Kvam Peter D
The Ohio State University, Columbus, OH, USA.
Psychon Bull Rev. 2025 Apr;32(2):588-613. doi: 10.3758/s13423-024-02587-0. Epub 2024 Oct 1.
Theories of dynamic decision-making are typically built on evidence accumulation, which is modeled using racing accumulators or diffusion models that track a shifting balance of support over time. However, these two types of models are only two special cases of a more general evidence accumulation process where options correspond to directions in an accumulation space. Using this generalized evidence accumulation approach as a starting point, I identify four ways to discriminate between absolute-evidence and relative-evidence models. First, an experimenter can look at the information that decision-makers considered to identify whether there is a filtering of near-zero evidence samples, which is characteristic of a relative-evidence decision rule (e.g., diffusion decision model). Second, an experimenter can disentangle different components of drift rates by manipulating the discriminability of the two response options relative to the stimulus to delineate the balance of evidence from the total amount of evidence. Third, a modeler can use machine learning to classify a set of data according to its generative model. Finally, machine learning can also be used to directly estimate the geometric relationships between choice options. I illustrate these different approaches by applying them to data from an orientation-discrimination task, showing converging conclusions across all four methods in favor of accumulator-based representations of evidence during choice. These tools can clearly delineate absolute-evidence and relative-evidence models, and should be useful for comparing many other types of decision theories.
动态决策理论通常基于证据积累构建，证据积累通过竞争累加器或扩散模型进行建模，这些模型会随着时间推移追踪支持度的不断变化的平衡。然而，这两种模型只是更一般的证据积累过程中的两个特殊情况，在这个过程中，选项对应于积累空间中的方向。以这种广义证据积累方法为出发点，我确定了四种区分绝对证据模型和相对证据模型的方法。第一，实验者可以查看决策者考虑的信息，以确定是否存在对接近零的证据样本的筛选，这是相对证据决策规则（如扩散决策模型）的特征。第二，实验者可以通过操纵两个反应选项相对于刺激的可辨别性来区分漂移率的不同组成部分，以从证据总量中描绘出证据的平衡。第三，建模者可以使用机器学习根据生成模型对一组数据进行分类。最后，机器学习还可以用于直接估计选择选项之间的几何关系。我将这些不同方法应用于来自方向辨别任务的数据，展示了所有四种方法得出的一致结论，即支持在选择过程中基于累加器的证据表示。这些工具可以清晰地区分绝对证据模型和相对证据模型，并且应该有助于比较许多其他类型的决策理论。
