IF 5.1 1区 心理学 Q1 PSYCHOLOGY
Peter Duggins, Chris Eliasmith
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引用次数: 0

摘要

速度-准确性权衡(SAT)是指快速决策以牺牲准确性为代价的趋势。漂移扩散模型等证据积累模型可以重现与 SAT 相关的各种行为数据,其参数也与大脑神经活动相关联。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不完整,这限制了我们将 SAT 的神经和计算描述统一起来的能力。为了弥补这一缺陷,我们开发并分析了一种生物学上可信的尖峰神经网络,并对漂移扩散方法进行了扩展。我们将模型应用于知觉和非知觉任务,研究了几种情境操作,并利用神经和行为数据验证了模型的性能。在行为学上,我们发现我们的模型(a)再现了个体的反应时间分布;(b)在不同的实验情境下具有普遍性,包括选择替代方案的数量、速度或准确性强调以及任务难度;以及(c)尽管仅拟合了反应时间数据,但仍能预测准确性数据。在神经方面,我们证明了我们的模型(a)再现了观察到的尖峰神经活动模式;(b)捕捉到了与年龄相关的缺陷,这些缺陷与行为数据是一致的。更广泛地说,我们的模型在各种任务和情境中都表现出了 SAT,并解释了速度和准确性方面的个体差异是如何通过尖峰神经网络中的突触权重产生的。我们的工作展示了一种将数学模型转化为功能神经网络的方法,并证明模拟这种网络可以进行纯粹数学模型范围之外的分析和预测。(PsycInfo Database Record (c) 2024 APA, 版权所有)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A spiking neural model of decision making and the speed-accuracy trade-off.

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).

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来源期刊
Psychological review
Psychological review 医学-心理学
CiteScore
9.70
自引率
5.60%
发文量
97
期刊介绍: Psychological Review publishes articles that make important theoretical contributions to any area of scientific psychology, including systematic evaluation of alternative theories.
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