The speed-accuracy trade-off (SAT) is the tendency for fast decisions to come at the expense of accurate performance. Evidence accumulation models such as the drift diffusion model can reproduce a variety of behavioral data related to the SAT, and their parameters have been linked to neural activities in the brain. However, our understanding of how biological neural networks realize the associated cognitive operations remains incomplete, limiting our ability to unify neurological and computational accounts of the SAT. We address this gap by developing and analyzing a biologically plausible spiking neural network that extends the drift diffusion approach. We apply our model to both perceptual and nonperceptual tasks, investigate several contextual manipulations, and validate model performance using neural and behavioral data. Behaviorally, we find that our model (a) reproduces individual response time distributions; (b) generalizes across experimental contexts, including the number of choice alternatives, speed- or accuracy-emphasis, and task difficulty; and (c) predicts accuracy data, despite being fit only to response time data. Neurally, we show that our model (a) recreates observed patterns of spiking neural activity and (b) captures age-related deficits that are consistent with the behavioral data. More broadly, our model exhibits the SAT across a variety of tasks and contexts and explains how individual differences in speed and accuracy arise from synaptic weights within a spiking neural network. Our work showcases a method for translating mathematical models into functional neural networks and demonstrates that simulating such networks permits analyses and predictions that are outside the scope of purely mathematical models. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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决策和速度-准确性权衡的尖峰神经模型。


速度-准确性权衡 （SAT） 是以牺牲准确性能为代价的快速决策的趋势。漂移扩散模型等证据积累模型可以再现与 SAT 相关的各种行为数据，并且它们的参数与大脑中的神经活动有关。然而，我们对生物神经网络如何实现相关认知操作的理解仍然不完整，这限制了我们统一 SAT 的神经学和计算账户的能力。我们通过开发和分析一种生物学上合理的脉冲神经网络来解决这一差距，该网络扩展了漂移扩散方法。我们将模型应用于感知和非感知任务，研究几种上下文操作，并使用神经和行为数据验证模型性能。从行为上讲，我们发现我们的模型 （a） 再现了个体响应时间分布;（b） 在实验环境中进行泛化，包括选择选项的数量、速度或准确性的强调以及任务难度;（c） 预测准确性数据，尽管仅适用于响应时间数据。在神经方面，我们表明我们的模型 （a） 重现了观察到的脉冲神经活动模式，并且 （b） 捕获了与行为数据一致的与年龄相关的缺陷。更广泛地说，我们的模型展示了各种任务和上下文中的 SAT，并解释了速度和准确性的个体差异是如何由脉冲神经网络中的突触权重引起的。我们的工作展示了一种将数学模型转换为函数神经网络的方法，并证明模拟此类网络允许进行超出纯数学模型范围的分析和预测。 （PsycInfo 数据库记录 （c） 2024 APA，保留所有权利）。