Cortes Robert A, Weinberger Adam B, Green Adam E
Department of Psychology, Georgetown University, Washington, DC, United States.
Interdisciplinary Program in Neuroscience, Georgetown University, Washington, DC, United States.
Front Psychol. 2023 Mar 10;14:1150210. doi: 10.3389/fpsyg.2023.1150210. eCollection 2023.
Reasoning is a complex form of human cognition whose nature has long been debated. While a number of neurocognitive mechanisms for deductive reasoning have been offered, one of the most prominent accounts is Mental Model Theory (MMT). According to MMT, humans are able to manipulate and represent information for reasoning and problem solving by leveraging the brain's evolved visuospatial resources. Thus, when solving deductive reasoning problems, reasoners build "mental models" of the essential pieces of information conveyed in the premises, with their relations to each other represented spatially-even when the information contained within a reasoning problem is not intrinsically spatial. Crucially, taking a spatially-based approach, such as building mental models, supports higher accuracy on deductive reasoning problems. However, no study has empirically tested whether explicitly training this mental modeling ability leads to improved deductive reasoning performance.
Therefore, we designed the Mental Models Training App, a cognitive training mobile application which requires participants to complete increasingly difficult reasoning problems while using an external mental modeling tool. In this preregistered study (https://osf.io/4b7kn), we conducted a between-subjects experiment ( = 301) which compared the Mental Models Training App to 3 distinct control conditions in order to examine which specific components (if any) of the training were causally responsible for improved reasoning performance.
Results demonstrate that, when compared to a passive control condition, the Mental Models Training App led to improvements in adults' verbal deductive reasoning performance both during and after the training intervention. However, contrary to our preregistered hypotheses, the training-induced improvements were not significantly larger than the effects of the active control conditions-one which included adaptive practice of the reasoning problems, and one which included adaptive practice as well as a spatial alphabetization control task.
Therefore, while the present results demonstrate the ability of the Mental Models Training App to enhance verbal deductive reasoning, they do not support the hypothesis that directly training participants mental modeling ability yields improved performance beyond the effects of adaptive practice of reasoning. Future research should examine the long-term effects of repeated usage of the Mental Models Training App, as well as transfer effects to other forms of reasoning. Finally, we present the Mental Models Training App as a free mobile application available on the Apple App store (https://apps.apple.com/us/app/mental-models-training/id1664939931), in the hope that this translational research may be utilized by the general public to improve their reasoning ability.
推理是人类认知的一种复杂形式,其本质长期以来一直存在争议。虽然已经提出了一些用于演绎推理的神经认知机制,但最突出的一种解释是心理模型理论(MMT)。根据心理模型理论,人类能够利用大脑进化出的视觉空间资源来操纵和表征用于推理和解决问题的信息。因此,在解决演绎推理问题时,推理者会构建前提中传达的基本信息的“心理模型”,这些信息之间的关系以空间方式呈现——即使推理问题中包含的信息本身并非空间信息。至关重要的是,采用基于空间的方法,比如构建心理模型,有助于提高演绎推理问题的准确性。然而,尚无研究通过实证检验明确训练这种心理建模能力是否会提高演绎推理表现。
因此,我们设计了心理模型训练应用程序,这是一款认知训练移动应用程序,要求参与者在使用外部心理建模工具的同时完成难度逐渐增加的推理问题。在这项预先注册的研究(https://osf.io/4b7kn)中,我们进行了一项组间实验(n = 301),将心理模型训练应用程序与3种不同的对照条件进行比较,以检验训练中的哪些特定组成部分(如果有的话)对推理表现的改善具有因果作用。
结果表明,与被动对照条件相比,心理模型训练应用程序在训练干预期间及之后都提高了成年人的言语演绎推理表现。然而,与我们预先注册的假设相反,训练带来的改善并不比主动对照条件的效果显著更大——其中一个主动对照条件包括对推理问题的适应性练习,另一个包括适应性练习以及一项空间字母排序控制任务。
因此,虽然目前的结果证明了心理模型训练应用程序增强言语演绎推理的能力,但它们并不支持这样的假设,即直接训练参与者的心理建模能力所产生的表现提升超过推理适应性练习的效果。未来的研究应该考察重复使用心理模型训练应用程序的长期效果,以及对其他推理形式的迁移效果。最后,我们将心理模型训练应用程序作为一款免费的移动应用程序展示在苹果应用商店(https://apps.apple.com/us/app/mental - models - training/id1664939931)上,希望这项转化研究能够被公众利用来提高他们的推理能力。