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Expectations and violations: delineating the neural network of proactive inhibitory control.

ABSTRACT: The ability to stop a prepared response (reactive inhibition) appears to depend on the degree to which stopping is expected (proactive inhibition). Functional MRI studies have shown that activation during proactive and reactive inhibition overlaps, suggesting that the whole neural network for reactive inhibition becomes already activated in anticipation of stopping. However, these studies measured proactive inhibition as the effect of stop-signal probability on activation during go trials. Therefore, activation could reflect expectation of a stop-signal (evoked by the stop-signal probability cue), but also violation of this expectation because stop-signals do not occur on go trials. We addressed this problem, using a stop-signal task in which the stop-signal probability cue and the go-signal were separated in time. Hence, we could separate activation during the cue, reflecting expectation of the stop-signal, from activation during the go-signal, reflecting expectation of the stop-signal or violation of that expectation. During the cue, the striatum, the supplementary motor complex (SMC), and the midbrain activated. During the go-signal, the right inferior parietal cortex (IPC) and the right inferior frontal cortex (IFC) activated. These findings suggest that the neural network previously associated with proactive inhibition can be subdivided into two components. One component, including the striatum, the SMC, and the midbrain, activated during the cue, implicating this network in proactive inhibition. Another component, consisting of the right IPC and the right IFC, activated during the go-signal. Rather than being involved in proactive inhibition, this network appears to be involved in processes associated with violation of expectations.

SUBMITTER: Zandbelt BB

PROVIDER: S-EPMC6869973 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Alterations in attention to cues signaling the need for inhibitory control play a significant role in a wide range of psychopathology. However, the degree to which motivational and attentional factors shape the neurocomputations of proactive inhibitory control remains poorly understood. The present study investigated how variation in monetary incentive valence and stake modulate the neurocomputational signatures of proactive inhibitory control. Adults (N = 46) completed a Stop-Signal Task (SST) with concurrent EEG recording under four conditions associated with stop performance feedback: low and high punishment (following unsuccessful stops) and low and high reward (following successful stops). A Bayesian learning model was used to infer individual's probabilistic expectations of the need to stop on each trial: P(stop). Linear mixed effects models were used to examine whether interactions between motivational valence, stake, and P(stop) parameters predicted P1 and N1 attention-related event-related potentials (ERPs) time-locked to the go-onset stimulus. We found that P1 amplitudes increased at higher levels of P(stop) in punished but not rewarded conditions, although P1 amplitude differences between punished and rewarded blocks were maximal on trials when the need to inhibit was least expected. N1 amplitudes were positively related to P(stop) in the high punishment condition (low N1 amplitude), but negatively related to P(stop) in the high reward condition (high N1 amplitude). Critically, high P(stop)-related N1 amplitude to the go-stimulus predicted behavioral stop success during the high reward block, providing evidence for the role of motivationally relevant context and inhibitory control expectations in modulating the proactive allocation of attentional resources that affect inhibitory control. These findings provide novel insights into the neurocomputational mechanisms underlying proactive inhibitory control under valence-dependent motivational contexts, setting the stage for developing motivation-based interventions that boost inhibitory control.

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Expectations and violations: delineating the neural network of proactive inhibitory control.

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