competing accumulator model. Psychol Rev 108(3):550–592CrossRefGoogle Scholar
  45. Weigard A, Huang-Pollock C, Brown S, Heathcote A (2018) Testing formal predictions of neuroscientific theories of ADHD with a cognitive model–based approach. J Abnorm Psychol 127(5):529–539CrossRefGoogle Scholar
  46. White CN, Ratcliff R, Starns JJ (2011) Diffusion models of the flanker task: discrete versus gradual attentional selection. Cogn Psychol 63(4):210–238CrossRefGoogle Scholar
  47. White CN, Servant M, Logan GD (2018) Testing the validity of conflict drift-diffusion models for use in estimating cognitive processes: a parameter-recovery study. Psychon Bull Rev 25(1):286–301CrossRefGoogle Scholar
  48. Willcutt EG, Doyle AE, Nigg JT, Faraone SV, Pennington BF (2005) Validity of the executive function theory of attention-deficit/hyperactivity disorder: a meta-analytic review. Biol Psychiatry 57(11):1336–1346CrossRefGoogle Scholar
  49. Winkel J, Hawkins GE, Ivry RB, Brown SD, Cools R, Forstmann BU (2016) Focal striatum lesions impair cautiousness in humans. Cortex 85:37–45CrossRefGoogle Scholar
  50. Ziegler S, Pedersen ML, Mowinckel AM, Biele G (2016) Modelling ADHD: a review of ADHD theories through their predictions for computational models of decision-making and reinforcement learning. Neurosci Biobehav Rev 71:633–656CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of PsychiatryUniversity of MichiganAnn ArborUSA
  2. 2.Addiction CenterUniversity of MichiganAnn ArborUSA
  3. 3.School of MedicineUniversity of TasmaniaHobartAustralia

Personalised recommendations