Brain Structure and Function

, Volume 224, Issue 5, pp 1911–1924 | Cite as

Validity expectancies shape the interplay of cueing and task demands during inhibitory control associated with right inferior frontal regions

  • Nico Adelhöfer
  • Christian BesteEmail author
Original Article


The neural mechanisms of inhibitory control have extensively been studied, including the effects of demands to engage in inhibitory control and the effects of valid and invalid cueing. Theoretical considerations, however, suggest that the aforementioned factors exert joined effects on response inhibition processes that are further modulated by the subject’s experience about the reliability of cue stimuli during response inhibition processes. To examine the underlying neurophysiological processes of these interactive effects we combined EEG signal decomposition with sLORETA source localization. We show that response inhibition performance is modulated by interactive effects between (1) cue information/validity, (2) demands on inhibitory control processes and (3) the subject’s experience that cue information is valid/invalid during response inhibition processes. Only if demands on inhibitory control processes are high and when participants acquainted the experience that cue information is very likely to be valid, invalid cue information compromised response inhibition performance. The neurophysiological data show that processes in the N2 time window, likely reflecting braking processes, but not stimulus-related processes during response inhibition, are modulated. It seems that braking processes cannot be sufficiently deployed if cue information that has been experienced to be highly valid turns out to be invalid in situations placing high demands on inhibitory control. Source localization data reveals that the interactive effects of the examined factors specifically modulate processes in the right inferior frontal gyrus (BA47). This provides electrophysiological evidence that the rIFG is a hub region integrating different factors modulating inhibitory control.


Response inhibition Inferior frontal gyrus EEG Source localization 



This work was supported by a Grant from the Deutsche Forschungsgemeinschaft (DFG) BE4045/26-1 to C.B.

Compliance with ethical standards

Conflict of interest

There are no conflicts of interest.

Ethical statement

The study was approved by the IRB of the TU Dresden.

Informed consent

Written informed consent was obtained from all subject before any of the study’s procedures were commenced.

Supplementary material

429_2019_1884_MOESM1_ESM.docx (14 kb)
Supplementary material 1 (DOCX 13 kb)


  1. Albert J, López-Martín S, Hinojosa JA, Carretié L (2013) Spatiotemporal characterization of response inhibition. NeuroImage 76:272–281. CrossRefGoogle Scholar
  2. Allen C, Singh KD, Verbruggen F, Chambers CD (2018) Evidence for parallel activation of the pre-supplementary motor area and inferior frontal cortex during response inhibition: a combined MEG and TMS study. R Soc Open Sci 5:171369. CrossRefGoogle Scholar
  3. Aron AR, Robbins TW, Poldrack RA (2014) Inhibition and the right inferior frontal cortex: one decade on. Trends Cogn Sci 18:177–185. CrossRefGoogle Scholar
  4. Aron AR, Cai W, Badre D, Robbins TW (2015) Evidence supports specific braking function for inferior PFC. Trends Cogn Sci (Regul Ed) 19:711–712. CrossRefGoogle Scholar
  5. Bari A, Robbins TW (2013) Inhibition and impulsivity: behavioral and neural basis of response control. Prog Neurobiol 108:44–79. CrossRefGoogle Scholar
  6. Beste C, Willemssen R, Saft C, Falkenstein M (2010) Response inhibition subprocesses and dopaminergic pathways: basal ganglia disease effects. Neuropsychologia 48:366–373. CrossRefGoogle Scholar
  7. Bianco V, Berchicci M, Perri RL et al (2017) The proactive self-control of actions: time-course of underlying brain activities. NeuroImage 156:388–393. CrossRefGoogle Scholar
  8. Bluschke A, Broschwitz F, Kohl S et al (2016) The neuronal mechanisms underlying improvement of impulsivity in ADHD by theta/beta neurofeedback. Sci Rep 6:31178. CrossRefGoogle Scholar
  9. Bluschke A, Chmielewski WX, Mückschel M et al (2017) Neuronal intra-individual variability masks response selection differences between ADHD subtypes—a need to change perspectives. Front Hum Neurosci 11:329. CrossRefGoogle Scholar
  10. Bodmer B, Mückschel M, Roessner V, Beste C (2018) Neurophysiological variability masks differences in functional neuroanatomical networks and their effectiveness to modulate response inhibition between children and adults. Brain Struct Funct 223:1797–1810. Google Scholar
  11. Boehler CN, Münte TF, Krebs RM et al (2009) Sensory MEG responses predict successful and failed inhibition in a stop-signal task. Cereb Cortex 19:134–145. CrossRefGoogle Scholar
  12. Bokura H, Yamaguchi S, Kobayashi S (2001) Electrophysiological correlates for response inhibition in a Go/NoGo task. Clin Neurophysiol 112:2224–2232CrossRefGoogle Scholar
  13. Braver TS (2012) The variable nature of cognitive control: a dual mechanisms framework. Trends Cogn Sci (Regul Ed) 16:106–113. CrossRefGoogle Scholar
  14. Chambers CD, Bellgrove MA, Gould IC et al (2007) Dissociable mechanisms of cognitive control in prefrontal and premotor cortex. J Neurophysiol 98:3638–3647. CrossRefGoogle Scholar
  15. Chi Y, Yue Z, Liu Y et al (2014) Dissociable identity- and modality-specific neural representations as revealed by cross-modal nonspatial inhibition of return. Hum Brain Mapp 35:4002–4015. CrossRefGoogle Scholar
  16. Chmielewski WX, Beste C (2016a) Perceptual conflict during sensorimotor integration processes—a neurophysiological study in response inhibition. Sci Rep 6:26289. CrossRefGoogle Scholar
  17. Chmielewski WX, Beste C (2016b) Testing interactive effects of automatic and conflict control processes during response inhibition—a system neurophysiological study. NeuroImage. Google Scholar
  18. Chmielewski WX, Mückschel M, Beste C (2018) Response selection codes in neurophysiological data predict conjoint effects of controlled and automatic processes during response inhibition. Hum Brain Mapp 39:1839–1849. CrossRefGoogle Scholar
  19. d’Acremont M, Schultz W, Bossaerts P (2013) The human brain encodes event frequencies while forming subjective beliefs. J Neurosci 33:10887–10897. CrossRefGoogle Scholar
  20. Di Russo F, Lucci G, Sulpizio V et al (2016) Spatiotemporal brain mapping during preparation, perception, and action. NeuroImage 126:1–14. CrossRefGoogle Scholar
  21. Dippel G, Beste C (2015) A causal role of the right inferior frontal cortex in the strategies of multi-component behaviour. Nat Commun. Google Scholar
  22. Dippel G, Chmielewski W, Mückschel M, Beste C (2016) Response mode-dependent differences in neurofunctional networks during response inhibition: an EEG-beamforming study. Brain Struct Funct 221:4091–4101. CrossRefGoogle Scholar
  23. Dippel G, Mückschel M, Ziemssen T, Beste C (2017) Demands on response inhibition processes determine modulations of theta band activity in superior frontal areas and correlations with pupillometry—implications for the norepinephrine system during inhibitory control. NeuroImage 157:575–585. CrossRefGoogle Scholar
  24. Dockree PM, Kelly SP, Roche RAP et al (2004) Behavioural and physiological impairments of sustained attention after traumatic brain injury. Brain Res Cogn Brain Res 20:403–414. CrossRefGoogle Scholar
  25. Dockree PM, Bellgrove MA, O’Keeffe FM et al (2006) Sustained attention in traumatic brain injury (TBI) and healthy controls: enhanced sensitivity with dual-task load. Exp Brain Res 168:218–229. CrossRefGoogle Scholar
  26. Dodds CM, Morein-Zamir S, Robbins TW (2011) Dissociating inhibition, attention, and response control in the frontoparietal network using functional magnetic resonance imaging. Cereb Cortex 21:1155–1165. CrossRefGoogle Scholar
  27. Falkenstein M, Hoormann J, Hohnsbein J (1999) ERP components in Go/Nogo tasks and their relation to inhibition. Acta Psychol (Amst) 101:267–291CrossRefGoogle Scholar
  28. Folstein JR, Van Petten C (2008) Influence of cognitive control and mismatch on the N2 component of the ERP: a review. Psychophysiology 45:152–170. CrossRefGoogle Scholar
  29. Friedrich J, Mückschel M, Beste C (2018) Specific properties of the SI and SII somatosensory areas and their effects on motor control: a system neurophysiological study. Brain Struct Funct 223:687–699. CrossRefGoogle Scholar
  30. Friston K, FitzGerald T, Rigoli F et al (2017) Active inference: a process theory. Neural Comput 29:1–49. CrossRefGoogle Scholar
  31. Gillies AJ, Willshaw DJ (1998) A massively connected subthalamic nucleus leads to the generation of widespread pulses. Proc R Soc Lond B Biol Sci 265:2101–2109. CrossRefGoogle Scholar
  32. Hampshire A (2015) Putting the brakes on inhibitory models of frontal lobe function. NeuroImage 113:340–355. CrossRefGoogle Scholar
  33. Hampshire A, Sharp DJ (2015) Contrasting network and modular perspectives on inhibitory control. Trends Cogn Sci 19:445–452. CrossRefGoogle Scholar
  34. Hampshire A, Chamberlain SR, Monti MM et al (2010) The role of the right inferior frontal gyrus: inhibition and attentional control. NeuroImage 50:1313–1319. CrossRefGoogle Scholar
  35. Helton WS (2009) Impulsive responding and the sustained attention to response task. J Clin Exp Neuropsychol 31:39–47. CrossRefGoogle Scholar
  36. Helton WS, Hollander TD, Warm JS et al (2005) Signal regularity and the mindlessness model of vigilance. Br J Psychol 96:249–261. CrossRefGoogle Scholar
  37. Herrmann CS, Knight RT (2001) Mechanisms of human attention: event-related potentials and oscillations. Neurosci Biobehav Rev 25:465–476CrossRefGoogle Scholar
  38. Hong X, Wang Y, Sun J et al (2017) Segregating top-down selective attention from response inhibition in a spatial cueing Go/NoGo task: an ERP and source localization study. Sci Rep 7:9662. CrossRefGoogle Scholar
  39. Huster RJ, Enriquez-Geppert S, Lavallee CF et al (2013) Electroencephalography of response inhibition tasks: functional networks and cognitive contributions. Int J Psychophysiol 87:217–233. CrossRefGoogle Scholar
  40. Huster RJ, Plis SM, Calhoun VD (2015) Group-level component analyses of EEG: validation and evaluation. Front Neurosci 9:254. CrossRefGoogle Scholar
  41. Kayser J, Tenke CE (2015) On the benefits of using surface Laplacian (current source density) methodology in electrophysiology. Int J Psychophysiol 97:171–173. CrossRefGoogle Scholar
  42. Lenartowicz A, Verbruggen F, Logan GD, Poldrack RA (2011) Inhibition-related activation in the right inferior frontal gyrus in the absence of inhibitory cues. J Cogn Neurosci 23:3388–3399. CrossRefGoogle Scholar
  43. Liebrand M, Pein I, Tzvi E, Krämer UM (2017) Temporal dynamics of proactive and reactive motor inhibition. Front Hum Neurosci 11:204. CrossRefGoogle Scholar
  44. Marco-Pallarés J, Grau C, Ruffini G (2005) Combined ICA-LORETA analysis of mismatch negativity. NeuroImage 25:471–477. CrossRefGoogle Scholar
  45. Masson MEJ (2011) A tutorial on a practical Bayesian alternative to null-hypothesis significance testing. Behav Res Methods 43:679–690. CrossRefGoogle Scholar
  46. 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 35:196–204. CrossRefGoogle Scholar
  47. Meyniel F, Dehaene S (2017) Brain networks for confidence weighting and hierarchical inference during probabilistic learning. Proc Natl Acad Sci USA 114:E3859–E3868. CrossRefGoogle Scholar
  48. Mückschel M, Stock A-K, Beste C (2014) Psychophysiological mechanisms of interindividual differences in goal activation modes during action cascading. Cereb Cortex 24:2120–2129. CrossRefGoogle Scholar
  49. Mückschel M, Chmielewski W, Ziemssen T, Beste C (2017a) The norepinephrine system shows information-content specific properties during cognitive control—evidence from EEG and pupillary responses. NeuroImage 149:44–52. CrossRefGoogle Scholar
  50. Mückschel M, Dippel G, Beste C (2017b) Distinguishing stimulus and response codes in theta oscillations in prefrontal areas during inhibitory control of automated responses. Hum Brain Mapp 38:5681–5690. CrossRefGoogle Scholar
  51. Nichols TE, Holmes AP (2002) Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp 15:1–25. CrossRefGoogle Scholar
  52. Nieuwenhuis S, Yeung N, Cohen JD (2004) Stimulus modality, perceptual overlap, and the go/no-go N2. Psychophysiology 41:157–160. CrossRefGoogle Scholar
  53. Nunez PL, Pilgreen KL (1991) The spline-Laplacian in clinical neurophysiology: a method to improve EEG spatial resolution. J Clin Neurophysiol 8:397–413CrossRefGoogle Scholar
  54. Nunez PL, Srinivasan R, Westdorp AF et al (1997) EEG coherency. I: statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales. Electroencephalogr Clin Neurophysiol 103:499–515CrossRefGoogle Scholar
  55. Ostwald D, Spitzer B, Guggenmos M et al (2012) Evidence for neural encoding of Bayesian surprise in human somatosensation. NeuroImage 62:177–188. CrossRefGoogle Scholar
  56. Ouyang G, Herzmann G, Zhou C, Sommer W (2011) Residue iteration decomposition (RIDE): a new method to separate ERP components on the basis of latency variability in single trials. Psychophysiology 48:1631–1647. CrossRefGoogle Scholar
  57. Ouyang G, Schacht A, Zhou C, Sommer W (2013) Overcoming limitations of the ERP method with Residue Iteration Decomposition (RIDE): a demonstration in go/no-go experiments. Psychophysiology 50:253–265. CrossRefGoogle Scholar
  58. Ouyang G, Sommer W, Zhou C (2015a) A toolbox for residue iteration decomposition (RIDE)—a method for the decomposition, reconstruction, and single trial analysis of event related potentials. J Neurosci Methods 250:7–21. CrossRefGoogle Scholar
  59. Ouyang G, Sommer W, Zhou C (2015b) Updating and validating a new framework for restoring and analyzing latency-variable ERP components from single trials with residue iteration decomposition (RIDE). Psychophysiology 52:839–856. CrossRefGoogle Scholar
  60. Ouyang G, Hildebrandt A, Sommer W, Zhou C (2017) Exploiting the intra-subject latency variability from single-trial event-related potentials in the P3 time range: a review and comparative evaluation of methods. Neurosci Biobehav Rev 75:1–21. CrossRefGoogle Scholar
  61. Pascual-Marqui RD (2002) Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find Exp Clin Pharmacol 24(Suppl D):5–12Google Scholar
  62. Quetscher C, Yildiz A, Dharmadhikari S et al (2015) Striatal GABA-MRS predicts response inhibition performance and its cortical electrophysiological correlates. Brain Struct Funct 220:3555–3564. CrossRefGoogle Scholar
  63. Raftery AE (1995) Bayesian model selection in social research. In: Mardsen PV (ed) Sociological methodology. Blackwell, Cambridge, pp 11–196Google Scholar
  64. Randall WM, Smith JL (2011) Conflict and inhibition in the cued-Go/NoGo task. Clin Neurophysiol 122:2400–2407. CrossRefGoogle Scholar
  65. Sekihara K, Sahani M, Nagarajan SS (2005) Localization bias and spatial resolution of adaptive and non-adaptive spatial filters for MEG source reconstruction. NeuroImage 25:1056–1067. CrossRefGoogle Scholar
  66. Sharp DJ, Bonnelle V, De Boissezon X et al (2010) Distinct frontal systems for response inhibition, attentional capture, and error processing. Proc Natl Acad Sci USA 107:6106–6111. CrossRefGoogle Scholar
  67. Simmonds DJ, Pekar JJ, Mostofsky SH (2008) Meta-analysis of Go/No-go tasks demonstrating that fMRI activation associated with response inhibition is task-dependent. Neuropsychologia 46:224–232. CrossRefGoogle Scholar
  68. Smith JL, Johnstone SJ, Barry RJ (2007) Response priming in the Go/NoGo task: the N2 reflects neither inhibition nor conflict. Clin Neurophysiol 118:343–355. CrossRefGoogle Scholar
  69. Smith JL, Johnstone SJ, Barry RJ (2008) Movement-related potentials in the Go/NoGo task: the P3 reflects both cognitive and motor inhibition. Clin Neurophysiol 119:704–714. CrossRefGoogle Scholar
  70. Snowden RJ, Willey J, Muir JL (2001) Visuospatial attention: the role of target contrast and task difficulty when assessing the effects of cues. Perception 30:983–991. CrossRefGoogle Scholar
  71. Stevenson H, Russell PN, Helton WS (2011) Search asymmetry, sustained attention, and response inhibition. Brain Cogn 77:215–222. CrossRefGoogle Scholar
  72. Stock A-K, Popescu F, Neuhaus AH, Beste C (2016) Single-subject prediction of response inhibition behavior by event-related potentials. J Neurophysiol 115:1252–1262. CrossRefGoogle Scholar
  73. Stock A-K, Gohil K, Beste C (2017a) Blocking effects in non-conditioned goal-directed behaviour. Brain Struct Funct 222:2807–2818. CrossRefGoogle Scholar
  74. Stock A-K, Gohil K, Huster RJ, Beste C (2017b) On the effects of multimodal information integration in multitasking. Sci Rep 7:4927. CrossRefGoogle Scholar
  75. Verleger R, Metzner MF, Ouyang G et al (2014) Testing the stimulus-to-response bridging function of the oddball-P3 by delayed response signals and residue iteration decomposition (RIDE). NeuroImage 100:271–280. CrossRefGoogle Scholar
  76. Verleger R, Siller B, Ouyang G, Śmigasiewicz K (2017) Effects on P3 of spreading targets and response prompts apart. Biol Psychol 126:1–11. CrossRefGoogle Scholar
  77. Vossel S, Thiel CM, Fink GR (2006) Cue validity modulates the neural correlates of covert endogenous orienting of attention in parietal and frontal cortex. NeuroImage 32:1257–1264. CrossRefGoogle Scholar
  78. Vuillier L, Bryce D, Szücs D, Whitebread D (2016) The maturation of interference suppression and response inhibition: ERP analysis of a cued Go/Nogo task. PLoS ONE 11:e0165697. CrossRefGoogle Scholar
  79. Wessel JR (2018) Prepotent motor activity and inhibitory control demands in different variants of the go/no-go paradigm. Psychophysiology 55:e12871. CrossRefGoogle Scholar
  80. Yu AJ, Dayan P (2005) Uncertainty, neuromodulation, and attention. Neuron 46:681–692. CrossRefGoogle Scholar

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

Authors and Affiliations

  1. 1.Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of MedicineTU DresdenDresdenGermany

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