Direct assessment of individual connotation and experience

Abstract

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.

Identifying key-psychological factors influencing the acceptance of yet emerging technologies–A multi-method-approach to inform climate policy

“…The procedure for summarizing CAM data is following a five-step procedure, which was theoretically motivated by existing CAM literature [cf. 107,108,112,113] and qualitative handbooks [114][115][116]: (1) In a first deductive step, super-and subordinate categories are derived from existing literature. Thereby, semantically identical terms can be positive or negative depending on the context.…”

“…As an exploratory method, CAMs offer added value compared to a survey, as the latter can only consider pre-identified potential factors influencing SAI acceptability [cf. 107,108]. This method enabled us to enrich the theoretical proposed integrative model by ethical and climate change related concerns (see Fig 1).…”

Human-machine Symbiosis: A Multivariate Perspective for Physically Coupled Human-machine Systems

“…Acceptance has been operationalized employing scales that refer to existing technologies (e.g., (Chintalapati & Daruri, 2017;van der Laan, Heino, & de Waard, 1997;Vantrepotte, Berberian, Pagliari, & Chambon, 2021)) or to future technologies described in scenario texts (Reinares-Lara, Olarte-Pascual, & Pelegrín-Borondo, 2018; Toft, Schuitema, & Thøgersen, 2014). Additionally, Cognitive-Affective Mapping, a recently developed method that bridges the gap between quantitative and qualitative research traditions (Reuter, Mansell, Rhea, & Kiesel, 2021;Thagard, 2010), may be employed in terms of anticipatory acceptance prediction of emerging technologies.…”