How can choice, confidence, and response times be modeled simultaneously? Here, we propose the new dynamical weighted evidence and visibility (dynWEV) model, an extension of the drift-diffusion model of decision-making, to account for choices, reaction times, and confidence simultaneously. The decision process in a binary perceptual task is described as a Wiener process accumulating sensory evidence about the choice options bounded by two constant thresholds. To account for confidence judgments, we assume a period of postdecisional accumulation of sensory evidence and parallel accumulation of information about the reliability of the present stimulus. We examined model fits in two experiments, a motion discrimination task with random dot kinematograms and a postmasked orientation discrimination task. A comparison between the dynWEV model, two-stage dynamical signal detection theory, and several versions of race models of decision-making showed that only dynWEV produced acceptable fits of choices, confidence, and reaction time. This finding suggests that confidence judgments depend not only on choice evidence but also on a parallel estimate of stimulus discriminability and postdecisional accumulation of evidence. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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对视觉感知中的选择、置信度和响应时间进行同步建模。


如何同时对选择、置信度和响应时间进行建模？在这里，我们提出了新的动态加权证据和可见性（dynWEV）模型，这是决策漂移扩散模型的扩展，可以同时考虑选择、反应时间和置信度。二元感知任务中的决策过程被描述为维纳过程，积累有关由两个常数阈值界定的选择选项的感官证据。为了解释信心判断，我们假设在决策后积累一段时间的感官证据，并同时积累有关当前刺激可靠性的信息。我们在两个实验中检查了模型拟合情况，一个是随机点运动图的运动辨别任务，另一个是后掩码方向辨别任务。 dynWEV 模型、两阶段动态信号检测理论和决策竞赛模型的几个版本之间的比较表明，只有 dynWEV 产生了可接受的选择、置信度和反应时间拟合。这一发现表明，置信度判断不仅取决于选择证据，还取决于对刺激辨别力和决策后证据积累的平行估计。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。