The Tweedledum and Tweedledee of dynamic decisions: Discriminating between diffusion decision and accumulator models.

IF 3.2 3区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL
Peter D Kvam
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Abstract

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.

动态决策中的 "Tweedledum "和 "Tweedledee":区分扩散决策模型和累加器模型。
动态决策理论通常建立在证据积累的基础上,而证据积累是通过赛车积累器或扩散模型来建模的,这些模型追踪着支持平衡随时间的变化。然而,这两类模型只是更普遍的证据积累过程的两个特例,在这个过程中,选项与积累空间中的方向相对应。以这种广义的证据积累方法为起点,我确定了四种区分绝对证据模型和相对证据模型的方法。首先,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证据样本的过滤,这是相对证据决策规则(如扩散决策模型)的特征。其次,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,从而从证据总量中划分出证据的平衡。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,机器学习还可用于直接估计选择方案之间的几何关系。我将这些不同的方法应用于一项方位辨别任务的数据中,从而对它们进行了说明,结果显示所有四种方法的结论都趋向于支持在选择过程中基于累加器的证据表征。这些工具可以清晰地划分绝对证据模型和相对证据模型,对于比较许多其他类型的决策理论应该很有用。
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来源期刊
CiteScore
6.70
自引率
2.90%
发文量
165
期刊介绍: The journal provides coverage spanning a broad spectrum of topics in all areas of experimental psychology. The journal is primarily dedicated to the publication of theory and review articles and brief reports of outstanding experimental work. Areas of coverage include cognitive psychology broadly construed, including but not limited to action, perception, & attention, language, learning & memory, reasoning & decision making, and social cognition. We welcome submissions that approach these issues from a variety of perspectives such as behavioral measurements, comparative psychology, development, evolutionary psychology, genetics, neuroscience, and quantitative/computational modeling. We particularly encourage integrative research that crosses traditional content and methodological boundaries.
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