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利用扩散模型进行符号数值任务,以检验衰老效应。

Using diffusion models for symbolic numeracy tasks to examine aging effects.

机构信息

Department of Psychology, The Ohio State University.

出版信息

J Exp Psychol Learn Mem Cogn. 2024 Sep;50(9):1385-1403. doi: 10.1037/xlm0001400.

DOI:10.1037/xlm0001400
PMID:39418451
Abstract

We present a model-based analysis of aging effects in three symbolic numeracy tasks using three groups of subjects (young adults, 60- to 69-year-olds, and 70- to 90-year-olds). The tasks are number discrimination (is this number greater or less than 50), number memory (was this number in the list of numbers just displayed), and number line (point to where this number is on this number line). The first two tasks were fit by the standard two-choice diffusion model and the last one by the spatially continuous diffusion model (Ratcliff, 2018). Results showed good fits of the models to accuracy (choices) and response time distributions. In the tasks, nondecision time (the time to encode a stimulus and make a response) increased with age, but the amount of evidence needed for a decision (boundary settings) increased in the number discrimination and number memory tasks, but not the number line task. The number discrimination task produced conflicting accuracy and response time results as a function of age, but the model-based analyses resolved these differences. In the number memory task, drift rates (evidence used to drive the decision process) were lower for the older adults than for young adults, but for the other two (easier) tasks, there was no change in drift rate with age. The analyses extracted differences among individuals in model components, some of which were systematic across tasks. In particular, drift rates were correlated across tasks, which shows consistent individual differences across tasks, results that could not have been obtained without model-based analyses. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

摘要

我们使用三组被试(年轻人、60-69 岁和 70-90 岁),通过基于模型的分析方法,研究了三个符号数字任务中的老化效应。这些任务分别是数字辨别(这个数字大于还是小于 50)、数字记忆(这个数字是否在刚才显示的数字列表中)和数字线(在这条数字线上指出这个数字的位置)。前两个任务通过标准的双选择扩散模型拟合,最后一个任务通过空间连续扩散模型(Ratcliff,2018)拟合。结果表明,模型对准确性(选择)和反应时间分布的拟合效果良好。在这些任务中,非决策时间(编码刺激和做出反应的时间)随着年龄的增长而增加,但做出决策所需的证据量(边界设置)在数字辨别和数字记忆任务中增加,但在数字线任务中没有增加。数字辨别任务的准确性和反应时间结果随着年龄的变化而产生冲突,但基于模型的分析解决了这些差异。在数字记忆任务中,漂移率(用于驱动决策过程的证据)对于老年人来说比年轻人低,但对于其他两个(更容易的)任务,漂移率随年龄变化没有变化。分析从模型成分中提取出个体差异,其中一些差异在任务之间是系统的。特别是,漂移率在任务之间具有相关性,这表明个体在任务之间存在一致的差异,这些结果如果没有基于模型的分析是无法获得的。(PsycInfo 数据库记录(c)2024 APA,保留所有权利)。

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