References for decidables

Ainslie, G. (1991). Derivation of “rational” economic behavior from hyperbolic discount curves. The American Economic Review, 81(2), 334–340. http://www.jstor.org/stable/2006881
Ainslie, G. (2016). The cardinal anomalies that led to behavioral economics: Cognitive or motivational? Managerial and Decision Economics, 37(4–5), 261–273. https://doi.org/10.1002/mde.2715
Banks, W. P. (1970). Signal detection theory and human memory. Psychological Bulletin, 74(2), 81–99. https://doi.org/10.1037/h0029531
Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psychology, 59(1), 617–645. https://doi.org/10.1146/annurev.psych.59.103006.093639
Berns, G. S., Laibson, D., & Loewenstein, G. (2007). Intertemporal choice – toward an integrative framework. Trends in Cognitive Sciences, 11(11), 482–488. https://doi.org/10.1016/j.tics.2007.08.011
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. https://doi.org/10.1007/3-540-58648-2_38
Case, N. (2022). Explorable explanations. https://github.com/explorableexplanations/explorableexplanations.github.io (Original work published 2015)
Explorable explanation. (2021). In Wikipedia. https://en.wikipedia.org/w/index.php?title=Explorable_explanation&oldid=1048302937
Farrell, S., & Lewandowsky, S. (2010). Computational models as aids to better reasoning in psychology. Current Directions in Psychological Science, 19(5), 329–335. https://doi.org/10.1177/0963721410386677
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. https://doi.org/10.1007/s10648-014-9249-3
Guest, O., & Martin, A. E. (2021). How computational modeling can force theory building in psychological science. Perspectives on Psychological Science, 16(4), 789–802. https://doi.org/10.1177/1745691620970585
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. https://doi.org/10.1186/s41235-016-0046-z
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–292. https://doi.org/10.2307/1914185
Karmon-Presser, A., Sheppes, G., & Meiran, N. (2018). How does it “feel”? A signal detection approach to feeling generation. Emotion, 18(1), 94–115. https://doi.org/10.1037/emo0000298
Lewandowsky, S., & Farrell, S. (2010). Introduction. In Computational modeling in cognition: Principles and practice (pp. 1–34). Sage Publications. https://doi.org/10.4135/9781483349428.n1
Little, D., & Sommer, F. (2013). Learning and exploration in action-perception loops. Frontiers in Neural Circuits, 7. https://www.frontiersin.org/article/10.3389/fncir.2013.00037
Lusted, L. B. (1971). Signal detectability and medical decision-making. Science (New York, N.Y.), 171(3977), 1217–1219. https://doi.org/10.1126/SCIENCE.171.3977.1217
McClelland, J. L. (2009). The place of modeling in cognitive science. Topics in Cognitive Science, 1(1), 11–38. https://doi.org/10.1111/j.1756-8765.2008.01003.x
Moreira, D., & Barbosa, F. (2019). Delay discounting in impulsive behavior. European Psychologist, 24(4), 312–321. https://doi.org/10.1027/1016-9040/a000360
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. http://stager.org/articles/8bigideas.pdf
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. https://doi.org/10.1109/TIT.1954.1057460
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. https://doi.org/10.1142/9789812834232_0022
Steel, P. (2007). The nature of procrastination: A meta-analytic and theoretical review of quintessential self-regulatory failure. Psychological Bulletin, 133(1), 65–94. https://doi.org/10.1037/0033-2909.133.1.65
Tanner, W. P., & Swets, J. A. (1954). A decision-making theory of visual detection. Psychological Review, 61(6), 401–409. https://doi.org/10.1037/h0058700
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. https://doi.org/10.1186/s41235-017-0053-8
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297–323. https://doi.org/10.1007/BF00122574
Victor, B. (2011). Explorable explanations. http://worrydream.com/ExplorableExplanations/
Wilson, R. C., & Collins, A. G. (2019). Ten simple rules for the computational modeling of behavioral data. eLife, 8, e49547. https://doi.org/10.7554/eLife.49547
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. https://doi.org/10.1111/tops.12474