Mental causation in a physical world

It is generally accepted that the most serious threat to the possibility of mental causation is posed by the causal self-sufficiency of physical causal processes. I argue, however, that this feature of the world, which I articulate in principle I call Completeness, in fact poses no genuine threat to mental causation. Some find Completeness threatening to mental causation because they confuse it with a stronger principle, which I call Closure. Others do not simply conflate Completeness and Closure, but hold that Completeness, together with certain plausible assumptions, entails Closure. I refute the most fully worked-out version of such an argument. Finally, some find Completeness all by itself threatening to mental causation. I argue that one will only find Completeness threatening if one operates with a philosophically distorted conception of mental causation. I thereby defend what I call naïve realism about mental causation.     

Correlation,partial correlation,and causation

Philosophers and scientists have maintained that causation, correlation, and “partial correlation” are essentially related. These views give rise to various rules of causal inference. This essay considers the claims of several philosophers and social scientists for causal systems with dichotomous variables. In section 2 important commonalities and differences are explicated among four major conceptions of correlation. In section 3 it is argued that whether correlation can serve as a measure of A's causal influence on B depends upon the conception of causation being used and upon certain background assumptions. In section 4 five major kinds of “partial correlation” are explicated, and some of the important relations are established among two conceptions of “partial correlation”, the conception of “screening off”, the conception of “partitioning”, and the measures of causal influence which have been suggested by advocates of path analysis or structural equation methods. In section 5 it is argued that whether any of these five conceptions of “partial correlation” can serve as a measure of causal influence depends upon the conception of causation being used and upon certain background assumptions. The important conclusion is that each of the approaches (considered here) to causal inference for causal systems with dichotomous variables stands in need of important qualifications and revisions if they are to be justified.     

Proceeding From Observed Correlation to Causal Inference: The Use of Natural Experiments

ABSTRACT— This article notes five reasons why a correlation between a risk (or protective) factor and some specified outcome might not reflect environmental causation. In keeping with numerous other writers, it is noted that a causal effect is usually composed of a constellation of components acting in concert. The study of causation, therefore, will necessarily be informative on only one or more subsets of such components. There is no such thing as a single basic necessary and sufficient cause. Attention is drawn to the need (albeit unobservable) to consider the counterfactual (i.e., what would have happened if the individual had not had the supposed risk experience). Fifteen possible types of natural experiments that may be used to test causal inferences with respect to naturally occurring prior causes (rather than planned interventions) are described. These comprise five types of genetically sensitive designs intended to control for possible genetic mediation (as well as dealing with other issues), six uses of twin or adoptee strategies to deal with other issues such as selection bias or the contrasts between different environmental risks, two designs to deal with selection bias, regression discontinuity designs to take into account unmeasured confounders, and the study of contextual effects. It is concluded that, taken in conjunction, natural experiments can be very helpful in both strengthening and weakening causal inferences.     

Causal Inference from Noise

Correlation is not causation is one of the mantras of the sciences—a cautionary warning especially to fields like epidemiology and pharmacology where the seduction of compelling correlations naturally leads to causal hypotheses. The standard view from the epistemology of causation is that to tell whether one correlated variable is causing the other, one needs to intervene on the system—the best sort of intervention being a trial that is both randomized and controlled. In this paper, we argue that some purely correlational data contains information that allows us to draw causal inferences: statistical noise. Methods for extracting causal knowledge from noise provide us with an alternative to randomized controlled trials that allows us to reach causal conclusions from purely correlational data.     

Learning the form of causal relationships using hierarchical bayesian models

People learn quickly when reasoning about causal relationships, making inferences from limited data and avoiding spurious inferences. Efficient learning depends on abstract knowledge, which is often domain or context specific, and much of it must be learned. While such knowledge effects are well documented, little is known about exactly how we acquire knowledge that constrains learning. This work focuses on knowledge of the functional form of causal relationships; there are many kinds of relationships that can apply between causes and their effects, and knowledge of the form such a relationship takes is important in order to quickly identify the real causes of an observed effect. We developed a hierarchical Bayesian model of the acquisition of knowledge of the functional form of causal relationships and tested it in five experimental studies, considering disjunctive and conjunctive relationships, failure rates, and cross-domain effects. The Bayesian model accurately predicted human judgments and outperformed several alternative models.     

Disagreement and causal learning: Others' hypotheses affect children's evaluations of evidence

When children evaluate evidence and make causal inferences, they are sensitive to the social context in which data are generated. This study investigated whether children learn more from evidence generated by an agent who agrees with them or from one who disagrees with them. Children in two age groups (5- and 6-year-olds and 9- and 10-year-olds) observed the functioning of a machine that lit up and played music in the presence of certain objects. After endorsing one of two plausible causal hypotheses, children observed a puppet either agree or disagree with their own hypotheses. The puppet then generated a further piece of evidence that confirmed, disconfirmed, or was neutral with respect to the children's hypotheses. When they were later asked to make causal inferences about objects they did not directly observe, children in both age groups responded differentially to identical evidence depending on whether the agent agreed or disagreed, and they often drew stronger inferences in response to disagreement. In addition, older children were particularly sensitive to disagreement when the evidence was ambiguous. Our results suggest that children consider the relationship between their own and others' hypotheses when evaluating evidence that others generate. (PsycINFO Database Record (c) 2012 APA, all rights reserved).     

Testing causal models of the relationship between childhood gender atypical behaviour and parent–child relationship

An association between childhood gender atypical behaviour (GAB) and a negative parent–child relationship has been demonstrated in several studies, yet the causal relationship of this association is not fully understood. In the present study, different models of causation between childhood GAB and parent–child relationships were tested. Direction of causation modelling was applied to twin data from a population‐based sample (n= 2,565) of Finnish 33‐ to 43‐year‐old twins. Participants completed retrospective self‐report questionnaires. Five different models of causation were then fitted to the data: GAB → parent–child relationship, parent–child relationship → GAB, reciprocal causation, a bivariate genetic model, and a model assuming no correlation. It was found that a model in which GAB and quality of mother–child, and father–child relationship reciprocally affect each other best fitted the data. The findings are discussed in light of how we should understand, including causality, the association between GAB and parent–child relationship.     

The mind's best trick: how we experience conscious will

We often consciously will our own actions. This experience is so profound that it tempts us to believe that our actions are caused by consciousness. It could also be a trick, however - the mind's way of estimating its own apparent authorship by drawing causal inferences about relationships between thoughts and actions. Cognitive, social, and neuropsychological studies of apparent mental causation suggest that experiences of conscious will frequently depart from actual causal processes and so might not reflect direct perceptions of conscious thought causing action.     

基于语境的预期推理构建的实验研究

两个实验用移动视窗技术,用目标句阅读时间和命名探测词两种指标来考察当文本中有两个情境模型时,读者是否会即时进行预期推理。结果表明,如果在支持性语境与引发推理的信息之间插入一个与支持性语境所描述的情境模型不同的,且与主人公目标有关的因果链上的情境模型的信息,则不管这个插入的情境模型在文本中的位置如何,也不管干扰信息的干扰水平高低,都会降低因果性预期推理在阅读中产生的可能性。     

Analogical and category-based inference: a theoretical integration with Bayesian causal models

A fundamental issue for theories of human induction is to specify constraints on potential inferences. For inferences based on shared category membership, an analogy, and/or a relational schema, it appears that the basic goal of induction is to make accurate and goal-relevant inferences that are sensitive to uncertainty. People can use source information at various levels of abstraction (including both specific instances and more general categories), coupled with prior causal knowledge, to build a causal model for a target situation, which in turn constrains inferences about the target. We propose a computational theory in the framework of Bayesian inference and test its predictions (parameter-free for the cases we consider) in a series of experiments in which people were asked to assess the probabilities of various causal predictions and attributions about a target on the basis of source knowledge about generative and preventive causes. The theory proved successful in accounting for systematic patterns of judgments about interrelated types of causal inferences, including evidence that analogical inferences are partially dissociable from overall mapping quality.     

Assessing interactive causal influence

The discovery of conjunctive causes--factors that act in concert to produce or prevent an effect--has been explained by purely covariational theories. Such theories assume that concomitant variations in observable events directly license causal inferences, without postulating the existence of unobservable causal relations. This article discusses problems with these theories, proposes a causal-power theory that overcomes the problems, and reports empirical evidence favoring the new theory. Unlike earlier models, the new theory derives (a). the conditions under which covariation implies conjunctive causation and (b). functions relating observable events to unobservable conjunctive causal strength. This psychological theory, which concerns simple cases involving 2 binary candidate causes and a binary effect, raises questions about normative statistics for testing causal hypotheses regarding categorical data resulting from discrete variables.     

The role of preschoolers' social understanding in evaluating the informativeness of causal interventions

Preschoolers use information from interventions, namely intentional actions, to make causal inferences. We asked whether children consider some interventions to be more informative than others based on two components of an actor’s knowledge state: whether an actor possesses causal knowledge, and whether an actor is allowed to use their knowledge in a given situation. Three- and four-year-olds saw a novel toy that activated in the presence of certain objects. Two actors, one knowledgeable about the toy and one ignorant, each tried to activate the toy with an object. In Experiment 1, either the actors chose objects or the child chose for them. In Experiment 2, the actors chose objects blindfolded. Objects were always placed on the toy simultaneously, and thus were equally associated with the effect. Preschoolers’ causal inferences favored the knowledgeable actor’s object only when he was allowed to choose it (Experiment 1). Thus, children consider both personal and situational constraints on knowledge when evaluating the informativeness of causal interventions.     

What Invariance Is and How to Test for It

Causal assessment is the problem of establishing whether a relation between (variable) X and (variable) Y is causal. This problem, to be sure, is widespread across the sciences. According to accredited positions in the philosophy of causality and in social science methodology, invariance under intervention provides the most reliable test to decide whether X causes Y. This account of invariance (under intervention) has been criticised, among other reasons, because it makes manipulations on the putative causal factor fundamental for the causal methodology; consequently, the argument goes, the account is ill-suited to those contexts where manipulations are not performed, for instance, the social sciences. The article aims to extend the account of invariance (under intervention), in a way that manipulations on the putative causal factors are not methodologically fundamental, and yet invariance remains key for causal assessment both in experimental and non-experimental contexts.     

Causal inference in lead research: introduction to the special section on the neurobehavioral effects of environmental lead.

This Special Section on the Neurobehavioral Effects of Environmental Lead includes empirical reports from three major longitudinal prospective studies and a review of animal research modeling effects of lead on human cognition. This Introductory article addresses a fundamental controversy in lead and other epidemiologic research. It is argued that, contrary to conventional wisdom, valid causal inferences can be made from correlational research. Justification for this is to be found in contemporary frameworks drawn from the field of the philosophy of science; an essential, but often neglected perspective for such metatheoretical issues as causal inference. It is concluded that the nonexperimental methods described in the articles comprising this Special Section can, indeed, contribute to causal formulations of lead effects on human development, and that such inferences are not exclusive to experimental evidence.     

Causal discovery and the problem of psychological interventions

Finding causes is a central goal in psychological research. In this paper, I argue based on the interventionist approach to causal discovery that the search for psychological causes faces great obstacles. Psychological interventions are likely to be fat-handed: they change several variables simultaneously, and it is not known to what extent such interventions give leverage for causal inference. Moreover, due to problems of measurement, the degree to which an intervention was fat-handed, or more generally, what the intervention in fact did, is difficult to reliably estimate. A further complication is that the causal findings in psychology are typically made at the population level, and such findings do not allow inferences to individual-level causal relationships. I also discuss the implications of these problems for research, as well as various ways of addressing them, such as focusing more on the discovery of robust but non-causal patterns.     

Action tendencies and characteristics of environmental risks

It is assumed that the mental representation of the causal structure of environmental risks, i.e., the type of cause and the type of potential consequence, determines which sort of action tendencies are formed. We propose a model of risk evaluation that includes consequentialist and deontological judgments as well as specific emotions as mediators of action tendencies. Four hundred participants took part in an experiment which presented scenario information about environmental risks. The scenarios differed with respect to (a) causation (human vs. natural cause; single vs. aggregate causation), (b) consequence (harm to self vs. harm to other people vs. harm to nature), and (c) geographical distance (proximate vs. distant). Participants indicated how much they preferred each of 31 prospective behaviors. Factor analyses yielded five types of action tendencies: help, aggression, escape, political action, and self-focus. The causal structure of the risks was systematically related to action tendencies, e.g., environmental risks that are caused by humans, and in particular those caused by a single human agent, elicit aggressive action tendencies. The findings conform that the perceived causal structure of a specific risk determines whether the focus is upon consequentialist or deontological judgments, which, in turn, elicit specific types of action tendency, mediated by emotions.