The dynamics model, which is based on L. Talmy’s (1988) theory of force dynamics, characterizes causation as a pattern of forces and a position vector. In contrast to counterfactual and probabilistic models, the dynamics model naturally distinguishes between different cause-related concepts and explains the induction of causal relationships from single observations. Support for the model is provided in experiments in which participants categorized 3-D animations of realistically rendered objects with trajectories that were wholly determined by the force vectors entered into a physics simulator. Experiments 1–3 showed that causal judgments are based on several forces, not just one. Experiment 4 demonstrated that people compute the resultant of forces using a qualitative decision rule. Experiments 5 and 6 showed that a dynamics approach extends to the representation of social causation. Implications for the relationship between causation and time are discussed.

Research suggests that causal judgment is influenced primarily by counterfactual or covariational reasoning. In contrast, the author of this article develops judgment dissociation theory (JDT), which predicts that these types of reasoning differ in function and can lead to divergent judgments. The actuality principle proposes that causal selections focus on antecedents that are sufficient to generate the actual outcome. The substitution principle proposes that ad hoc categorization plays a key role in counterfactual and covariational reasoning such that counterfactual selections focus on antecedents that would have been sufficient to prevent the outcome or something like it and covariational selections focus on antecedents that yield the largest increase in the probability of the outcome or something like it. The findings of 4 experiments support JDT but not the competing counterfactual and covariational accounts. If causation is the cement of the universe, as the philosopher David Hume (1740/1938) put it, then it is fair to say that causal knowledge is the cement that binds together each person’s representational universe. Causal reasoning—the process that generates this glue—confers many functional advantages. In virtually every sphere of human interest, our abilities to learn and categorize

Participants recalled either a negative academic or interpersonal experience, and the relations among counterfactual thinking, negative emotions, and attributions of blame and control were examined. Situational context effects on attribution, counterfactual thinking, and emotion were observed, indicating a greater tendency toward self-focused cognition and emotion in the academic context than in the interpersonal context. Consistent with recent theorising, upward counterfactual thinking was associated with negative emotions of guilt, shame, regret, disappointment, and sadness. However, there was no indication that downward counterfactual thinking regulated emotion as previous literature suggests. Implications for functional and process theories of counterfactual thinking are discussed.

The significance of counterfactual thinking in the causal judgment process has been emphasized for nearly two decades, yet no previous research has directly compared the relative effect of thinking counterfactually versus factually on causal judgment. Three experiments examined this comparison by manipulating the task frame used to focus participants’ thinking about a target event. Prior to making judgments about causality, preventability, blame, and control, participants were directed to think about a target actor either in counterfactual terms (what the actor could have done to change the outcome) or in factual terms (what the actor had done that led to the outcome). In each experiment, the effect of counterfactual thinking did not differ reliably from the effect of factual thinking on causal judgment. Implications for research on causal judgment and mental representation are discussed.

This paper proposes a new model of causal meaning, the Vector Model, which formalizes a model of causation based on Talmys notions of force dynamics (Wolff, Song, & Driscoll, 2002). In the Vector Model, the concepts of CAUSE, ENABLE and PREVENT are distinguished from one another in terms of force vectors, their resultant and the relationship of each force vector to a target vector. The predictions of the model were tested in two experiments in which participants saw realistic 3D-animations of an inflatable boat moving through a pool of water. The boats movements were completely determined by the force vectors entered into a physics simulator. Participants linguistic descriptions of the animations were closely matched by those predicted by the model given the same force vectors as those used to produce the animations. Our model may have implications for the semantics of causal verbs as well as the perception of causal events.

Causation by omission is instantiated when an effect occurs from an absence, as in The absence of nicotine causes withdrawal or Not watering the plant caused it to wilt. The phenomenon has been viewed as an insurmountable problem for process theories of causation, which specify causation in terms of conserved quantities, like force, but not for theories that specify causation in terms of statistical or counterfactual dependencies. A new account of causation challenges these assumptions. According to the force theory, absences are causal when the removal of a force leads to an effect. Evidence in support of this account was found in three experiments in which people classified animations of complex causal chains involving force removal, as well as chains involving virtual forces, that is, forces that were anticipated but never realized. In a fourth experiment, the force theory’s ability to predict synonymy relationships between different types of causal expressions provided further evidence for this theory over dependency theories. The findings show not only how causation by omission can be grounded in the physical world, but also why only certain absences, amongst the potentially infinite number of absences, are causal.

Consider the following scenario: on a stormy night, a tree was struck by lightning, caught fire and burned down. What caused the tree to burn down? Even though both the lightning strike and the presence of oxygen were required and sufficient for combustion, we usually say the lightning strike is “the cause ” of the fire and the presence of oxygen is merely a background

Causation by omission is instantiated when an effect occurs from an absence, as in The absence of nicotine causes withdrawal or Not watering the plant caused it to wilt. The phenomenon has been viewed as an insurmountable problem for process theories of causation, which specify causation in terms of conserved quantities, like force, but not for theories that specify causation in terms of statistical or counterfactual dependencies. A new account of causation challenges these assumptions. According to the force theory, absences are causal when the removal of a force leads to an effect. Evidence in support of this account was found in 3 experiments in which people classified animations of complex causal chains involving force removal, as well as chains involving virtual forces, that is, forces that were anticipated but never realized. In a 4th experiment, the force theory’s ability to predict synonymy relationships between different types of causal expressions provided further evidence for this theory over dependency theories. The findings show not only how causation by omission can be grounded in the physical world but also why only certain absences, among the potentially infinite number of absences, are causal.