Trumping the causal influence account of causation

Here is a simple counterexample to David Lewis’s causal influence account of causation, one that is especially illuminating due to its connection to what Lewis himself writes: it is a variant of his trumping example

Agency,Consciousness, and Executive Control

On the Causal Theory of Action (CTA), internal proper parts of an agent such as desires and intentions are causally responsible for actions. CTA has increasingly come under attack for its alleged failure to account for agency. A recent version of this criticism due to François Schroeter proposes that CTA cannot provide an adequate account of either the executive control or the autonomous control involved in full-fledged agency. Schroeter offers as an alternative a revised understanding of the proper role of consciousness in agency. In this paper we criticize Schroeter’s analysis of the type of consciousness involved in executive control and examine the way in which the conscious self allegedly intervenes in action. We argue that Schroeter’s proposal should not be preferred over recent versions of CTA.     

Causal-based property generalization

A central question in cognitive research concerns how new properties are generalized to categories. This article introduces a model of how generalizations involve a process of causal inference in which people estimate the likely presence of the new property in individual category exemplars and then the prevalence of the property among all category members. Evidence in favor of this causal-based generalization (CBG) view included effects of an existing feature's base rate (Experiment 1), the direction of the causal relations (Experiments 2 and 4), the number of those relations (Experiment 3), and the distribution of features among category members (Experiments 4 and 5). The results provided no support for an alternative view that generalizations are promoted by the centrality of the to-be-generalized feature. However, there was evidence that a minority of participants based their judgments on simpler associative reasoning processes.     

Bridging the gap: Causality-at-a-distance in children's categorization and inferences about internal properties

Previous research has shown that preschoolers extend labels and internal properties of objects based on those objects’ causal properties, even when the causal properties conflict with the objects’ perceptual appearance [Nazzi, T., & Gopnik, A. (2000). A shift in children's use of perceptual and causal cues to categorization. Developmental Science, 3, 389–396; Sobel, D. M., Yoachim, C. M., Gopnik, A., Meltzoff, A. N., & Blumenthal, E. J. (2007). The blicket within: Preschoolers’ inferences about insides and causes. Journal of Cognition and Development, 8, 159–182]. These studies, however, only presented causal relations that acted on contact. In two studies, contact causality was replaced by distance causality. In contrast to the contact causality case, 4- and 5-year-olds extended labels to objects with similar perceptual properties over objects with similar causal properties when those properties acted at a distance. When children were asked to make inferences about object's internal properties, they were more likely to make causal responses, with 5-year-olds doing so to a greater extent than 4-year-olds. In a second study, 4-year-olds registered causal properties that acted at a distance and used them to make inferences when no perceptual conflict was present. These results support a hypothesis that young children develop an understanding of the specific mechanisms that link causal relations.     

The role of feature type and causal status in 4–5-year-old children's biological categorizations

The present study investigated the role of the causal status of features and feature type in biological categorizations by young children. Study 1 showed that 5-year-olds are more strongly influenced by causal features than effect features; 4-year-olds exhibit no such tendency. There therefore appears to be a conceptual change between the ages of 4 and 5 in the evaluation of the causal relations between features that characterize biological categories. The aim of Study 2 was to identify the nature of the abstract beliefs that underlie children's categorial choices. Results show that 5-year-olds base category choices on causal features only when the status of the cause is associated with an internal feature and not if the feature is merely a surface feature. Children thus use biological knowledge to perform the task.     

Covariation and quantifier polarity: what determines causal attribution in vignettes?

Tests of causal attribution often use verbal vignettes, with covariation information provided through statements quantified with natural language expressions. The effect of covariation information has typically been taken to show that set size information affects attribution. However, recent research shows that quantifiers provide information about discourse focus as well as covariation information. In the attribution literature, quantifiers are used to depict covariation, but they confound quantity and focus. In four experiments, we show that focus explains all (Experiment 1) or some (Experiment 2-4) of the impact of covariation information on the attributions made, confirming the importance of the confound. Attribution experiments using vignettes that present covariation information with natural language quantifiers may overestimate the impact of set size information, and ignore the impact of quantifier-induced focus.     

Do We “do”?

A normative framework for modeling causal and counterfactual reasoning has been proposed by Spirtes, Glymour, and Scheines (1993; cf. Pearl, 2000). The framework takes as fundamental that reasoning from observation and intervention differ. Intervention includes actual manipulation as well as counterfactual manipulation of a model via thought. To represent intervention, Pearl employed the do operator that simplifies the structure of a causal model by disconnecting an intervened-on variable from its normal causes. Construing the do operator as a psychological function affords predictions about how people reason when asked counterfactual questions about causal relations that we refer to as undoing, a family of effects that derive from the claim that intervened-on variables become independent of their normal causes. Six studies support the prediction for causal (A causes B) arguments but not consistently for parallel conditional (if A then B) ones. Two of the studies show that effects are treated as diagnostic when their values are observed but nondiagnostic when they are intervened on. These results cannot be explained by theories that do not distinguish interventions from other sorts of events.     

The perception of causality in chimpanzees (Pan spp.)

Chimpanzees (Pan spp.) were tested on a habituation/dishabituation paradigm that was originally developed to test for comprehension of causality in very young human infants. Three versions of the test were used: a food item being moved by a hand, a human pushing another human off a chair to obtain a food item, and a film clip of natural chimpanzee behaviour (capturing and eating a monkey). Chimpanzees exhibited similar results to those obtained with human infants, with significantly elevated levels of looking on the dishabituation trials. Since the level of response was significantly greater on natural/unnatural sequences than on unnatural/natural sequences, we conclude that the chimpanzees were not responding just to novelty but rather to events that infringed their sense of natural causation.     

Social Trust: Fairness Matters More Than Homogeneity

This article explores whether homogeneity or fairness better explains generalized interpersonal trust across countries. Although some research suggests that ethnic diversity and income heterogeneity have a negative impact on social trust, I argue that cross‐national variations in social trust are better explained by fairness: fair procedural rules (democracy), fair administration of rules (freedom from corruption), and fair income distribution (relatively equal but also unskewed). This expectation is confirmed by a multilevel analysis of data from the World Values Surveys and European Values Study covering 170,000 individuals in 80 countries.     

Non-bayesian inference: causal structure trumps correlation

The study tests the hypothesis that conditional probability judgments can be influenced by causal links between the target event and the evidence even when the statistical relations among variables are held constant. Three experiments varied the causal structure relating three variables and found that (a) the target event was perceived as more probable when it was linked to evidence by a causal chain than when both variables shared a common cause; (b) predictive chains in which evidence is a cause of the hypothesis gave rise to higher judgments than diagnostic chains in which evidence is an effect of the hypothesis; and (c) direct chains gave rise to higher judgments than indirect chains. A Bayesian learning model was applied to our data but failed to explain them. An explanation-based hypothesis stating that statistical information will affect judgments only to the extent that it changes beliefs about causal structure is consistent with the results.