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响应时间扩展多项处理树模型的优势:对 Starns(2018)的回复。

Benefits of response time-extended multinomial processing tree models: A reply to Starns (2018).

机构信息

Department of Psychology, Philipps-Universität Marburg, Gutenbergstraße 18, 35032, Marburg, Germany.

Department of Psychology, School of Social Sciences, University of Mannheim, 68131, Mannheim, Germany.

出版信息

Psychon Bull Rev. 2020 Jun;27(3):571-580. doi: 10.3758/s13423-019-01663-0.

DOI:10.3758/s13423-019-01663-0
PMID:32052267
Abstract

In his comment on Heck and Erdfelder (2016, Psychonomic Bulletin & Review, 23, 1440-1465), Starns (2018, Psychonomic Bulletin & Review, 25, 2406-2416) focuses on the response time-extended two-high-threshold (2HT-RT) model for yes-no recognition tasks, a specific example for the general class of response time-extended multinomial processing tree models (MPT-RTs) we proposed. He argues that the 2HT-RT model cannot accommodate the speed-accuracy trade-off, a key mechanism in speeded recognition tasks. As a remedy, he proposes a specific discrete-state model for recognition memory that assumes a race mechanism for detection and guessing. In this reply, we clarify our motivation for using the 2HT-RT model as an example and highlight the importance and benefits of MPT-RTs as a flexible class of general-purpose, simple-to-use models. By binning RTs into discrete categories, the MPT-RT approach facilitates the joint modeling of discrete responses and response times in a variety of psychological paradigms. In fact, many paradigms either lack a clear-cut accuracy criterion or show performance levels at ceiling, making corrections for incautious responding redundant. Moreover, we show that some forms of speed-accuracy trade-off can in fact not only be accommodated but also be measured by appropriately designed MPT-RTs.

摘要

在他对 Heck 和 Erdfelder(2016,《心理通报与评论》,23,1440-1465)的评论中,Starns(2018,《心理通报与评论》,25,2406-2416)关注了用于 yes-no 识别任务的扩展反应时-双高阈限(2HT-RT)模型,这是我们提出的广义扩展反应时多项式处理树模型(MPT-RTs)的一个具体示例。他认为,2HT-RT 模型不能容纳速度-准确性权衡,这是速度识别任务中的一个关键机制。作为补救措施,他提出了一种特定的识别记忆离散状态模型,该模型假设检测和猜测的竞争机制。在本回复中,我们澄清了使用 2HT-RT 模型作为示例的动机,并强调了 MPT-RTs 作为一种灵活的通用、易于使用的模型类的重要性和优势。通过将 RT 分为离散类别,MPT-RT 方法促进了各种心理范式中离散响应和反应时间的联合建模。事实上,许多范式要么缺乏明确的准确性标准,要么表现出接近上限的水平,因此对轻率反应的校正变得多余。此外,我们表明,某些形式的速度-准确性权衡实际上不仅可以被容纳,而且可以通过适当设计的 MPT-RTs 进行测量。

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引用本文的文献

1
Benefits of multinomial processing tree models with discrete and continuous variables in memory research: an alternative modeling proposal to Juola et al. (2019).多项处理树模型在记忆研究中对离散和连续变量的优势:对 Juola 等人(2019 年)的替代建模建议。
Mem Cognit. 2024 May;52(4):793-825. doi: 10.3758/s13421-023-01501-8. Epub 2024 Jan 4.