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.

对表明需要抑制控制的线索的注意力的改变在广泛的精神病理学中发挥着重要作用。然而，动机和注意力因素在多大程度上影响主动抑制控制的神经计算仍然知之甚少。本研究调查了货币激励价和股权的变化如何调节主动抑制控制的神经计算特征。成年人 （氮= 46）在与停止表现反馈相关的四种条件下完成了同时进行脑电图记录的停止信号任务（SST）：低和高惩罚（在不成功停止之后）以及低和高奖励（在成功停止之后）。使用贝叶斯学习模型来推断个人对每次试验是否需要停止的概率期望：P(stop)。使用线性混合效应模型来检查动机效价、利益和 P（停止）参数之间的相互作用是否可以预测与持续刺激时间锁定的 P1 和 N1 注意力相关事件相关电位 (ERP)。我们发现，在惩罚而非奖励条件下，P(stop) 水平较高时，P1 幅度会增加，尽管在试验中，当最不需要抑制时，惩罚块和奖励块之间的 P1 幅度差异最大。在高惩罚条件下（低 N1 幅度），N1 幅度与 P（停止）正相关，但在高奖励条件（高 N1 幅度）下，与 P（停止）负相关。至关重要的是，与去刺激相关的高 P（停止）N1 幅度可预测高奖励块期间行为停止的成功，这为动机相关背景和抑制控制期望在调节影响抑制的注意力资源的主动分配中的作用提供了证据控制。这些发现为价依赖动机环境下主动抑制控制的神经计算机制提供了新的见解，为开发基于动机的干预措施来促进抑制控制奠定了基础。