Kumar Medha, Dutt Varun
Applied Cognitive Science Laboratory, School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, Kamand, India.
School of Humanities and Social Sciences, Indian Institute of Technology Mandi, Kamand, India.
Front Psychol. 2018 Mar 26;9:299. doi: 10.3389/fpsyg.2018.00299. eCollection 2018.
Research shows that people's wait-and-see preferences for actions against climate change are a result of several factors, including cognitive misconceptions. The use of simulation tools could help reduce these misconceptions concerning Earth's climate. However, it is still unclear whether the learning in these tools is of the problem's surface features (dimensions of emissions and absorptions and cover-story used) or of the problem's structural features (how emissions and absorptions cause a change in CO concentration under different CO concentration scenarios). Also, little is known on how problem's difficulty in these tools (the shape of CO concentration trajectory), as well as the use of these tools as a decision aid influences performance. The primary objective of this paper was to investigate how learning about Earth's climate via simulation tools is influenced by problem's surface and structural features, problem's difficulty, and decision aids. In experiment 1, we tested the influence of problem's surface and structural features in a simulation called Dynamic Climate Change Simulator (DCCS) on subsequent performance in a paper-and-pencil Climate Stabilization (CS) task ( = 100 across four between-subject conditions). In experiment 2, we tested the effects of problem's difficulty in DCCS on subsequent performance in the CS task ( = 90 across three between-subject conditions). In experiment 3, we tested the influence of DCCS as a decision aid on subsequent performance in the CS task ( = 60 across two between-subject conditions). Results revealed a significant reduction in people's misconceptions in the CS task after performing in DCCS compared to when performing in CS task in the absence of DCCS. The decrease in misconceptions in the CS task was similar for both problems' surface and structural features, showing both structure and surface learning in DCCS. However, the proportion of misconceptions was similar across both simple and difficult problems, indicating the role of cognitive load to hamper learning. Finally, misconceptions were reduced when DCCS was used as a decision aid. Overall, these results highlight the role of simulation tools in alleviating climate misconceptions. We discuss the implication of using simulation tools for climate education and policymaking.
研究表明，人们对应对气候变化行动的观望偏好是由多种因素造成的，包括认知误解。使用模拟工具有助于减少这些关于地球气候的误解。然而，目前尚不清楚在这些工具中的学习是关于问题的表面特征（排放和吸收的维度以及所使用的背景故事）还是问题的结构特征（在不同的二氧化碳浓度情景下，排放和吸收如何导致二氧化碳浓度变化）。此外，对于这些工具中问题的难度（二氧化碳浓度轨迹的形状）以及将这些工具用作决策辅助工具如何影响表现，人们了解甚少。本文的主要目的是研究通过模拟工具学习地球气候如何受到问题的表面和结构特征、问题的难度以及决策辅助工具的影响。在实验1中，我们在一个名为动态气候变化模拟器（DCCS）的模拟中测试了问题的表面和结构特征对后续纸笔形式的气候稳定（CS）任务表现的影响（在四个被试间条件下，n = 100）。在实验2中，我们测试了DCCS中问题的难度对后续CS任务表现的影响（在三个被试间条件下，n = 90）。在实验3中，我们测试了DCCS作为决策辅助工具对后续CS任务表现的影响（在两个被试间条件下，n = 60）。结果显示，与在没有DCCS的情况下进行CS任务相比，在DCCS中进行任务后，人们在CS任务中的误解显著减少。对于问题的表面和结构特征，CS任务中误解的减少是相似的，表明在DCCS中既有结构学习也有表面学习。然而，简单问题和困难问题的误解比例相似，表明认知负荷对学习的阻碍作用。最后，当DCCS被用作决策辅助工具时，误解减少了。总体而言，这些结果凸显了模拟工具在减轻气候误解方面的作用。我们讨论了使用模拟工具对气候教育和政策制定的意义。
