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Abstract When making inferences about the mental lives of others (e.g., others’ preferences), it is critical to consider the extent to which the choices we observe are constrained. Prior research on the development of this tendency indicates a contradictory pattern: Children show remarkable sensitivity to constraints in traditional experimental paradigms, yet often fail to consider real‐world constraints and privilege inherent causes instead. We propose that one explanation for this discrepancy may be that real‐world constraints are often stable over time and lose their salience. The present research tested whether children (N = 133, 5‐ to 12‐year‐old mostly US children; 55% female, 45% male) becomelesssensitive to an actor's constraints after first observing two constrained actors (Stable condition) versus after first observing two actors in contexts with greater choice (Not Stable condition). We crossed thestabilityof the constraint with thetypeof constraint: either the constraint was deterministic such that there was only one option available (No Other Option constraint) or, in line with many real‐world constraints, the constraint was probabilistic such that therewasanother option, but it was difficult to access (Hard to Access constraint). Results indicated that children in the Stable condition became less sensitive to the probabilistic Hard to Access constraint across trials. Notably, we also found that children's sensitivity to constraints was enhanced in the Not Stable condition regardless of whether the constraint was probabilistic or deterministic. We discuss implications for children's sensitivity to real‐world constraints. Research HighlightsThis research addresses the apparent contradiction that children are sensitive to constraints in experimental paradigms but are ofteninsensitiveto constraints in the real world.One explanation for this discrepancy is that constraints in the real world tend to be stable over time and may lose their salience.When probabilistic constraints (i.e., when a second option is available but hard to access) are stable, children become de‐sensitized to constraints across trials.First observing contexts with greater choice increases children's sensitivity to both probabilistic and deterministic constraints. 

Abstract How do people come to opposite causal judgments about societal problems, such as whether a public health policy reduced COVID‐19 cases? The current research tests an understudied cognitive mechanism in which people may agree about whatactuallyhappened (e.g., that a public health policy was implemented and COVID‐19 cases declined), but can be made to disagree about the counterfactual, or whatwould havehappened otherwise (e.g., whether COVID‐19 cases would have declined naturally without intervention) via comparison cases. Across two preregistered studies (totalN= 480), participants reasoned about the implementation of a public policy that was followed by an immediate decline in novel virus cases. Study 1 shows that people's judgments about the causal impact of the policy could be pushed in opposite directions by emphasizing comparison cases that imply different counterfactual outcomes. Study 2 finds that people recognize they can use such information to influence others. Specifically, in service of persuading others to support or reject a public health policy, people systematically showed comparison cases implying the counterfactual outcome that aligned with their position. These findings were robust across samples of U.S. college students and politically and socioeconomically diverse U.S. adults. Together, these studies suggest that implied counterfactuals are a powerful tool that individuals can use to manufacture others’ causal judgments and warrant further investigation as a mechanism contributing to belief polarization. 

The release of ChatGPT in late 2022 prompted widespread concern about the implications of artificial intelligence for academic integrity, but thus far there has been little direct empirical evidence to inform this debate. Participants (69 high school teachers, 140 high school students, total N = 209) took an AI Identification Test in which they read pairs of essays—one written by a high school student and the other by ChatGPT—and guessed which was generated by the chatbot. Accuracy was only 70% for teachers, and it was slightly worse for students (62%). Self-reported confidence did not predict accuracy, nor did experience with ChatGPT or subject-matter expertise. Well-written student essays were especially hard to differentiate from the ChatGPT texts. In another set of measures, students reported greater optimism than their teachers did about the future role of ChatGPT in education. Students expressed disapproval of submitting ChatGPT-generated essays as one’s own but rated this and other possible academic integrity violations involving ChatGPT less negatively than teachers did. These results form an empirical basis for further work on the relationship between AI and academic integrity.