Evidence accumulation models (EAMs) have become the dominant models of speeded decision making, which are able to decompose choices and response times into cognitive parameters that drive the decision process. Several models within the EAM framework contain fundamentally different ideas of how the decision making process operates, though previous assessments have found that these models display a high level of mimicry, which has hindered the ability of researchers to contrast these different theoretical viewpoints. Our study introduces a neglected phenomenon that we term "double responding", which can help to further constrain these models. We show that double responding produces several interesting benchmarks, and that the predictions of different EAMs can be distinguished in standard experiment paradigms when they are constrained to account for the choice response time distributions and double responding behaviour in unison. Our findings suggest that lateral inhibition (e.g., the leaky-competing accumulator) provides models with a universal ability to make accurate predictions for these data. Furthermore, only models containing feed-forward inhibition (e.g., the diffusion model) performed poorly under both of our proposed extensions of the standard EAM framework to double responding, suggesting a general inability of feed-forward inhibition to accurately predict these data. We believe that our study provides an important step forward in further constraining models of speeded decision making, though additional research on double responding is required before broad conclusions are made about which models provide the best explanation of the underlying decision-making process.

中文翻译：

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双响应：快速决策模型的新约束

证据积累模型 (EAM) 已成为加速决策的主要模型，它能够将选择和响应时间分解为驱动决策过程的认知参数。EAM 框架内的几个模型包含关于决策过程如何运作的根本不同的想法，尽管之前的评估发现这些模型表现出高度的模仿性，这阻碍了研究人员对比这些不同理论观点的能力。我们的研究引入了一种被我们称为“双重响应”的被忽视的现象，这有助于进一步限制这些模型。我们表明双重响应产生了几个有趣的基准，并且当不同 EAM 的预测被限制以同时考虑选择响应时间分布和双重响应行为时，可以在标准实验范式中区分它们。我们的研究结果表明，横向抑制（例如，泄漏竞争累加器）为模型提供了对这些数据进行准确预测的通用能力。此外，只有包含前馈抑制的模型（例如，扩散模型）在我们提出的将标准 EAM 框架扩展为双重响应的情况下表现不佳，这表明前馈抑制普遍无法准确预测这些数据。我们相信，我们的研究在进一步限制快速决策模型方面迈出了重要的一步，