TY - GEN
T1 - Using Machine Learning to Guide Cognitive Modeling
T2 - 41st Annual Meeting of the Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019
AU - Agrawal, Mayank
AU - Peterson, Joshua C.
AU - Griffiths, Thomas L.
N1 - Funding Information:
We thank Edmond Awad for providing guidance on navigating the Moral Machine dataset.
Publisher Copyright:
© Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019.All rights reserved.
PY - 2019
Y1 - 2019
N2 - Large-scale behavioral datasets enable researchers to use complex machine learning algorithms to better predict human behavior, yet this increased predictive power does not always lead to a better understanding of the behavior in question. In this paper, we outline a data-driven, iterative procedure that allows cognitive scientists to use machine learning to generate models that are both interpretable and accurate. We demonstrate this method in the domain of moral decision-making, where standard experimental approaches often identify relevant principles that influence human judgments, but fail to generalize these findings to “real world” situations that place these principles in conflict. The recently released Moral Machine dataset allows us to build a powerful model that can predict the outcomes of these conflicts while remaining simple enough to explain the basis behind human decisions.
AB - Large-scale behavioral datasets enable researchers to use complex machine learning algorithms to better predict human behavior, yet this increased predictive power does not always lead to a better understanding of the behavior in question. In this paper, we outline a data-driven, iterative procedure that allows cognitive scientists to use machine learning to generate models that are both interpretable and accurate. We demonstrate this method in the domain of moral decision-making, where standard experimental approaches often identify relevant principles that influence human judgments, but fail to generalize these findings to “real world” situations that place these principles in conflict. The recently released Moral Machine dataset allows us to build a powerful model that can predict the outcomes of these conflicts while remaining simple enough to explain the basis behind human decisions.
KW - machine learning
KW - moral psychology
UR - http://www.scopus.com/inward/record.url?scp=85099467528&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85099467528&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85099467528
T3 - Proceedings of the 41st Annual Meeting of the Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019
SP - 1318
EP - 1323
BT - Proceedings of the 41st Annual Meeting of the Cognitive Science Society
PB - The Cognitive Science Society
Y2 - 24 July 2019 through 27 July 2019
ER -