The dynamics of decision-making have been widely studied over the past several decades through the lens of an overarching theory called sequential sampling theory (SST). Within SST, choices are represented as accumulators, each of which races toward a decision boundary by drawing stochastic samples of evidence through time. Although progress has been made in understanding how decisions are made within the SST framework, considerable debate centers on whether the accumulators exhibit dependency during the evidence accumulation process; namely, whether accumulators are independent, fully dependent, or partially dependent. To evaluate which type of dependency is the most plausible representation of human decision-making, we applied a novel twist on two classic perceptual tasks; namely, in addition to the classic paradigm (i.e., the unequal-evidence conditions), we used stimuli that provided different magnitudes of equal-evidence (i.e., the equal-evidence conditions). In equal-evidence conditions, response times systematically decreased with increase in the magnitude of evidence, whereas in unequal-evidence conditions, response times systematically increased as the difference in evidence between the two alternatives decreased. We designed a spectrum of models that ranged from independent accumulation to fully dependent accumulation, while also examining the effects of within-trial and between-trial variability (BTV). We then fit the set of models to our two experiments and found that models instantiating the principles of partial dependency provided the best fit to the data. Our results further suggest that mechanisms inducing partial dependency, such as lateral inhibition, are beneficial for understanding complex decision-making dynamics, even when the task is relatively simple. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

中文翻译：

---

同等证据感知任务表明互动竞争在决策制定中的关键作用。

在过去的几十年中，决策的动态已经通过称为顺序抽样理论 (SST) 的总体理论进行了广泛的研究。在 SST 中，选择被表示为累加器，每个选择都通过随时间抽取随机证据样本而朝着决策边界前进。尽管在理解如何在 SST 框架内做出决策方面取得了进展，但大量争论集中在累积器是否在证据积累过程中表现出依赖性；即，累加器是独立的、完全依赖的还是部分依赖的。为了评估哪种类型的依赖最能代表人类决策，我们在两个经典的感知任务上应用了一个新的转折；即，除了经典范式（即，在不等证据条件下），我们使用了提供不同程度的等证据的刺激（即等证据条件）。在等证据条件下，响应时间随着证据量的增加而系统地减少，而在不等证据条件下，响应时间随着两种备选方案之间证据差异的减少而系统地增加。我们设计了从独立累积到完全依赖累积的一系列模型，同时还检查了试验内和试验间变异性 (BTV) 的影响。然后，我们将这组模型拟合到我们的两个实验中，并发现实例化部分依赖原则的模型提供了对数据的最佳拟合。我们的研究结果进一步表明，诱导部分依赖的机制，例如侧抑制，有利于理解复杂的决策动态，即使任务相对简单。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）