Cognitive-Affective Maps

What are Cognitive-Affective Maps?

Cognitive-Affective Maps (CAMs) are a qualitative and quantitative research method and first became popular through Thagard in 20101. CAMs can be considered as a specific form of mind maps. The different elements of a CAM are concepts (also called nodes), which are linked by connections (also called edges). Concepts incorporate so-called affective valences by representing whether a person associates positive, negative, neutral or ambivalent emotions with a drawn concept. Furthermore, it is possible to specify the connections in different strengths in two different forms: Solid lines stand for supporting connections and dashed lines stand for inhibitory connections. For more information see the homepage of Paul Thagard who introduced CAMs, https://paulthagard.com/links/cognitive-affective-maps/

Additionally, CAMs might contain directional arrows which represent a directional effect. Thus, CAMs represent a weighted directional network with a simple graph structure and can be analyzed by means of network analysis in addition to more common quantitative as well as qualitative analyses. Possible elements of a CAM:

CAM Legend missing

For more information see the homepage of Paul Thagard who introduced CAMs.

Using CAMs, it is possible to identify, visually represent, and analyze existing belief structures or any kind of declarative knowledge. With our developed Data Collection Tool, participants can draw within online (or offline) studies their own CAM and the resulting data can be preprocessed and analyzed by the Data Analysis Tool.

In the following example CAM, participants compared cars and public transport2 (click on CAM to enlarge it in new tab - could take few seconds):

CAM Example missing

What are the advantages of using Cognitive-Affective Maps?

CAMs have a strong theoretical foundation and have been discussed within philosophy (mainly in the context of explanatory coherence) since the end of the 1990s 3,4. The process of drawing a CAM is understood as a constraint satisfaction problem and multiple mathematical models, like the hot cognition (HOTCO) model5, were proposed. Since 20206, CAM tools for large online studies are under development in the Cognition, Action and Sustainability Unit (in German Allgemeine Psychologie) at the University of Freiburg, Department of Psychology. For more information regarding the most recent research, see the homepage of the department in Freiburg, https://www.psychologie.uni-freiburg.de/abteilungen/Allgemeine.Psychologie/research/cam-research.

Data obtained in CAM studies can be analyzed quantitatively, in terms of network parameters and affective connotation, as well as qualitatively. CAMs as a research method foster research in multiple fields of application, which are linked to multiple interesting research question:

Research questions already investigated (incomplete list):

  • In two empirical studies it was explored how people (in different countries) are perceiving the COVID-19 pandemic7,8.
  • The HOTCO model has been applied in the context of agent-based modelling9,10.
  • CAMs have been applied to study political belief systems11,12,13.
  • The potentiality of CAMs for conflict mediation was investigated14.
  • The additional value of CAMs to survey studies has been investigated15.
  • CAMs have been applied to check if a cost intervention has an impact on the perception of environmentally friendly mobility vs. car2.
  • ...

Research questions currently under investigation (incomplete list):

  • Do CAMs add value to surveys?
    • CAMs, being an exploratory tool, allow us to go beyond predefined standardized surveys to identify possible interrelationships within belief systems that would otherwise go unnoticed16,17. This has been investigated in a study identifying key psychological factors influencing the acceptance of hypothetical technologies15.
  • Can CAMs help understand concepts like "Life" and "Sustainability"?
    • CAMs are used to explore how researchers from different disciplines or laypersons understand certain terms. This will be examined in multiple studies focused on the concept of "sustainability"; see Michael Gorki (livMatS)
  • How reliable are CAMs?
    • The reliability of CAMs is being investigated through longitudinal designs in a PhD project; see Wilhelm Gros (livMatS)
  • What drives the perception of soft robots compared to rigid robots? How can the complex construct of trust be operationalized?
    • Using intervention designs and exploratory studies in a post-doc project, the perceptions of soft robots and the concept of trust are being investigated across multiple studies; see Dr. Louisa Estadieu (livMatS)
  • ...

References

  1. Paul Thagard. EMPATHICA: A Computer Support System with Visual Representations for Cognitive-Affective Mapping. In Workshops at the Twenty-Fourth AAAI Conference on Artificial Intelligence, 79–81. July 2010. URL: https://www.aaai.org/ocs/index.php/WS/AAAIW10/paper/view/1981 (visited on 2022-07-07). ↩

  2. Clara Sendtner. Kostbare kisten: gründe für fehleinschätzungen der kosten des eigenen autos und deren auswirkungen auf die bewertung des öPNV -masterarbeit. Master's thesis, University of Freiburg, 2021. URL: https://www.researchgate.net/publication/354095986_Kostbare_Kisten_Grunde_fur_Fehleinschatzungen_der_Kosten_des_eigenen_Autos_und_deren_Auswirkungen_auf_die_Bewertung_des_OPNV_-Masterarbeit, doi:10.13140/RG.2.2.32640.56325. ↩↩

  3. Paul Thagard. Ethical coherence. Philosophical Psychology, 11(4):405–422, December 1998. Publisher: Routledge _eprint: https://doi.org/10.1080/09515089808573270. URL: https://doi.org/10.1080/09515089808573270 (visited on 2022-07-06), doi:10.1080/09515089808573270. ↩

  4. Paul Thagard. Coherence in Thought and Action. MIT Press, 2000. ISBN 978-0-262-70092-4. Google-Books-ID: Px0vctI8eGQC. ↩

  5. Paul Thagard. Hot Thought: Mechanisms and Applications of Emotional Cognition. MIT Press, 2006. ISBN 978-0-262-70124-2. Google-Books-ID: tJV735_HoLAC. ↩

  6. Carter Rhea, Lisa Reuter, Christian Thibeault, and Jinelle Piereder. Valence Software Release. August 2020. Publisher: OSF. URL: https://osf.io/9tza2/ (visited on 2022-07-22). ↩

  7. Jordan Mansell, Lisa Reuter, Carter Rhea, and Andrea Kiesel. A Novel Network Approach to Capture Cognition and Affect: COVID-19 Experiences in Canada and Germany. Frontiers in Psychology, 12:1–14, 2021. URL: https://www.frontiersin.org/articles/10.3389/fpsyg.2021.663627 (visited on 2022-07-06), doi:10.3389/fpsyg.2021.663627. ↩

  8. Lisa Reuter, Julius Fenn, Tobias Andreas Bilo, Melanie Schulz, Annemarie Lina Weyland, Andrea Kiesel, and Roland Thomaschke. Leisure walks modulate the cognitive and affective representation of the corona pandemic: Employing Cognitive-Affective Maps within a randomized experimental design. Applied Psychology: Health and Well-Being, 13(4):952–967, 2021. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/aphw.12283. URL: https://onlinelibrary.wiley.com/doi/abs/10.1111/aphw.12283 (visited on 2022-07-06), doi:10.1111/aphw.12283. ↩

  9. Ingo Wolf, Tobias Schröder, Jochen Neumann, and Gerhard de Haan. Changing minds about electric cars: An empirically grounded agent-based modeling approach. Technological Forecasting and Social Change, 94:269–285, May 2015. URL: https://linkinghub.elsevier.com/retrieve/pii/S0040162514002960 (visited on 2022-07-06), doi:10.1016/j.techfore.2014.10.010. ↩

  10. Tobias Schröder and Ingo Wolf. Modeling multi-level mechanisms of environmental attitudes and behaviours: The example of carsharing in Berlin. Journal of Environmental Psychology, 52:136–148, October 2017. URL: https://linkinghub.elsevier.com/retrieve/pii/S0272494416300196 (visited on 2022-07-06), doi:10.1016/j.jenvp.2016.03.007. ↩

  11. Thomas Homer-Dixon, Jonathan Leader Maynard, Matto Mildenberger, Manjana Milkoreit, Steven J. Mock, Stephen Quilley, Tobias Schröder, and Paul Thagard. A Complex Systems Approach to the Study of Ideology: Cognitive-Affective Structures and the Dynamics of Belief Systems. Journal of Social and Political Psychology, 1(1):337–363, December 2013. Number: 1. URL: https://jspp.psychopen.eu/index.php/jspp/article/view/4763 (visited on 2022-07-06), doi:10.5964/jspp.v1i1.36. ↩

  12. Thomas Homer-Dixon, Manjana Milkoreit, Steven J. Mock, Tobias Schröder, and Paul Thagard. The Conceptual Structure of Social Disputes: Cognitive-Affective Maps as a Tool for Conflict Analysis and Resolution. SAGE Open, 4(1):1–20, January 2014. URL: http://journals.sagepub.com/doi/10.1177/2158244014526210 (visited on 2022-07-06), doi:10.1177/2158244014526210. ↩

  13. Milkoreit, Manjana. Mindmade Politics - The Role of Cognition in Global Climate Change Governance. PhD Thesis, UWSpace, 2013. URL: http://hdl.handle.net/10012/7711. ↩

  14. Wilhelm Gros, Lisa Reuter, Michael Stumpf, and Andrea Kiesel. CAMediaid: multimethod approach to assess cognitive-affective maps in mediation - a quantitative validation study. Master's thesis, University of Freiburg, 2021. doi:10.13140/RG.2.2.12436.78726. ↩

  15. Julius Fenn, Jessica F. Helm, Philipp Höfele, Lars Kulbe, Andreas Ernst, and Andrea Kiesel. Identifying key-psychological factors influencing the acceptance of yet emerging technologies–a multi-method-approach to inform climate policy. Plos Climate, 2(6):1–25, 2023. Publisher: Public Library of Science. URL: https://journals.plos.org/climate/article?id=10.1371/journal.pclm.0000207 (visited on 2023-06-07), doi:10.1371/journal.pclm.0000207. ↩↩

  16. Sabrina Livanec, Michael Stumpf, Lisa Reuter, Julius Fenn, and Andrea Kiesel. Who’s gonna use this? Acceptance prediction of emerging technologies with Cognitive-Affective Mapping and transdisciplinary considerations in the Anthropocene. The Anthropocene Review, pages 1–20, March 2022. URL: http://journals.sagepub.com/doi/10.1177/20530196221078924 (visited on 2022-07-06), doi:10.1177/20530196221078924. ↩

  17. Jordan Mansell, Steven Mock, Carter Rhea, Adrienne Tecza, and Jinelle Piereder. Measuring attitudes as a complex system: Structured thinking and support for the Canadian carbon tax. Politics and the Life Sciences, 40(2):179–201, 2021. Publisher: Cambridge University Press. URL: https://www.cambridge.org/core/journals/politics-and-the-life-sciences/article/abs/measuring-attitudes-as-a-complex-system/751DA923AFE65ABDC61C4F39F26E151A (visited on 2022-07-06), doi:10.1017/pls.2021.16. ↩