Spatiotemporal brain dynamics supporting the immediate automatization of inhibitory control by implementation intentions

Abstract

While cognitive interventions aiming at reinforcing intentional executive control of unwanted response showed only modest effects on impulse control disorders, the establishment of fast automatic, stimulus-driven inhibition of responses to specific events with implementation intention self-regulation strategies has proven to be an effective remediation approach. However, the neurocognitive mechanisms underlying implementation intentions remain largely unresolved. We addressed this question by comparing electrical neuroimaging analyses of event-related potentials recorded during a Go/NoGo task between groups of healthy participants receiving either standard or implementation intentions instructions on the inhibition stimuli. Inhibition performance improvements with implementation intentions were associated with a Group by Stimulus interaction 200–250 ms post-stimulus onset driven by a selective decrease in response to the inhibition stimuli within the left superior temporal gyrus, the right precuneus and the right temporo-parietal junction. We further observed that the implementation intentions group showed already at the beginning of the task the pattern of task-related functional activity reached after practice in the group having received standard instructions. We interpret our results in terms of an immediate establishment of an automatic, bottom-up form of inhibitory control by implementation intentions, supported by stimulus-driven retrieval of verbally encoded stimulus-response mapping rules, which in turn triggered inhibitory processes.

Stimulus Reward Value Interacts with Training-induced Plasticity in Inhibitory Control

“…As complementary analyses, we ran the same statistics on the RT and FA rate separately, which suggested that the effect on the IES were mainly driven by changes in RT, as repeatedly observed in previous studies on IC training (e.g. Hartmann et al, 2016;De Pretto et al, 2017): For the RT, there was a Training  Stimuli interaction: F(1,35) = 17.010; p < 0.001; g p 2 = 0.327), a main effect of training (F(1,35) = 43.865; p < 0.001; g p 2 = 0.556), but no main effect of NoGo Type (F(1,35) = 0.408; p = 0.527; g p 2 = 0.012). The same analyses were conducted on FA rate, and showed no interaction nor main effect of training (two-way mixed ANOVA Training  NoGo Type; main effect of Training: F(1,35) = 1.499; p = 0.229; g p 2 = 0.041; Interaction: F (1,35) = 0.357; p = 0.554; g p 2 = 0.010), but a small main effect of NoGo Type: F(1,35) = 6.286, p = 0.017; g p 2 = 0.152.…”

“…i) The training may first reinforce top-down inhibitory control processes, in turn helping participants to voluntarily resist impulses toward palatable food items. This mechanism predicts a modification of the brain responses to the NoGo stimuli during the implementation of the inhibition command, as indexed by the P3 ERP component 300 ms post-stimuli onset and within right ventrolateral prefrontal cortices (Manuel et al, 2010;Lenartowicz et al, 2011;Spierer et al, 2013;Berkman et al, 2014;Chavan et al, 2015;Hartmann et al, 2016;De Pretto et al, 2017). In addition, this account predicts larger behavioral improvements with appetitive than aversive pictures because higher rewarding value elicit strongermore difficult to inhibit-approach impulses.…”

Executive control training does not generalize, even when associated with plastic changes in domain-general prefrontal areas

“…The power analysis indicated that a total sample size of 42 participants was required (calculated by the G Â Power software, Faul et al, 2007). Based on previous studies with corresponding designs (De Pretto et al, 2017;Hartmann et al, 2015), we added five participants per group to compensate for potential exclusion due to e.g. bad electroencephalogram (EEG) signal, missed training sessions at home or misunderstanding of the instructions.…”

“…First, a 30-trial block was performed to acclimate the participants to the task. A 'calibration' phase of 23 trials was then completed to estimate the average RT of the participant to Go stimuli (for a similar procedure see De Pretto et al, 2017;Manuel et al, 2010). A RT threshold (RTt ¼ 110% of the mean RT during the calibration phase) was implemented so that a feedback "too late" was displayed on the screen when the participant's RT was above the RTt.…”

“…The solution space (i.e., the lead field matrix) was calculated on a realistic head model that included 3005 nodes, selected from a 6 mm  6 mm x 6 mm grid of voxels equally distributed within the gray matter of the average brain of the Montreal Neurological Institute (MNI). For the analyses in the source space, ERPs were first averaged over the period of interest identified during the time-frame (TF) wise ERP analysis for each subject and each condition separately (De Pretto et al, 2017;Sallard et al, 2018). Then, the source of the resulting 1 TF ERPs were estimated and the current densities at each solution point submitted to the same Group x Time x Stimulus statistical design as for the ERPs analyses.…”