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A randomness perspective on intelligence processes
Intelligence ( IF 3.3 ) Pub Date : 2022-02-21 , DOI: 10.1016/j.intell.2022.101632
Inhan Kang 1 , Paul De Boeck 1 , Ivailo Partchev 2
Affiliation  

We study intelligence processes using a diffusion IRT model with random variability in cognitive model parameters: variability in drift rate (the trend of information accumulation toward a correct or incorrect response) and variability in starting point (from where the information accumulation starts). The random variation concerns randomness across person-item pairs and cannot be accounted for by individual and inter-item differences. Interestingly, the models explain the conditional dependencies between response accuracy and response time that are found in previous studies on cognitive ability tests, leading us to the formulation of a randomness perspective on intelligence processes. For an empirical test, we have analyzed verbal analogies data and matrix reasoning data using diffusion IRT models with different variability assumptions. The results indicate that 1) models with random variability fit better than models without, with implications for the conditional dependencies in both types of tasks; 2) for verbal analogies, random variation in drift rate seems to exist, which can be explained by person-by-item word knowledge differences; and 3) for both types of tasks, the starting point variation was also established, in line with the inductive nature of the tasks, requiring a sequential hypothesis testing process. Finally, the correlation of individual differences in drift rate and SAT suggests a meta-strategic choice of respondents to focus on accuracy rather than speed when they have a higher cognitive capacity and when the task is one for which investing in time pays off. This seems primarily the case for matrix reasoning and less so for verbal analogies.



中文翻译:

智能过程的随机性视角

我们使用具有认知模型参数随机变异性的扩散 IRT 模型研究智能过程:漂移率的变异性(信息积累朝着正确或不正确响应的趋势)和起点的变异性(信息积累的起点)。随机变化涉及人-项目对之间的随机性,不能由个体和项目间的差异来解释。有趣的是,这些模型解释了先前关于认知能力测试的研究中发现的响应准确性和响应时间之间的条件依赖关系,从而导致我们对智能过程的随机性观点进行了阐述。对于实证检验,我们使用具有不同可变性假设的扩散 IRT 模型分析了口头类比数据和矩阵推理数据。结果表明 1) 具有随机变异性的模型比没有随机变异性的模型更适合,这对两种类型的任务中的条件依赖性都有影响;2)对于言语类比,漂移率似乎存在随机变化,这可以通过逐项单词知识差异来解释;3) 对于这两种类型的任务,还建立了起点变化,符合任务的归纳性质,需要一个连续的假设检验过程。最后,漂移率和 SAT 的个体差异的相关性表明,当他们具有更高的认知能力并且当任务是一项投资时间得到回报的任务时,受访者的元战略选择会关注准确性而不是速度。这似乎主要是矩阵推理的情况,而不是口头类比。

更新日期:2022-02-21
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