Dataset Information

Correct Me if I'm Wrong: Groups Outperform Individuals in the Climate Stabilization Task.

ABSTRACT: Avoiding dangerous climate change requires ambitious emissions reduction. Scientists agree on this, but policy-makers and citizens do not. This discrepancy can be partly attributed to faulty mental models, which cause individuals to misunderstand the carbon dioxide (CO2) system. For example, in the Climate Stabilization Task (hereafter, "CST") (Sterman and Booth-Sweeney, 2007), individuals systematically underestimate the emissions reduction required to stabilize atmospheric CO2 levels, which may lead them to endorse ineffective "wait-and-see" climate policies. Thus far, interventions to correct faulty mental models in the CST have failed to produce robust improvements in decision-making. Here, in the first study to test a group-based intervention, we found that success rates on the CST markedly increased after participants deliberated with peers in a group discussion. The group discussion served to invalidate the faulty reasoning strategies used by some individual group members, thus increasing the proportion of group members who possessed the correct mental model of the CO2 system. Our findings suggest that policy-making and public education would benefit from group-based practices.

SUBMITTER: Xie B

PROVIDER: S-EPMC6284004 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Correct Me if I'm Wrong: Groups Outperform Individuals in the Climate Stabilization Task.

Xie Belinda B Hurlstone Mark J MJ Walker Iain I

Frontiers in psychology 20181130

Avoiding dangerous climate change requires ambitious emissions reduction. Scientists agree on this, but policy-makers and citizens do not. This discrepancy can be partly attributed to faulty mental models, which cause individuals to misunderstand the carbon dioxide (CO<sub>2</sub>) system. For example, in the Climate Stabilization Task (hereafter, "CST") (Sterman and Booth-Sweeney, 2007), individuals systematically underestimate the emissions reduction required to stabilize atmospheric CO<sub>2< ...[more]

PMID: 30555370

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Project description:BackgroundMultiple Sclerosis (MS), a neurodegenerative and neuroinflammatory disease, causing lesions that disrupt the brain's anatomical and physiological connectivity networks, resulting in cognitive, visual and/or motor disabilities. Advanced imaging techniques like diffusion and functional MRI allow measurement of the brain's structural connectivity (SC) and functional connectivity (FC) networks, and can enable a better understanding of how their disruptions cause disability in people with MS (pwMS). However, advanced MRI techniques are used mainly for research purposes as they are expensive, time-consuming and require high-level expertise to acquire and process. As an alternative, the Network Modification (NeMo) Tool can be used to estimate SC and FC using lesion masks derived from pwMS and a reference set of controls' connectivity networks.ObjectiveHere, we test the hypothesis that estimated SC and FC (eSC and eFC) from the NeMo Tool, based only on an individual's lesion masks, can be used to classify pwMS into disability categories just as well as SC and FC extracted from advanced MRI directly in pwMS. We also aim to find the connections most important for differentiating between no disability vs evidence of disability groups.Materials and methodsOne hundred pwMS (age:45.5 ± 11.4 years, 66% female, disease duration: 12.97 ± 8.07 years) were included in this study. Expanded Disability Status Scale (EDSS) was used to assess disability, 67 pwMS had no disability (EDSS < 2). Observed SC and FC were extracted from diffusion and functional MRI directly in pwMS, respectively. The NeMo Tool was used to estimate the remaining structural connectome (eSC), by removing streamlines in a reference set of tractograms that intersected the lesion mask. The NeMo Tool's eSC was used then as input to a deep neural network to estimate the corresponding FC (eFC). Logistic regression with ridge regularization was used to classify pwMS into disability categories (no disability vs evidence of disability), based on demographics/clinical information (sex, age, race, disease duration, clinical phenotype, and spinal lesion burden) and either pairwise entries or regional summaries from one of the following matrices: SC, FC, eSC, and eFC. The area under the ROC curve (AUC) was used to assess the classification performance. Both univariate statistics and parameter coefficients from the classification models were used to identify features important to differentiating between the groups.ResultsThe regional eSC and eFC models outperformed their observed FC and SC counterparts (p-value < 0.05), while the pairwise eSC and SC performed similarly (p = 0.10). Regional eSC and eFC models had higher AUC (0.66-0.68) than the pairwise models (0.60-0.65), with regional eFC having highest classification accuracy across all models. Ridge regression coefficients for the regional eFC and regional observed FC models were significantly correlated (Pearson's r = 0.52, p-value < 10e-7). Decreased estimated SC node strength in default mode and ventral attention networks and increased eFC node strength in visual networks was associated with evidence of disability.DiscussionHere, for the first time, we use clinically acquired lesion masks to estimate both structural and functional connectomes in patient populations to better understand brain lesion-dysfunction mapping in pwMS. Models based on the NeMo Tool's estimates of SC and FC better classified pwMS by disability level than SC and FC observed directly in the individual using advanced MRI. This work provides a viable alternative to performing high-cost, advanced MRI in patient populations, bringing the connectome one step closer to the clinic.

Dataset Information

Correct Me if I'm Wrong: Groups Outperform Individuals in the Climate Stabilization Task.

Publications

Correct Me if I'm Wrong: Groups Outperform Individuals in the Climate Stabilization Task.

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