Direct assessment of individual connotation and experience An introduction to cognitive-affective mapping.

We introduce cognitive-affective maps (CAMs) as a novel tool to assess individual experiences and belief systems. CAMs were first presented by the cognitive scientist and philosopher Paul Thagard as a graphical representation of a mental network, visualizing attitudes, thoughts, and affective connotations toward the topic of interest. While CAMs were originally used primarily to visualize existing data, the recent release of the new software tool Valence has facilitated the applicability of CAMs for empirical data collection. In this article, we explain the concept and the theoretical background of CAMs. We exemplify how CAMs can be applied in research practice, including different options for analysis. We propose CAMs as a user-friendly and versatile methodological bridge between qualitative and quantitative research approaches and encourage incorporating the method into studies to access and visualize human attitudes and experience.

Predicting the issuance of COVID-19 stay-at-home orders in Africa: Using machine learning to develop insight for health policy research.

During the COVID-19 pandemic, many countries have issued stay-at-home orders (SAHOs) to reduce viral transmission. Because of their social and economic consequences, SAHOs are a politically risky decision for governments. Researchers typically attribute public health policymaking to five theoretically significant factors: political, scientific, social, economic, and external. However, a narrow focus on extant theory runs the risk of biasing findings and missing novel insights. This research employs machine learning to shift the focus from theory to data to generate hypotheses and insights "born from the data" and unconstrained by current knowledge. Beneficially, this approach can also confirm the extant theory. We apply machine learning in the form of a random forest classifier to a novel and multiple-domain data set of 88 variables to identify the most significant predictors of the issuance of a COVID-19-related SAHO in African countries (n = 54). Our data set includes a wide range of variables from sources such as the World Health Organization that cover the five principal theoretical factors and previously ignored domains. Generated using 1000 simulations, our model identifies a combination of theoretically significant and novel variables as the most important to the issuance of a SAHO and has a predictive accuracy using 10 variables of 78%, which represents a 56% increase in accuracy compared to simply predicting the modal outcome.

Psychophysiology, cognition, and political differences: Guest editors' introduction to the special issue.

We introduce the Politics and the Life Sciences special issue on Psychophysiology, Cognition, and Political Differences. This issue represents the second special issue funded by the Association for Politics and the Life Sciences that adheres to the Open Science Framework for registered reports (RR). Here pre-analysis plans (PAPs) are peer-reviewed and given in-principle acceptance (IPA) prior to data being collected and/or analyzed, and are published contingent upon the preregistration of the study being followed as proposed. Bound by a common theme of the importance of incorporating psychophysiological perspectives into the study of politics, broadly defined, the articles in this special issue feature a unique set of research questions and methodologies. In the following, we summarize the findings, discuss the innovations produced by this research, and highlight the importance of open science for the future of political science research.

Measuring attitudes as a complex system: Structured thinking and support for the Canadian carbon tax.

We test a method for applying a network-based approach to the study of political attitudes. We use cognitive-affective mapping, an approach that visually represents attitudes as networks of concepts that an individual associates with a given issue. Using a software tool called Valence, we asked a sample of Canadians (n = 111) to draw a cognitive-affective map (CAM) of their views on the carbon tax. We treat these networks as a series of undirected graphs and examine the extent to which support for the tax can be predicted based on each graph's emotional and structural properties. We find evidence that the emotional but not the structural properties significantly predict individuals' attitudes toward the carbon tax. We also find associations between CAMs' structural properties (density and centrality) and several measures of political interest. Our results provide preliminary evidence for the efficacy of CAMs as a tool for studying political attitudes. The study data are available at https://osf.io/qwpvd/?view_only=6834a1c442224e72bf45e7641880a17f.

A Novel Network Approach to Capture Cognition and Affect: COVID-19 Experiences in Canada and Germany.

We tested a novel method for studying human experience (thoughts and affect). We utilized Cognitive-Affective Maps (CAMs)-an approach to visually represent thoughts and their affective connotations as networks of concepts that individuals associate with a given event. Using an innovative software tool, we recruited a comparative sample of (n = 93) Canadians and (n = 100) Germans to draw a CAM of their experience (events, thoughts, feelings) with the Covid-19 pandemic. We treated these CAM networks as a series of directed graphs and examined the extent to which their structural properties (latent and emotional) are predictive for the perceived coronavirus threat (PCT). Across multiple models, we found consistent and significant relationships between these network variables and the PCT in both the Canadian and German sample. Our results provide unique insights into individuals' thinking and perceptions of the viral outbreak. Our results also demonstrate that a network analysis of CAMs' properties is a promising method to study the relationship between human thought and affective connotation. We suggest that CAMs can bridge several gaps between qualitative and quantitative methods. Unlike when using quantitative tools (e.g., questionnaires), participants' answers are not restricted by response items as participants are free to incorporate any thoughts and feelings on the given topic. Furthermore, as compared to traditional qualitative measures, such as structured interviews, the CAM technique may better enable researchers to objectively assess and integrate the substance of a shared experience for large samples of participants.

Ideology and social cognition Are liberals and conservatives differentially affected by social cues about group inequality?

Research links liberal and conservative ideological orientations with variation on psychological and cognitive characteristics that are important for perceptual processes and decision-making. This study investigates whether this variation can impact the social behaviors of liberals and conservatives. A sample of subjects (n = 1,245) participated in a modified public goods game in which an intragroup inequality was introduced to observe the effect on individuals' tendency toward self-interested versus prosocial behavior. Overall, the contributions of neither liberal- nor conservative-oriented individuals were affected by conditions of a general intragroup inequality. However, in response to the knowledge that group members voted to redress the inequality, levels of contribution among liberals significantly increased in comparison to the control. This was not true for conservatives. The results provide evidence that differences in ideological orientation are associated with individual differences in social cognition.

Social cues and ideology.

Research shows that individuals with liberal and conservative ideological orientations display different value positions concerning the acceptance of social change and inequality. Research also links the expression of different values to a number of biological factors, including heredity. In light of these biological influences, I investigate whether differences in social values associated with liberal and conservative ideologies reflect alternative strategies to maximize returns from social interactions. Using an American sample of Democrats and Republicans, I test whether information about shared and unshared social values in the form of implicit social attitudes have a disproportionate effect on the willingness of Democrats and Republicans to trust an anonymous social partner. I find evidence that knowledge of shared values significantly increases levels of trust among Democrats but not Republicans. I further find that knowledge of unshared values significantly decreases trust among Republicans but not Democrats. These findings are consistent with studies indicating that differences in ideological orientation are linked to differences in cognition and decision-making.