Perceptual decision making can be accounted for by drift-diffusion models, a class of decision-making models that assume a stochastic accumulation of evidence on each trial. Fitting response time and accuracy to a drift-diffusion model produces evidence accumulation rate and non-decision time parameter estimates that reflect cognitive processes. Our goal is to elucidate the effect of attention on visual decision making. In this study, we show that measures of attention obtained from simultaneous EEG recordings can explain per-trial evidence accumulation rates and perceptual preprocessing times during a visual decision making task. Models assuming linear relationships between diffusion model parameters and EEG measures as external inputs were fit in a single step in a hierarchical Bayesian framework. The EEG measures were features of the evoked potential (EP) to the onset of a masking noise and the onset of a task-relevant signal stimulus. Single-trial evoked EEG responses, P200s to the onsets of visual noise and N200s to the onsets of visual signal, explain single-trial evidence accumulation and preprocessing times. Within-trial evidence accumulation variance was not found to be influenced by attention to the signal or noise. Single-trial measures of attention lead to better out-of-sample predictions of accuracy and correct reaction time distributions for individual subjects.

中文翻译：

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注意力如何影响感知决策：单次试验脑电图与漂移扩散模型参数的相关性


感知决策可以通过漂移扩散模型来解释，这是一类假设每次试验的证据随机积累的决策模型。将响应时间和准确性拟合到漂移扩散模型可以产生反映认知过程的证据积累率和非决策时间参数估计。我们的目标是阐明注意力对视觉决策的影响。在这项研究中，我们表明，从同步脑电图记录中获得的注意力测量可以解释视觉决策任务期间试验期间的证据积累率和感知预处理时间。假设扩散模型参数和脑电图测量之间存在线性关系作为外部输入的模型在分层贝叶斯框架中的一步中拟合。脑电图测量是掩蔽噪声开始和任务相关信号刺激开始的诱发电位（EP）的特征。单次试验诱发脑电图反应、视觉噪声出现的 P200 和视觉信号出现的 N200，解释了单次试验证据的积累和预处理时间。未发现试验内证据积累方差受到对信号或噪声的关注的影响。单次注意力试验测量可以更好地预测个体受试者的准确度和正确的反应时间分布。