Abstract
Lee et al. (2019) recently proposed a number of quantitative modeling techniques and practices culminating with a recommendation for the creation of registered model reports. In addition to increasing transparency, trust, and robustness of modeling practices, registered model reports seem likely to increase the visibility and dissemination of quantitative models to researchers from other scientific disciplines (including other behavioral and non-behavioral sciences). In addition to the recommendations proposed by Lee et al. (2019), interdisciplinary communication and collaboration will be improved if researchers include the function of the quantitative model within registered model reports. Here, the function of a quantitative model refers to the contexts (e.g., questions, data types) and goals (e.g., description, prediction, exploration) surrounding the scientist’s use of a model. Explicitly specifying the function of a model will allow for more accurate and fair tests of quantitative models and appropriate tests of model generalizability to novel research questions.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Agrawal, M, Peterson, JC, & Griffiths, TL (2019). Using machine learning to guide cognitive modeling: a case study in moral reasoning. https://arxiv.org/pdf/1902.06744.pdf
Amlung, M., Vedelago, L., Acker, J., Balodis, I., & MacKillop, J. (2017). Steep delay discounting and addictive behavior: a meta-analysis of continuous associations. Addiction, 112, 51–62. https://doi.org/10.1111/add.13535.
Baum, W. M. (2018). Three laws of behavior: allocation , induction, and covariance. Behavior Analysis: Research and Practice, 18, 239–251. https://doi.org/10.1037/bar0000104.
Chaplot, D, MacLellan, C, Salakhutdnov, R, & Koedinger, K (2018). Learning cognitive models using neural networks. https://arxiv.org/pdf/1806.08065.pdf
Chaturvedi, A. R., Hutchinson, G. K., & Nazareth, D. L. (1993). Supporting complex real-time decision making through machine learning. Decision Support Systems, 10, 213–233. https://doi.org/10.1016/0167-9236(93)90039-6.
Dallery, J., & Soto, P. L. (2013). Quantitative description of environment-behavior relations. In G. J. Madden (Ed.), APA handbook of behavior analysis: Vol. I. Methods and principles (pp. 219–249). Washington DC: American Psychological Association.
Durstewitz, D., Koppe, G., & Meyer-Lindenberg, A. (2019). Deep neural networks in psychiatry. Molecular Psychiatry, (ePub ahead of print). https://doi.org/10.1038/s41380-019-0365-9.
Erdfelder, E., Auer, T. S., Hilbig, B. E., Abfalg, A., Moshagen, M., & Nadarevic, L. (2009). Multinomial processing tree models: a review of the literature. Journal of Psychology, 217, 108–124. https://doi.org/10.1027/0044-3409.217.3.108.
Fretwell, S. D., & Lucas, H. L. (1970). On territorial behavior and other factors influencing habitat distribution in birds. I. Theoretical development. Acta Biotheoretica, 19, 16–36.
Herrnstein, R. J. (1961). Relative and absolute strength of response as a function of frequency of reinforcement. Journal of the Experimental Analysis of Behavior, 4, 267–272. https://doi.org/10.1901/jeab.1961.4-267.
Herrnstein, R. J. (1970). On the law of effect. Journal of the Experimental Analysis of Behavior, 13, 243–266.
Hubbard, T. L. (2017). Toward a general theory of momentum-like effects. Behavioural Processes, 141, 50–66. https://doi.org/10.1016/j.beproc.2017.02.019.
Kolodner, J. L. (1991). Improving human decision making through case-based decision aiding. AI Magazine, 12, 52–68. https://doi.org/10.1609/aimag.v12i2.895.
Kraft, J. R., Baum, W. M., & Burge, M. J. (2002). Group choice and individual choices: Modeling human social behavior with the Ideal Free Distribution. Behavioural Processes, 57, 227–240. https://doi.org/10.1016/S0376-6357(02)00016-5.
Lee, MD, Criss, AH, Devezer, B, Donkin, C, Etz, A, Leite FP, Matzke D, Rouder JN, Trueblood JS, White CN, & Vandekerckhove, J (2019). Robust modeling in cognitive science. Computational Brain & Behavior, ePub ahead of print. https://doi.org/10.1007/s42113-019-00029-y.
MacCorquodale, K., & Meehl, P. E. (1948). On a distinction between hypothetical constructs and intervening variables. Psychological Review, 55, 95–107. https://doi.org/10.1037/h0056029.
Marr, M. J. (2015). Reprint of “Mathematics as verbal behavior”. Behavioural Processes, 114, 34–40. https://doi.org/10.1016/j.beproc.2015.03.008.
McKerchar, T. L., & Renda, C. R. (2012). Delay and probability discounting in humans: an overview. The Psychological Record, 62, 817–834. https://doi.org/10.1007/BF03395837.
Meyer, G., Adomavicius, G., Johnson, P. E., Elidrisi, M., Rush, W. A., Sperl-Hillen, J. M., & O’Connor, P. J. (2014). A machine learning approach to improving dynamic decision making. Information Systems Research, 25, 239–263. https://doi.org/10.1287/isre.2014.0513.
Moore, J. (2015). Pragmatism, mathematical models, and the scientific ideal of prediction and control. Behavioural Processes, 114, 2–13. https://doi.org/10.1016/j.beproc.2015.01.007.
Nevin, J. A. (2008). Control, prediction, order, and the joys of research. Journal of the Experimental Analysis of Behavior, 89, 119–123. https://doi.org/10.1901/jeab.2008.89-119.
Nevin, J. A., Mandell, C., & Yarinsky, P. (1981). Response rate and resistance to change in chained schedules. Journal of Experimental Psychology. Animal Behavior Processes, 7, 278–294.
Nevin, J. A., Mandell, C., & Yarinsky, P. (1983). The analysis of behavioral momentum. Journal of the Experimental Analysis of Behavior, 39, 49–59.
Odum, A. L. (2011). Delay discounting: I’m a k, you’re a k. Journal of the Experimental Analysis of Behavior, 96, 427–439. https://doi.org/10.1901/jeab.2011.96-423.
Regenwetter, M., & Robinson, M. M. (2017). The construct-behavior gap in behavioral decision research: a challenge beyond replicability. Psychological Review, 124, 533–550. https://doi.org/10.1037/rev0000067.
Skinner, B. F. (1957). Verbal behavior. New York, NY: Appleton-Century-Crofts.
Stewart, I., & Barnes-Holmes, D. (2001). Understanding metaphor: a relational frame theory perspective. The Behavior Analyst, 24, 191–199.
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5, 297–323.
Vanderveldt, A., Oliveira, L., & Green, L. (2016). Delay discounting: pigeon, rate, human – does it matter? Journal of Experimental Psychology: Animal Learning and Cognition, 42, 141–162. https://doi.org/10.1037/xan0000097.
Walsh, C. G., Ribeiro, J. D., & Franklin, J. C. (2018). Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning. Journal of Child Psychology and Psychiatry, 59, 1261–2170. https://doi.org/10.1111/jcpp.12916.
Zhou, M., Yoshimi Fukuoka, Y., Mintz, Y., Goldberg, K., Kaminsky, P., Flowers, E., & Aswani, A. (2018). Evaluating machine learning-based automated personalized daily step goals delivered through a mobile phone app: randomized controlled trial. JMIR mHealth and uHealth, 6, e28. https://doi.org/10.2196/mhealth.9117.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The author declares no conflict of interest.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Cox, D.J. The Many Functions of Quantitative Modeling. Comput Brain Behav 2, 166–169 (2019). https://doi.org/10.1007/s42113-019-00048-9
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42113-019-00048-9