Mental jenga: A counterfactual simulation model of causal judgments about physical support.

Abstract

From building towers to picking an orange from a stack of fruit, assessing support is critical for successfully interacting with the physical world. But how do people determine whether one object supports another? In this paper, we develop a counterfactual simulation model (CSM) of causal judgments about physical support. The CSM predicts that people judge physical support by mentally simulating what would happen to a scene if the object of interest was removed. Three experiments test the model by asking one group of participants to judge what would happen to a tower if one of the blocks were removed, and another group of participants how responsible that block was for the tower's stability. The CSM accurately captures participants' predictions by running noisy simulations that incorporate different sources of uncertainty. Participants' responsibility judgments are closely related to counterfactual predictions: a block is more responsible when many other blocks would fall if it were removed. By construing physical support as preventing from falling, the CSM provides a unified account of how causal judgments in dynamic and static physical scenes arise from the process of counterfactual simulation.

Reasoning about actual causation in reversible and irreversible causal structures.

“…So far, psychological theories and experiments in that area have addressed causal concepts such as "enable" or "allow" and what distinguishes them from the meaning of "cause" (Beller et al, 2020;Cao et al, 2023;Cheng & Novick, 1991, 1992Sloman et al, 2009;Wolff, 2007;Wolff et al, 2010;Wolff & Song, 2003), but studies that (directly) address how people learn and think about causal maintaining are still largely absent. An exception is a recent paper by Zhou et al (2023), who propose a counterfactual simulation model of physical support. Their model is able to capture reasoners' judgments in scenarios in which the building blocks of a tower (e.g., the wooden blocks of a Jenga tower) are either actually or only hypothetically removed.…”

“…The model by Zhou et al (2023) is a counterfactual dependency model. Another interesting question is how maintaining might be modeled by different classes of models about causal semantics, such as force dynamics models (Talmy, 1988; Wolff, 2007; Wolff & Song, 2003; Wolff & Thorstad, 2017; Wolff et al, 2010) covariation accounts (e.g., Cheng & Novick, 1991, 1992), and causal Bayes nets (e.g., Sloman et al, 2009).…”

“…Such knowledge might also be at play in the mental jenga task ofZhou et al (2023), as participants might be very much aware that a tower that was built can collapse again.13 I would like to thank an anonymous reviewer for pointing this out.…”

Reasoning about Actual Causation in Reversible and Irreversible Causal Structures

“…So far, psychological theories and experiments in that area have addressed causal concepts like "enable" or "allow" and what distinguishes them from the meaning of "cause" (Beller, Bennett, & Gerstenberg, 2020;Cao, Geiger, Kreiss, Icard, & Gerstenberg, 2023;Cheng & Novick, 1991, 1992Sloman, Barbey, & Hotaling, 2009;Wolff, 2007;Wolff, Barbey, & Hausknecht, 2010;Wolff & Song, 2003), but studies that (directly) address how people learn and think about causal maintaining are still largely absent. An exception is a recent paper by Zhou, Smith, Tenenbaum, and Gerstenberg (2023), who propose a counterfactual simulation model of physical support. Their model is able to capture reasoners' judgments in scenarios in which building blocks of a tower (e.g., the wooden blocks of a jenga tower) are either actually or only hypothetically removed.…”

“…The model by Zhou et al (2023) is a counterfactual dependency model. Another interesting question is how maintaining might be modeled by different classes of models about causal semantics, like force dynamics models (Talmy, 1988;Wolff, 2007;Wolff et al, 2010;Wolff & Song, 2003;Wolff & Thorstad, 2017), covariation accounts (e.g., Cheng & Novick, 1991, 1992, and causal Bayes nets (e.g., Sloman et al, 2009).…”