A counterfactual simulation model of causal judgments for physical events.
Name
Gerstenberg et al. - A counterfactual simulation model of causal judgment.pdf
Description
Submitted version
Size
9.15 MB
Format
Adobe PDF
Checksum (MD5)
2edd4103a4983f1d6b0eb7ecbec9de67
Author(s)
Gerstenberg, Tobias
Goodman, Noah D
Lagnado, David A
Tenenbaum, Joshua B
Date Issued
2021
Journal
Psychological Review
Publisher
American Psychological Association (APA)
Citation
Gerstenberg, Tobias, Goodman, Noah D, Lagnado, David A and Tenenbaum, Joshua B. 2021. "A counterfactual simulation model of causal judgments for physical events.." Psychological Review, 128 (5).
Version
Original manuscript
Abstract
How do people make causal judgments about physical events? We introduce the counterfactual simulation model (CSM) which predicts causal judgments in physical settings by comparing what actually happened with what would have happened in relevant counterfactual situations. The CSM postulates different aspects of causation that capture the extent to which a cause made a difference to whether and how the outcome occurred, and whether the cause was sufficient and robust. We test the CSM in several experiments in which participants make causal judgments about dynamic collision events. A preliminary study establishes a very close quantitative mapping between causal and counterfactual judgments. Experiment 1 demonstrates that counterfactuals are necessary for explaining causal judgments. Participants' judgments differed dramatically between pairs of situations in which what actually happened was identical, but where what would have happened differed. Experiment 2 features multiple candidate causes and shows that participants' judgments are sensitive to different aspects of causation. The CSM provides a better fit to participants' judgments than a heuristic model which uses features based on what actually happened. We discuss how the CSM can be used to model the semantics of different causal verbs, how it captures related concepts such as physical support, and how its predictions extend beyond the physical domain. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Center for Brains, Minds, and Machines
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1037/REV0000281
