Abstract
In this chapter counterfactual thinking is addressed based on literature mainly from Neuroscience and Psychology. A detailed literature review was conducted in identifying processes, neural correlates and theories related to counterfactual thinking from different disciplines. A familiar scenario with respect to counterfactual thinking was identified. Based on the literature, an adaptive self-modeling network model was designed. This model captures the complex process of counterfactual thinking, the mental models that are involved, and the learning and control.
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References
Barbey, A.K., Krueger, F., Grafman, J.: Structured event complexes in the medial prefrontal cortex support counterfactual representations for future planning. Philosophical Trans. Royal Soc. B: Biol. Sci. 364(1521), 1291–1300 (2009)
Bhalwankar, R., Treur, J.: Modeling the development of internal mental models by an adaptive network model. In: Proceedings of the 11th Annual International Conference on Brain-Inspired Cognitive Architectures for AI, BICA*AI’20. Procedia Computer Science, vol. 190(4), pp. 90–101. Elsevier (2021a)
Bhalwankar, R., Treur, J.: A second-order adaptive network model for learner-controlled mental model learning processes. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardpo, M. (eds), Proceedings of the 9th International Conference on Complex Networks and Their Applications. Studies in Computational Intelligence, vol. 944, pp. 245–259. Springer Nature Switzerland AG (2021b)
Bhalwankar, R., Treur, J.: ‘If only I would have done that...‘: a controlled adaptive network model for learning by counterfactual thinking. In: Proceedings of the 17th International Conference on Artificial Intelligence Applications and Innovations, AIAI’21, pp. 3–16. Advances in Information and Communication Technology, vol. 627. Springer Nature Switzerland (2021c)
Byrne, R.M.J.: Mental models and counterfactual thoughts about what might have been. Trends Cogn. Sci. 6(10), 426–431 (2002)
Byrne, R.M.J.: The Rational Imagination: How People Create Alternatives to Reality. MIT Press (2005)
Byrne, R.M.J.: Precis of ‘the rational imagination: how people create alternatives to reality.’ Behavior. Brain Sci. 30(5–6), 439–453 (2007)
Byrne, R.M.J.: Counterfactual thought. Annu. Rev. Psychol. 67, 135–157 (2016)
De Brigard, F., Hanna, E., St Jacques, P.L., Schacter, D.L.: How thinking about what could have been affects how we feel about what was. Cogn. Emot. 33, 646–659 (2019)
Epstude, K., Roese, N.J.: The functional theory of counterfactual thinking. Pers. Soc. Psychol. Rev. 12(2), 168–192 (2008)
Fortin, N.J., Agster, K.L., Eichenbaum, H.B.: Critical role of the hippocampus in memory for sequences of events. Nat. Neurosci. 5(5), 458–462 (2002)
Kahneman, D., Miller, D.T.: Norm theory: comparing reality to its alternatives. Psychol. Rev. 93(2), 136 (1986)
Markman, K.D., Gavanski, I., Sherman, S.J., McMullen, M.N.: The mental simulation of better and worse possible worlds. J. Exp. Soc. Psychol. 29(1), 87–109 (1993)
Roese, N.J.: The functional basis of counterfactual thinking. J. Pers. Soc. Psychol. 66(5), 805 (1994)
Russell, J.A.: Core affect and the psychological construction of emotion. Psychol. Rev. 110(1), 145 (2003)
Sanna, L.J., Schwarz, N., Small, E.M.: Accessibility experiences and the hindsight bias: I knew it all along versus it could never have happened. Mem. Cognit. 30(8), 1288–1296 (2002)
Starr, W.B.: Conditional and counterfactual logic. In: Knauff, M., Spohn, W. (eds.). The Handbook of Rationality. MIT Press: Cambridge, MA (2020)
Timberlake, B.: The effects of counterfactual comparison on learning and reasoning (Doctoral dissertation, University of Trento) (2019)
Treur, J.: Network-Oriented Modeling: Addressing Complexity of Cognitive, Affective and Social Interactions. Springer Nature, Cham, Switzerland (2016)
Treur, J.: Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models. Springer Nature, Cham, Switzerland (2020)
Treur, J.: With a little help: a modeling environment for self-modeling network models. In: Treur, J., Van Ments, L. (eds.) Mental Models and their Dynamics, Adaptation and Control: a Self-Modeling Network Modeling Approach, Ch. 17 (this volume). Springer Nature (2022)
Tulving, E., Markowitsch, H.J.: Episodic and declarative memory: role of the hippocampus. Hippocampus 8(3), 198–204 (1998)
Van Hoeck, N., Watson, P.D., Barbey, A.K.: Cognitive neuroscience of human counterfactual reasoning. Front. Hum. Neurosci. 9, 420 (2015)
Wang, Y., Wan, Y., Zhang, C., Bai, L., Cui, L., Yu, P.: Competitive multi-agent deep reinforcement learning with counterfactual thinking. In 2019 IEEE International Conference on Data Mining (ICDM), pp. 1366–1371. IEEE (2019)
Wilson, R.C., Collins, A.G.: Ten simple rules for the computational modeling of behavioral data. Elife 8, e49547 (2019)
Roese, N. J., Epstude, K.: The functional theory of counterfactual thinking: New evidence, new challenges, new insights. In Advances in experimental social psychology, vol. 56, pp. 1–79. Academic Press (2017)
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Bhalwankar, R., Treur, J. (2022). ‘What if I Would Have Done Otherwise…’: A Controlled Adaptive Network Model for Mental Models in Counterfactual Thinking. In: Treur, J., Van Ments, L. (eds) Mental Models and Their Dynamics, Adaptation, and Control. Studies in Systems, Decision and Control, vol 394. Springer, Cham. https://doi.org/10.1007/978-3-030-85821-6_6
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