Direct assessment of individual connotation and experience .

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 has facilitated the applicability of CAMs for empirical data collection.

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: However, a narrow focus on extant theory runs the risk of biasing findings and missing novel insights.

Measuring attitudes as a complex system: .

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.

Psychophysiology, cognition, and political differences: .

We introduce the 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.

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 ( = 93) Canadians and ( = 100) Germans to draw a CAM of their experience (events, thoughts, feelings) with the Covid-19 pandemic.

Ideology and social cognition

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 ( = 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.

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.