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Measuring attitudes as a complex system

Structured thinking and support for the Canadian carbon tax

Published online by Cambridge University Press:  10 November 2021

Jordan Mansell ,
Steven Mock ,
Carter Rhea ,
Adrienne Tecza  and
Jinelle Piereder
Jordan Mansell*
Affiliation:
Western University, Network for Economic and Social Trends
Steven Mock
Affiliation:
University of Waterloo, Balsillie School of International Affairs
Carter Rhea
Affiliation:
Université de Montréal
Adrienne Tecza
Affiliation:
Colorado Center for Civic Learning and Engagement
Jinelle Piereder
Affiliation:
University of Waterloo
*
Correspondence: Jordan Mansell, Western University, Network for Economic and Social Trends, London, Ontario, Canada Email: jmansel3@uwo.ca
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Abstract

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

Keywords

political attitudespolitical ideologynetwork analysislatent propertiescognitive-affective mappingcarbon tax
Type
Special Issue: Psychophysiology, Cognition, and Political Differences
Information
Politics and the Life Sciences , Volume 40 , Issue 2: SPECIAL ISSUE: PSYCHOPHYSIOLOGY, COGNITION, AND POLITICAL DIFFERENCES , Fall 2021 , pp. 179 - 201
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the Association for Politics and the Life Sciences

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References

Abel, N., Ross, H., & Walker, P. (1998). Mental models in rangeland research, communication and management. The Rangeland Journal, 20(1), 77–91.CrossRefGoogle Scholar
Abelson, R. P., Kinder, D. R., Peters, M. D., & Fiske, S. T. (1982). Affective and semantic components in political person perception. Journal of personality and social psychology , 42(4), 619.CrossRefGoogle Scholar
Achen, C. H. (2002). Parental socialization and rational party identification. Political Behavior, 24(2), 151–170.CrossRefGoogle Scholar
Airoldi, E. M., Blei, D. M., Fienberg, S. E., Goldenberg, A., Xing, E. P., & Zheng, A. X. (Eds.). (2008). Statistical network analysis: Models, issues, and new directions: ICML 2006 Workshop on Statistical Network Analysis, Pittsburgh, PA, USA, June 29, 2006, revised selected papers (Vol. 4503). Springer.Google Scholar
Arceneaux, K., & Vander Wielen, R. J. (2017). Taming intuition: How reflection minimizes partisan reasoning and promotes democratic accountability. Cambridge University Press.CrossRefGoogle Scholar
Austin, D. E. (1994). Incorporating cognitive theory into environmental policymaking. The Environmental Professional , 16(3), 262–274.Google Scholar
Ausubel, D. P., Novak, J. D., & Hanesian, H. (1968). Educational psychology: A cognitive view. Holt, Rinehart and Winston.Google Scholar
Axelrod, R. (Ed.). (1976). Structure of decision: The cognitive maps of political elites. Princeton University Press.Google Scholar
Bucy, E. P. (2000). Emotional and evaluative consequences of inappropriate leader displays. Communication Research, 27(2), 194–226.CrossRefGoogle Scholar
Bucy, E. P., & Bradley, S. D. (2004). Presidential expressions and viewer emotion: Counterempathic responses to televised leader displays. Social Science Information, 43(1), 59–94.CrossRefGoogle Scholar
Carley, K., & Palmquist, M. (1992). Extracting, representing, and analyzing mental models. Social forces, 70(3), 601–636.CrossRefGoogle Scholar
Carney, D. R., Jost, J. T., Gosling, S. D., & Potter, J. (2008). The secret lives of liberals and conservatives: Personality profiles, interaction styles, and the things they leave behind. Political Psychology, 29(6), 807–840.CrossRefGoogle Scholar
Collins, A., & Gentner, D. (1987). How people construct mental models. In Holland, D. & Quinn, N. (Eds.), Cultural models in language and thought (pp. 243–268). Cambridge University Press.CrossRefGoogle Scholar
Conway, L. G. III, Gornick, L. J., Houck, S. C., Anderson, C., Stockert, J., Sessoms, D., & McCue, K. (2016). Are conservatives really more simple‐minded than liberals? The domain specificity of complex thinking. Political Psychology, 37(6), 777–798.CrossRefGoogle Scholar
Cornelius, R. R. (1996). The science of emotion: Research and tradition in the psychology of emotions. Prentice-Hall, Inc.Google Scholar
Craik, K. J. W. (1943). The nature of explanation. Cambridge University Press.Google Scholar
Cranmer, S. J., Leifeld, P., McClurg, S. D., & Rolfe, M. (2017). Navigating the range of statistical tools for inferential network analysis. American Journal of Political Science, 61(1), 237–251.CrossRefGoogle Scholar
Crawford, B., Gera, R., House, J., Knuth, T., & Miller, R. (2016). Graph structure similarity using spectral graph theory. In Cherifi, H., Gaito, S., Quattrociocchi, W., & Sala, A. (Eds.), International Workshop on Complex Networks and Their Applications V (pp. 209–221). Springer.Google Scholar
Damasio, A. R. (1994). Descartes’ error: Emotion, rationality and the human brain. New York: Putnam, 352.Google Scholar
Davies, I., Green, P., Rosemann, M., Indulska, M., & Gallo, S. (2006). How do practitioners use conceptual modeling in practice? Data & Knowledge Engineering, 58(3), 358–380.CrossRefGoogle Scholar
Delli Carpini, M. X., & Keeter, S. (1996). What Americans know about politics and why it matters. Yale University Press.Google Scholar
Dickerson, J. A., & Kosko, B. (1993, October). Hybrid fuzzy ellipsoidal learning. In Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan) (Vol. 3, pp. 2853–2856). IEEE.Google Scholar
Dickerson, J. A., & Kosko, B. (1996a). Fuzzy function approximation with ellipsoidal rules. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(4), 542–560.CrossRefGoogle Scholar
Dickerson, J. A., & Kosko, B. (1996b). Virtual worlds as fuzzy dynamical systems. In Multimedia technology for applications (pp. 567–603) IEEE.Google Scholar
Dodd, M. D., Balzer, A., Jacobs, C. M., Gruszczynski, M. W., Smith, K. B., & Hibbing, J. R. (2012). The political left rolls with the good and the political right confronts the bad: Connecting physiology and cognition to preferences. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 367(1589), 640–649.CrossRefGoogle ScholarPubMed
Dray, A., Perez, P., Jones, N., Le Page, C., D’Aquino, P., White, I., and Auatabu, T. (2006). The AtollGame experience: From knowledge engineering to a computer-assisted role playing game. Journal of Artificial Societies and Social Simulation, 9(1), 1–11.Google Scholar
Druckman, J. N., & Lupia, A. (2000). Preference formation. Annual Review of Political Science, 3, 1–24.CrossRefGoogle Scholar
Eidelman, S., Crandall, C. S., Goodman, J. A., & Blanchar, J. C. (2012). Low-effort thought promotes political conservatism. Personality and Social Psychology Bulletin, 38(6), 808–820.CrossRefGoogle ScholarPubMed
Estrada, E., Hatano, N., & Gutierrez, A. (2008). “Clumpiness” mixing in complex networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(3), P03008.CrossRefGoogle Scholar
Faust, K. (2006). Comparing social networks: Size, density, and local structure. Metodoloski zvezki , 3(2), 185–216.Google Scholar
Faust, K., & Skvoretz, J. (2002). Comparing networks across space and time, size and species. Sociological Methodology , 32(1), 267–299.CrossRefGoogle Scholar
Findlay, S. D., & Thagard, P. (2014). Emotional change in international negotiation: Analyzing the Camp David accords using cognitive-affective maps. Group Decision and Negotiation, 23, 1281–1300.CrossRefGoogle Scholar
Fisher, R., Ury, W. (1983). Getting to Yes: Negotiating Agreement Without Giving In. Penguin Books.Google Scholar
Fontaine, J., Scherer, K., Roesch, E., & Ellsworth, P. (2007). The world of emotions is not two-dimensional. Psychological Science 18(12): 1050–1057.CrossRefGoogle Scholar
Forrester, J. W. (1971). Counterintuitive behavior of social systems. Theory and Decision, 2(2), 109–140.CrossRefGoogle Scholar
Foster, P. S., Roosa, K. M., Drago, V., Branch, K., Finney, G., & Heilman, K. M. (2013). Recall of word lists is enhanced with increased spreading activation. Aging, Neuropsychology, and Cognition, 20(5), 553–566.CrossRefGoogle ScholarPubMed
Gentner, D., & Gentner, D. R. (1983). Flowing waters or teeming crowds: Mental models of electricity. In Gentner, D. & Stevens, A. (Eds.), Mental models (pp. 99–130). Lawrence Erlbaum.Google Scholar
Ghali, N., Panda, M., Hassanien, A. E., Abraham, A., & Snasel, V. (2012). Social networks analysis: Tools, measures and visualization. In Abraham, A. (Ed.), Computational social networks (pp. 3–23). Springer.CrossRefGoogle Scholar
Goren, P. (2004). Political sophistication and policy reasoning: A reconsideration. American Journal of Political Science, 48(3), 462–478.CrossRefGoogle Scholar
Grabe, M. E., & Bucy, E. P. (2009). Image bite politics: News and the visual framing of elections. Oxford University Press.CrossRefGoogle Scholar
Gray, S. A., Gray, S., De Kok, J. L., Helfgott, A. E., O’Dwyer, B., Jordan, R., & Nyaki, A. (2015). Using fuzzy cognitive mapping as a participatory approach to analyze change, preferred states, and perceived resilience of social-ecological systems. Ecology and Society, 20(2), article 11.CrossRefGoogle Scholar
Gray, S. A., Mellor, D. T., Jordan, R. C., Crall, A., & Newman, G. (2014, June). Modeling with citizen scientists: Using community-based modeling tools to develop citizen science projects [Paper presentation]. 7th International Congress on Environmental Modelling and Software, San Diego, CA.Google Scholar
Gross, J. J. (2013). Emotion regulation: taking stock and moving forward. Emotion, 13(3), 359.CrossRefGoogle ScholarPubMed
Hage, P., & Harary, F. (1983). Structural models in anthropology. Oxford University Press.Google Scholar
Hibbing, J. R., Smith, K. B., & Alford, J. R. (2014). Differences in negativity bias underlie variations in political ideology. Behavioral and Brain Sciences, 37(3), 297–307.CrossRefGoogle ScholarPubMed
Hill, R. D., Allen, C., & McWhorter, P. (1991). Stories as a mnemonic aid for older learners. Psychology and Aging , 6(3), 484–486.CrossRefGoogle ScholarPubMed
Hinze, T., Doster, J., & Joe, V. C. (1997). The relationship of conservatism and cognitive-complexity. Personality and Individual Differences, 22(2), 297–298.CrossRefGoogle Scholar
Hobbs, B. F., Ludsin, S. A., Knight, R. L., Ryan, P. A., Biberhofer, J., & Ciborowski, J. J. (2002). Fuzzy cognitive mapping as a tool to define management objectives for complex ecosystems. Ecological Applications, 12(5), 1548–1565.CrossRefGoogle Scholar
Hoffman, M., Lubell, M., & Hillis, V. (2014). Linking knowledge and action through mental models of sustainable agriculture. Proceedings of the National Academy of Sciences, 111(36), 13016–13021.CrossRefGoogle ScholarPubMed
Homer-Dixon, T., Maynard, J. L., Mildenberger, M., Milkoreit, M., Mock, S. J., Quilley, S., Schröder, T., & Thagard, P. (2013). A complex systems approach to the study of ideology: Cognitive-affective structures and the dynamics of belief systems. Journal of Social and Political Science, 1(1), 337–363.Google Scholar
Johnson-Laird, P. N. (1983). Mental models. Cambridge University Press.Google Scholar
Johnson-Laird, P. N. (1989). Mental models. In Posner, M. I. (Ed.), Foundations of cognitive science (pp. 467–499). MIT Press.Google Scholar
Johnson-Laird, P. N., & Byrne, R. M. (1991). Deduction. Lawrence Erlbaum.Google Scholar
Jones, N. A., Ross, H., Lynam, T., Perez, P., & Leitch, A. (2011). Mental models: An interdisciplinary synthesis of theory and methods. Ecology and Society, 16(1), article 46.CrossRefGoogle Scholar
Jost, J. T., Glaser, J., Kruglanski, A. W., & Sulloway, F. J. (2003). Political conservatism as motivated social cognition. Psychological Bulletin , 129(3), 339–375.CrossRefGoogle ScholarPubMed
Jost, J. T., Kruglanski, A. W., & Simon, L. (1999). Effects of epistemic motivation on conservatism, intolerance, and other system justifying attitudes. In Thompson, L. L., Levine, J. M., & Messick, D. M. (Eds.), Shared cognition in organizations: The management of knowledge (pp. 91–116). Psychology Press.CrossRefGoogle Scholar
Kanai, R., Feilden, T., Firth, C., & Rees, G. (2011). Political orientations are correlated with brain structure in young adults. Current Biology, 21(8), 677–680.CrossRefGoogle ScholarPubMed
Kearney, A. R., Bradley, G., Kaplan, R., & Kaplan, S. (1999). Stakeholder perspectives on appropriate forest management in the Pacific Northwest. Forest Science, 45(1), 62–73.Google Scholar
Kearney, A. R., & Kaplan, S. (1997). Toward a methodology for the measurement of knowledge structures of ordinary people: The conceptual content cognitive map (3CM). Environment and Behavior, 29(5), 579–617.CrossRefGoogle Scholar
Kok, K. (2009). The potential of fuzzy cognitive maps for semi-quantitative scenario development, with an example from Brazil. Global Environmental Change, 19(1), 122–133.CrossRefGoogle Scholar
Kolaczyk, E. D. (2009). Statistical analysis of network data. Springer.CrossRefGoogle Scholar
Kolaczyk, E. D., & Csárdi, G. (2014). Statistical analysis of network data with R (Vol. 65). Springer.CrossRefGoogle Scholar
Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24(1), 65–75.CrossRefGoogle Scholar
Kosko, B. (1987). Adaptive inference in fuzzy knowledge networks. In Proceedings of the First IEEE International Conference on Neural Networks (ICNN-86, San Diego, CA (pp. 261–268). IEEE.Google Scholar
Kosko, B. (1988). Hidden patterns in combined and adaptive knowledge networks. International Journal of Approximate Reasoning, 2(4), 377–393.CrossRefGoogle Scholar
Kosko, B. (1992). Neural networks and fuzzy systems: A dynamical systems approach to machine intelligence. Prentice Hall.Google Scholar
Kosko, B. (1993). Adaptive inference in fuzzy knowledge networks. In Dubois, D., Prade, H., & Yager, R. R., Readings in fuzzy sets for intelligent systems (pp. 888–891). Morgan Kaufman.CrossRefGoogle Scholar
Laszlo, E., Artigiani, R., Combs, A., & Csányi, V. (1996). Changing visions: Human cognitive maps: Past, present, and future. Praeger/Greenwood.Google Scholar
Leiserowitz, A., Maibach, E., Roser-Renouf, C., Rosenthal, S., & Cutler, M. (2017, July 5). Climate change in the American mind: May 2017. Yale Program on Climate Change Communication. https://climatecommunication.yale.edu/publications/climate-change-american-mind-may-2017/Google Scholar
Loewenstein, G. F., Weber, E. U., Hsee, C. K., & Welch, N. (2001). Risk as feelings. Psychological bulletin, 127(2), 267.CrossRefGoogle ScholarPubMed
Luskin, R. C. (2002). Political psychology, political behavior, and politics: Questions of aggregation, causal distance, and taste. In Kuklinski, J. H. (Ed.), Thinking about political psychology (pp. 217–250). Cambridge University Press.CrossRefGoogle Scholar
Mansell, J., Reuter, L., Rhea, C., & Kiesel, A. (2021). A novel network approach to capture cognition and affect: COVID-19 experiences in Canada and Germany. Frontiers in Psychology, 12, 2082.CrossRefGoogle ScholarPubMed
Marcus, G. E. (2003). The psychology of emotion and politics. In Sears, D. O., Huddy, L., & Jervis, R. (Eds.), Oxford handbook of political psychology (pp. 182–221). Oxford University Press.Google Scholar
Marcus, G. E., Neuman, W. R., & MacKuen, M. (2000). Affective intelligence and political judgment. University of Chicago Press.Google Scholar
Maynard, J. L. (2013). A map of the field of ideological analysis. Journal of Political Ideologies, 18(3), 299–327.CrossRefGoogle Scholar
Maynard, J. L., & Mildenberger, M. (2018). Convergence and divergence in the study of ideology: A critical review. British Journal of Political Science, 48(2), 563–589.CrossRefGoogle Scholar
Milkoreit, M. (2017). Mindmade politics: The cognitive roots of international climate governance . MIT Press.CrossRefGoogle Scholar
Morgan, R. L., and Heise, D. (1988). Structure of emotions. Social Psychology Quarterly, 51(1), 19–31.CrossRefGoogle Scholar
Novak, J. D. (1998). Learning, creating, and using knowledge: Concept maps as facilitative tools in schools and corporations. Lawrence Erlbaum.CrossRefGoogle Scholar
Novak, J. D., & Gowin, D. B. (1984). Learning how to learn. Cambridge University Press.CrossRefGoogle Scholar
Nussbaum, M. (1996). Compassion: The basic social emotion. Social Philosophy and Policy, 13(1), 27–58.CrossRefGoogle Scholar
Özesmi, U., & Özesmi, S. L. (2004). Ecological models based on people’s knowledge: a multi-step fuzzy cognitive mapping approach. Ecological modelling, 176(1-2), 43–64.CrossRefGoogle Scholar
Paiva, B. M. R., Ferreira, F. A. F., Carayannis, E. G., Zopounidis, C., Ferreira, J. J. M., Pereira, L. F., & Dias, P. J. (2020). Strategizing sustainability in the banking industry using fuzzy cognitive maps and system dynamics. International Journal of Sustainable Development & World Ecology, 28(2), 93–108.CrossRefGoogle Scholar
Papageorgiou, E., & Kontogianni, A. (2012). Using fuzzy cognitive mapping in environmental decision making and management: a methodological primer and an application. International Perspectives on Global Environmental Change, 427–450.CrossRefGoogle Scholar
Puccia, C. J. (1983). Qualitative models for East Coast benthos. In Lauenroth, W. K., Skogerboe, G. V., & Flug, M. (Eds.), Analysis of ecological systems: State-of-the-art in ecological modelling (pp. 719–724). Elsevier.CrossRefGoogle Scholar
Raiffa, H. (1982). The art and science of negotiation. Harvard University Press.Google Scholar
Redlawsk, D. P. (2002). Hot cognition or cool consideration? Testing the effects of motivated reasoning on political decision making. The Journal of Politics, 64(4), 1021–1044.CrossRefGoogle Scholar
Redlawsk, D. P. (2006). Motivated reasoning, affect, and the role of memory in voter decision making. In Feeling politics (pp. 87–107). Palgrave Macmillan, New York.CrossRefGoogle Scholar
Roberts, M., Byg, A., Faccioli, M., Novo, P., & Kyle, C. (2021). Stakeholder perceptions of public good provision from agriculture and implications for governance mechanism design. Journal of Environmental Planning and Management, 64(2), 289–307.CrossRefGoogle Scholar
Rogers, K. B., Schröder, T., & von Scheve, C. (2014). Dissecting the sociality of motion: A multilevel approach. Emotion Review, 6(2), 124–133.CrossRefGoogle Scholar
Sapolsky, R. M. (2017). Be have: The biology of humans at our best and worst. Penguin.Google Scholar
Saver, J. L., & Damasio, A. R. (1991). Preserved access and processing of social knowledge in a patient with acquired sociopathy due to ventromedial frontal damage. Neuropsychologia, 29(12), 1241–1249.CrossRefGoogle Scholar
Scherer, K., Dan, E., & Flykt, A. (2006). What determines a feeling’s position in affective space? A case for appraisal. Cognition and Emotion, 20(1), 92–113.CrossRefGoogle Scholar
Schreiber, D., Fonzo, G., Simmons, A. N., Dawes, C. T., Flagan, T., Fowler, J. H., & Paulus, M. P. (2013). Red brain, blue brain: Evaluative processes differ in Democrats and Republicans. PLOS ONE, 8(2), e52970.CrossRefGoogle ScholarPubMed
Sears, D. O., Lau, R. R., Tyler, T. R., & Allen, H. M. (1980). Self-interest vs. symbolic politics in policy attitudes and presidential voting. American Political Science Review, 74(3), 670–684.CrossRefGoogle Scholar
Sharif, A. M., & Irani, Z. (2006). Exploring fuzzy cognitive mapping for IS evaluation. European Journal of Operational Research, 173(3), 1175–1187.CrossRefGoogle Scholar
Simon, D., Stenstrom, D. M., & Read, S. J. (2015). The coherence effect: Blending cold and hot cognitions. Journal of Personality and Social Psychology, 109(3), 369–394.CrossRefGoogle ScholarPubMed
Sniderman, P. M., Brody, R. A., & Tetlock, P. E. (1993). Reasoning and choice: Explorations in political psychology. Cambridge University Press.Google Scholar
Sowa, J. F. (1999). Knowledge representation: Logical, philosophical, and computational foundations. Brooks Cole.Google Scholar
Stone-Jovicich, S. S., Lynam, T., Leitch, A., & Jones, N. A. (2011). Using consensus analysis to assess mental models about water use and management in the Crocodile River catchment, South Africa. Ecology and Society, 16(1), article 45.CrossRefGoogle Scholar
Talhelm, T., Haidt, J., Oishi, S., Zhang, X., Miao, F. F., & Chen, S. (2015). Liberals think more analytically (more “WEIRD”) than conservatives. Personality and Social Psychology Bulletin, 41(2), 250–267.CrossRefGoogle ScholarPubMed
Tasker, John Paul. (2021, March 20). Conservative delegates reject adding “climate change is real” to the policy book. CBC News. https://www.cbc.ca/news/politics/conservative-delegates-reject-climate-change-is-real-1.5957739Google Scholar
Tetlock, P. E. (1984). Cognitive style and political belief systems in the British House of Commons. Journal of Personality and Social Psychology , 46(2), 365–375.CrossRefGoogle Scholar
Thagard, P. (2000). Coherence in thought and action. MIT Press.CrossRefGoogle Scholar
Thagard, P. (2006). Hot thought: Mechanisms and applications of emotional cognition. MIT Press.CrossRefGoogle Scholar
Thagard, P. (2010). EMPATHICA: A computer support system with visual representations for cognitive-affective mapping. In McGregor, K. (Ed.), Proceedings of the Workshop on Visual Reasoning and Representation (pp. 79–81). AAAI Press.Google Scholar
Thagard, P. (2011). The brain is wider than the sky: Analogy, emotion, and allegory. Metaphor and Symbol, 26(2), 131–142.CrossRefGoogle Scholar
Thagard, P. (2012a). The cognitive science of science: Explanation, discovery, and conceptual change. MIT Press.CrossRefGoogle Scholar
Thagard, P. (2012b). Mapping minds across cultures. In Sun, R. (Ed.), Grounding social sciences in cognitive sciences (pp. 35–62). MIT Press.Google Scholar
Thagard, P. (2014). The cognitive-affective structure of political ideologies. In Martinovski, B. (Ed.), Emotion in group decision and negotiation (pp. 51–71). Springer.Google Scholar
Thagard, P. (2018). Social equality: Cognitive modeling based on emotional coherence explains attitude change. Policy Insights from the Behavioral and Brain Sciences, 5(2), 247–256.CrossRefGoogle Scholar
Thagard, P. (2019). Mind-society: From brains to social sciences and professions. Oxford University Press.CrossRefGoogle Scholar
Tikkanen, J., Isokääntä, T., Pykäläinen, J., & Leskinen, P. (2006). Applying cognitive mapping approach to explore the objective–structure of forest owners in a Northern Finnish case area. Forest Policy and Economics, 9(2), 139–152.CrossRefGoogle Scholar
Tolman, E. C. (1948). Cognitive maps in rats and men. Psychological review, 55(4), 189.CrossRefGoogle ScholarPubMed
Vanermen, I., Muys, B., Verheyen, K., Vanwindekens, F., Bouriaud, L., Kardol, P., & Vranken, L. (2020). What do scientists and managers know about soil biodiversity? Comparative knowledge mapping for sustainable forest management. Forest Policy and Economics, 119, 102264.CrossRefGoogle Scholar
Varela, F. J., Thompson, E., & Rosch, E. (2017). The embodied mind, Revised Edition: Cognitive science and human experience. MIT press.CrossRefGoogle Scholar
Vohs, Kathleen D., Baumeister, Roy F., and Loewenstein, George. (2007). Do Emotions Help Or Hurt Decision Making?: A Hedgefoxian Perspective. Russell Sage Foundation.Google Scholar
Vomiero, Jessica. (2019, August 19). Elections Canada slammed after warning groups climate change may be “partisan” issue. Global News. https://globalnews.ca/news/5783619/elections-canada-criticism-climate-change-warning/Google Scholar
Ward, M. D., Stovel, K., & Sacks, A. (2011). Network analysis and political science. Annual Review of Political Science, 14, 245–264.CrossRefGoogle Scholar
Wei, W., Joseph, K., Liu, H., & Carley, K. M. (2015, August). The fragility of Twitter social networks against suspended users. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015 (pp. 9–16). IEEE.CrossRefGoogle Scholar
Zaller, J. R. (1992). The nature and origins of mass opinion. Cambridge University Press.CrossRefGoogle Scholar
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