Of Rodents and Primates: Time-Variant Gain in Drift-Diffusion Decision Models
Of Rodents and Primates: Time-Variant Gain in Drift-Diffusion Decision Models
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啮齿动物和灵长类动物：漂移扩散决策模型中的时变增益
DOI：
10.1007/s42113-023-00194-1
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Asadpour A
中科院分区：
文献类型：
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作者：
Asadpour A
决策的序贯抽样模型涉及随着时间的推移而积累的证据，并已成功地捕捉选择行为。一个流行的模型是漂移扩散模型（DDM）。为了捕获选择反应时间（RT）的更精细方面，已经在DDM中实现了表示紧迫性信号的时变增益特征，其可以表现出比正确RT更慢的错误RT。然而，时变增益通常在DDM的信号和噪声特征上实现，假设增加漂移率上的增益（由于紧急性）类似于具有塌陷决策界限的DDM。因此，目前还不清楚增益对信号或噪声特征的影响是否会导致不同的选择行为。这项工作提出了一种替代DDM变体，专注于时变增益机制的影响，模型简约的约束。具体而言，使用大鼠，猴子和人类的选择行为的计算模型，我们系统地表明，仅在DDM的噪声的时变增益足以产生较慢的错误RT，如在猴子，而仅在漂移率的时变增益导致更快的错误RT，如在啮齿动物。我们还发现人类中时变增益的影响很小。通过突出这些模式，本研究强调了群体水平建模在捕捉总体趋势和跨物种一致影响方面的实用性。因此，DDM的不同组件的时变增益可以导致不同的选择行为，揭示了不同物种的基本时变增益机制，并可用于系统的数据拟合。
Sequential sampling models of decision-making involve evidence accumulation over time and have been successful in capturing choice behaviour. A popular model is the drift–diffusion model (DDM). To capture the finer aspects of choice reaction times (RTs), time-variant gain features representing urgency signals have been implemented in DDM that can exhibit slower error RTs than correct RTs. However, time-variant gain is often implemented on both DDM’s signal and noise features, with the assumption that increasing gain on the drift rate (due to urgency) is similar to DDM with collapsing decision bounds. Hence, it is unclear whether gain effects on just the signal or noise feature can lead to a different choice behaviour. This work presents an alternative DDM variant, focusing on the implications of time-variant gain mechanisms, constrained by model parsimony. Specifically, using computational modelling of choice behaviour of rats, monkeys, and humans, we systematically showed that time-variant gain only on the DDM’s noise was sufficient to produce slower error RTs, as in monkeys, while time-variant gain only on drift rate leads to faster error RTs, as in rodents. We also found minimal effects of time-variant gain in humans. By highlighting these patterns, this study underscores the utility of group-level modelling in capturing general trends and effects consistent across species. Thus, time-variant gain on DDM’s different components can lead to different choice behaviours, shed light on the underlying time-variant gain mechanisms for different species, and can be used for systematic data fitting.