Moral dynamics: Grounding moral judgment in intuitive physics and intuitive psychology

Counterfactual simulation in causal cognition

How do people make causal judgments and assign responsibility? In this review article, I argue that counterfactual simulations are key. To simulate counterfactuals, we need three ingredients: a generative mental model of the world, the ability to perform interventions on that model, and the capacity to simulate the consequences of these interventions. The counterfactual simulation model (CSM) uses these ingredients to capture people's intuitive understanding of the physical and social world. In the physical domain, the CSM predicts people's causal judgments about dynamic collision events, complex situations that involve multiple causes, omissions as causes, and causes that sustain physical stability. In the social domain, the CSM predicts responsibility judgments in helping and hindering scenarios.

Revisiting causal pluralism: Intention, process, and dependency in cases of double prevention

Causal pluralism proposes that humans can reason about causes and effects in terms of both dependency and process relations, depending on the scenario. Support for this view is provided by responses to double prevention scenarios in which an affector attempts to bring about an outcome, a preventer attempts to prevent the outcome, and a double preventer intervenes to stop the preventer's prevention attempt. Previous research indicates that reasoners award the affector high causal ratings regardless of whether their action was executed intentionally, whereas reasoners only award the double preventer high causal ratings when the double preventer acts intentionally. These results were interpreted as evidence that intentional actions prompt reasoners to differentially weight separately maintained representations of dependency and process relations. The current study presents three challenges to this causal pluralism account by (1) questioning whether intentionality only affects causal ratings by increasing the weighting placed on dependency relations during reasoning (Experiments 1-2), (2) presenting evidence consistent with the possibility that reasoners interpret the double preventer's action in terms of a process relation (Experiments 1-2), and (3) demonstrating that an unintentional double preventer can receive significantly higher causal ratings than an intentional affector when the order in which the characters act is altered (Experiments 3-4). These results underscore the need to revisit fundamental questions regarding how reasoners form, maintain, and reason over representations of causal scenarios featuring intentional actions.

Computational Social Psychology

Social psychologists attempt to explain how we interact by appealing to basic principles of how we think. To make good on this ambition, they are increasingly relying on an interconnected set of formal tools that model inference, attribution, value-guided decision making, and multi-agent interactions. By reviewing progress in each of these areas and highlighting the connections between them, we can better appreciate the structure of social thought and behavior, while also coming to understand when, why, and how formal tools can be useful for social psychologists.

Actual and counterfactual effort contribute to responsibility attributions in collaborative tasks

How do people judge responsibility in collaborative tasks? Past work has proposed a number of metrics that people may use to attribute blame and credit to others, such as effort, competence, and force. Some theories consider only the actual effort or force (individuals are more responsible if they put forth more effort or force), whereas others consider counterfactuals (individuals are more responsible if some alternative behavior on their or their collaborator's part could have altered the outcome). Across four experiments (N=717), we found that participants' judgments are best described by a model that considers both actual and counterfactual effort. This finding generalized to an independent validation data set (N=99). Our results thus support a dual-factor theory of responsibility attribution in collaborative tasks.

If not me, then who? Responsibility and replacement

How do people hold others responsible? Responsibility judgments are affected not only by what actually happened, but also by what could have happened if things had turned out differently. Here, we look at how replaceability - the ease with which a person could have been replaced by someone else - affects responsibility. We develop the counterfactual replacement model, which runs simulations of alternative scenarios to determine the probability that the outcome would have differed if the person of interest had been replaced. The model predicts that a person is held more responsible, the more difficult it would have been to replace them. To test the model's predictions, we design a paradigm that quantitatively varies replaceability by manipulating the number of replacements and the probability with which each replacement would have been available. Across three experiments featuring increasingly complex scenarios, we show that the model explains participants' responsibility judgments well in both social and physical settings, and better than alternative models that rely only on features of what actually happened.

What would have happened? Counterfactuals, hypotheticals and causal judgements

How do people make causal judgements? In this paper, I show that counterfactual simulations are necessary for explaining causal judgements about events, and that hypotheticals do not suffice. In two experiments, participants viewed video clips of dynamic interactions between billiard balls. In Experiment 1, participants either made hypothetical judgements about whether ball B would go through the gate if ball A were not present in the scene, or counterfactual judgements about whether ball B would have gone through the gate if ball A had not been present. Because the clips featured a block in front of the gate that sometimes moved and sometimes stayed put, hypothetical and counterfactual judgements came apart. A computational model that evaluates hypotheticals and counterfactuals by running noisy physical simulations accurately captured participants' judgements. In Experiment 2, participants judged whether ball A caused ball B to go through the gate. The results showed a tight fit between counterfactual and causal judgements, whereas hypotheticals did not predict causal judgements. I discuss the implications of this work for theories of causality, and for studying the development of counterfactual thinking in children. This article is part of the theme issue 'Thinking about possibilities: mechanisms, ontogeny, functions and phylogeny'.

Planning with Theory of Mind

Understanding Theory of Mind should begin with an analysis of the problems it solves. The traditional answer is that Theory of Mind is used for predicting others' thoughts and actions. However, the same Theory of Mind is also used for planning to change others' thoughts and actions. Planning requires that Theory of Mind consists of abstract structured causal representations and supports efficient search and selection from innumerable possible actions. Theory of Mind contrasts with less cognitively demanding alternatives: statistical predictive models of other people's actions, or model-free reinforcement of actions by their effects on other people. Theory of Mind is likely used to plan novel interventions and predict their effects, for example, in pedagogy, emotion regulation, and impression management.