References for decidables

Ainslie, G. (1991). Derivation of “rational” economic behavior from hyperbolic discount curves. The American Economic Review, 81(2), 334–340.
Ainslie, G. (2016). The cardinal anomalies that led to behavioral economics: Cognitive or motivational? Managerial and Decision Economics, 37(4–5), 261–273.
Banks, W. P. (1970). Signal detection theory and human memory. Psychological Bulletin, 74(2), 81–99.
Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psychology, 59(1), 617–645.
Berns, G. S., Laibson, D., & Loewenstein, G. (2007). Intertemporal choice – toward an integrative framework. Trends in Cognitive Sciences, 11(11), 482–488.
Brusilovsky, P. (1994). Explanatory visualization in an educational programming environment: Connecting examples with general knowledge. In B. Blumenthal, J. Gornostaev, & C. Unger (Eds.), Human-Computer Interaction (pp. 202–212). Springer.
Case, N. (2022). Explorable explanations. (Original work published 2015)
Explorable explanation. (2021). In Wikipedia.
Farrell, S., & Lewandowsky, S. (2010). Computational models as aids to better reasoning in psychology. Current Directions in Psychological Science, 19(5), 329–335.
Fyfe, E. R., McNeil, N. M., Son, J. Y., & Goldstone, R. L. (2014). Concreteness fading in mathematics and science instruction: A systematic review. Educational Psychology Review, 26(1), 9–25.
Guest, O., & Martin, A. E. (2021). How computational modeling can force theory building in psychological science. Perspectives on Psychological Science, 16(4), 789–802.
Hayes, J. C., & Kraemer, D. J. M. (2017). Grounded understanding of abstract concepts: The case of STEM learning. Cognitive Research: Principles and Implications, 2(1), 7.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–292.
Karmon-Presser, A., Sheppes, G., & Meiran, N. (2018). How does it “feel”? A signal detection approach to feeling generation. Emotion, 18(1), 94–115.
Lewandowsky, S., & Farrell, S. (2010). Introduction. In Computational modeling in cognition: Principles and practice (pp. 1–34). Sage Publications.
Little, D., & Sommer, F. (2013). Learning and exploration in action-perception loops. Frontiers in Neural Circuits, 7.
Lusted, L. B. (1971). Signal detectability and medical decision-making. Science (New York, N.Y.), 171(3977), 1217–1219.
McClelland, J. L. (2009). The place of modeling in cognitive science. Topics in Cognitive Science, 1(1), 11–38.
Moreira, D., & Barbosa, F. (2019). Delay discounting in impulsive behavior. European Psychologist, 24(4), 312–321.
Papert, S. A. (1993). Mindstorms: Children, computers, and powerful ideas (Subsequent edition). Basic Books.
Papert, S. A. (1999). Eight big ideas behind the constructionist learning lab.
Papert, S. A., Harel, I., & Book, I. H. (1991). Situating constructionism. In Constructionism (pp. 1–11). Ablex Publishing.
Peterson, W., Birdsall, T., & Fox, W. (1954). The theory of signal detectability. Transactions of the IRE Professional Group on Information Theory, 4(4), 171–212.
Stafford, T. (2009). What use are computational models of cognitive processes? In Connectionist models of behaviour and cognition II: Vol. Volume 18 (pp. 265–274). WORLD SCIENTIFIC.
Steel, P. (2007). The nature of procrastination: A meta-analytic and theoretical review of quintessential self-regulatory failure. Psychological Bulletin, 133(1), 65–94.
Tanner, W. P., & Swets, J. A. (1954). A decision-making theory of visual detection. Psychological Review, 61(6), 401–409.
Tran, C., Smith, B., & Buschkuehl, M. (2017). Support of mathematical thinking through embodied cognition: Nondigital and digital approaches. Cognitive Research: Principles and Implications, 2(1), 16.
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297–323.
Victor, B. (2011). Explorable explanations.
Wilson, R. C., & Collins, A. G. (2019). Ten simple rules for the computational modeling of behavioral data. eLife, 8, e49547.
Zuidema, W., French, R. M., Alhama, R. G., Ellis, K., O’Donnell, T. J., Sainburg, T., & Gentner, T. Q. (2020). Five ways in which computational modeling can help advance cognitive science: lessons from artificial grammar learning. Topics in Cognitive Science, 12(3), 925–941.