16:30 - 18:00
Parallel sessions 6
16:30 - 18:00
Room: HSZ - N4
Chair/s:
Julius Fenn
Understanding belief systems requires insight into the mental models that underlie how individuals represent and reason about complex or contested phenomena, such as disruptive technologies or political discourses. Mental models are internal representations that describe how people understand the structure and functioning of external systems. They form the cognitive foundation of laypersons’ belief systems and shape how information and values are integrated. To investigate such belief systems, methods that capture both explicit and implicit layers of meaning are needed. This symposium presents two complementary approaches for mapping mental models that differ in their degree of explicitness and the level of participant engagement required. At the explicit end, Cognitive-Affective Maps (CAMs) visualize belief systems as networks of emotionally evaluated concepts and relations. At the more implicit end, the Triads Task captures belief systems of individuals and groups in a standardized way, based on ratings of the similarity of three stimuli.

Julius Fenn (University of Freiburg) presents tools that make CAMs applicable within experimental paradigms. These tools enable researchers to manipulate belief structures, measure changes in affective–cognitive coherence, and integrate CAMs as dependent or independent variables in controlled designs.
René Dutschke (TU Dresden) presents its roots in Kelly’s theory of personal constructs and showcases its applications as a research tool.
Irina Monno (University of Freiburg) explores the potential of CAMs as a method to capture and measure changes in belief systems by visualizing shifts in cognitive and emotional structures.
Michael Gorki (University of Freiburg) uses CAMs alongside questionnaires to examine how “laypersons” conceptualize sustainability, a highly contested concept in public, academia and policy-making.
Bettina Harder (University of Erlangen-Nuremberg) evaluates the use of CAMs in diagnostic and counseling contexts. CAMs have proven to be helpful diagnostic tools by providing in-depth information in a structured way, thereby identifying individually relevant starting points for interventions to deal with stress or test anxiety.

Together, these approaches demonstrate a continuum of mapping techniques, from explicit to implicit. By highlighting their advantages, limitations, and practical potential, the symposium provides insights into new methods for investigating belief systems related to technological, ethical, psychological, and societal issues.
Submission 697
Developing Advanced Tools for Cognitive–Affective Mapping: Possible Applications for Experimental Research
SymposiumTalk-01
Presented by: Julius Fenn
Julius Fenn 1, 2, Michael Gorki 1, 2, Wilhelm Gros 1, 2, Irina Monno 1, Andrea Kiesel 1, 2
1 Institute for Psychology, University of Freiburg, Germany
2 Cluster of Excellence livMatS @ FIT–Freiburg Center for Interactive Materials and Bioinspired Technologies, University of Freiburg, Germany
Cognitive–Affective Maps (CAMs) provide a structured method to capture how individuals organize concepts, associations, and emotional evaluations within a network-like representation. Although originally introduced as a mixed-methods tool, CAMs can be systematically embedded into experimental paradigms. Researchers may manipulate the initial network structure (e.g., predefined central or opposing concepts), the affective or informational context, or task constraints during map construction. The resulting CAMs serve as sensitive dependent variables that reflect cognitive–emotional processing and change.

We present Cognitive–Affective Map Extended Logic (C.A.M.E.L.), a software suite that enables such applications. Its data collection tool allows participants to construct or modify CAMs under standardized, fully configurable conditions. CAMs can contain graded emotional evaluations, weighted supporting or opposing connections, directional arrows, and typed semantic content, producing a complex data structure suitable for quantitative and qualitative analysis. The accompanying CAM-App supports preprocessing, semantic clustering, and network-level quantification (e.g., mean valence, density, neighborhood indicators) through transparent, protocol-based analysis pipelines. A web-based administrative panel facilitates study setup, configurations of parameters, and participant management.

This framework accommodates diverse experimental designs: CAMs as dependent variables in pre–post studies; CAMs as independent variables via predefined network; and adaptive paradigms in which real-time CAM analysis trigger subsequent tasks or feedback.

By integrating emotional evaluation, semantic associations, and graph structure into a different format, C.A.M.E.L. advances the study of cognitive–affective processes such as coherence formation, attitude change, and belief updating in the context of mental models.