Decision-making is a fundamental human activity requiring explanation at the neurocognitive level. Current theoretical frameworks assume that, during sensory-based decision-making, the stimulus is sampled sequentially. The resulting evidence is accumulated over time as a decision variable until a threshold is reached and a response is initiated. Several neural signals, including the centroparietal positivity (CPP) measured from the human electroencephalogram (EEG), appear to display the accumulation-to-bound profile associated with the decision variable. Here, we evaluate the putative computational role of the CPP as a model-derived accumulation-to-bound signal, focussing on point-by-point correspondence between model predictions and data in order to go beyond simple summary measures like average slope. In two experiments, we explored the CPP under two manipulations (namely non-stationary evidence and probabilistic decision biases) that complement one another by targeting the shape and amplitude of accumulation respectively. We fit sequential sampling models to the behavioural data, and used the resulting parameters to simulate the decision variable, before directly comparing the simulated profile to the CPP waveform. In both experiments, model predictions deviated from our naïve expectations, yet showed similarities with the neurodynamic data, illustrating the importance of a formal modelling approach. The CPP appears to arise from brain processes that implement a decision variable (as formalised in sequential-sampling models) and may therefore inform our understanding of decision-making at both the representational and implementational levels of analysis, but at this point it is uncertain whether a single model can explain how the CPP varies across different kinds of task manipulation.

中文翻译：

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当跟踪有偏见和随时间变化的感官证据时，中心顶活动反映了决策变量

决策是一项基本的人类活动，需要在神经认知层面进行解释。当前的理论框架假设，在基于感官的决策过程中，刺激是按顺序采样的。所得证据作为决策变量随时间累积，直到达到阈值并启动响应。几个神经信号，包括从人类脑电图 (EEG) 测量的中心顶叶阳性 (CPP)，似乎显示了与决策变量相关的累积到结合曲线。在这里，我们将 CPP 的推定计算作用评估为模型衍生的累积到绑定信号，重点关注模型预测和数据之间的逐点对应关系，以超越平均斜率等简单的汇总度量。在两个实验中，我们在两种操作（即非平稳证据和概率决策偏差）下探索了 CPP，它们通过分别针对累积的形状和幅度来相互补充。我们将连续采样模型拟合到行为数据中，并使用所得参数来模拟决策变量，然后将模拟的轮廓与 CPP 波形直接进行比较。在这两个实验中，模型预测都偏离了我们的天真预期，但显示出与神经动力学数据的相似性，说明了正式建模方法的重要性。CPP 似乎来自执行决策变量的大脑过程（如顺序采样模型中的形式化），因此可能会告知我们在分析的代表性和实施级别上对决策的理解，