Causal relations drive young children's induction, naming, and categorization

A number of recent models and experiments have suggested that evidence of early category-based induction is an artifact of perceptual cues provided by experimenters. We tested these accounts against the prediction that different relations (causal versus non-causal) determine the types of perceptual similarity by which children generalize. Young children were asked to label, to infer novel properties, and to project future appearances of a novel animal that varied in two opposite respects: (1) how much it looked like another animal whose name and properties were known, and (2) how much its parents looked like parents of another animal whose name and properties were known. When exemplar origins were known, children generalized to exemplars with similar origins rather than with similar appearances; when origins were unknown, children generalized to exemplars with similar appearances. Results indicate even young children possess the cognitive control to choose the similarities that best predict accurate generalizations.     

Diagnostic causal reasoning with verbal information

In diagnostic causal reasoning, the goal is to infer the probability of causes from one or multiple observed effects. Typically, studies investigating such tasks provide subjects with precise quantitative information regarding the strength of the relations between causes and effects or sample data from which the relevant quantities can be learned. By contrast, we sought to examine people’s inferences when causal information is communicated through qualitative, rather vague verbal expressions (e.g., “X occasionally causes A”). We conducted three experiments using a sequential diagnostic inference task, where multiple pieces of evidence were obtained one after the other. Quantitative predictions of different probabilistic models were derived using the numerical equivalents of the verbal terms, taken from an unrelated study with different subjects. We present a novel Bayesian model that allows for incorporating the temporal weighting of information in sequential diagnostic reasoning, which can be used to model both primacy and recency effects. On the basis of 19,848 judgments from 292 subjects, we found a remarkably close correspondence between the diagnostic inferences made by subjects who received only verbal information and those of a matched control group to whom information was presented numerically. Whether information was conveyed through verbal terms or numerical estimates, diagnostic judgments closely resembled the posterior probabilities entailed by the causes’ prior probabilities and the effects’ likelihoods. We observed interindividual differences regarding the temporal weighting of evidence in sequential diagnostic reasoning. Our work provides pathways for investigating judgment and decision making with verbal information within a computational modeling framework.     

A rating measure of ADHD-related neuropsychological impairment in children and adolescents: Data from the Cognition and Motivation in Everyday Life (CAMEL) Scale from population and clinical samples

A new parent-completed questionnaire, the Cognition and Motivation in Everyday Life (CAMEL) scale, was developed to provide a comprehensive assessment of neuropsychological impairment in children related to attention-deficit/hyperactivity disorder (ADHD) across diverse cognitive, motivational and energetic domains. Its psychometric properties were investigated. A total of 60 items were generated to cover a wide range of putative ADHD-related neuropsychological processes. A clinical (n = 142) and community (n = 810) sample of parents with children between 6 and 16 years of age completed the questionnaire. Data on ADHD symptoms were also collected with a commonly-used, validated parent rating scale to explore the associations between CAMEL scores and dimensional measures of child ADHD and conduct problems. Factor analysis identified six factors which we labeled (i) Cognition, (ii) Self-Direction and Organization, (iii) Effort Engagement, (iv) Arousal Regulation, (v) Motivational Responsiveness, and (vi) Cautiousness. Self-Direction and Organization and Arousal Regulation were the strongest predictors for ADHD symptomatology. Self-Direction and Organization was strongly associated with inattention and Arousal Regulation with hyperactivity-impulsivity symptoms. Parents distinguished between broad neuropsychological domains in reliable and plausible ways, making distinctions between key aspects of functioning. However, the boundaries between these domains did not map directly onto the distinctions drawn within traditional models of ADHD deficits. Further research is required to examine the predictive validity and cost-effectiveness of the CAMEL scale compared to direct objective testing using laboratory measures in predicting prognosis and treatment outcome.     

Must God Be Dead? Reinventing the Sacred

Newton became a death knell for a theistic God and for a non-epiphenominal mind. The culprit is the deterministic causal closure of classical physics. Two major revolutions are taking us beyond this closure. The first is Quantum Mechanics, which is non-deterministic. The second revolution may just be happening. The emergence and evolution of life in our or any biosphere is governed by no law at all. This freedom suggests one sense of God as the natural creativity of the Universe.     

Nomic Universals and Particular Causal Relations: Which are Basic and Which are Derived?

Armstrong holds that a law of nature is a certain sort of structural universal which, in turn, fixes causal relations between particular states of affairs. His claim that these nomic structural universals explain causal relations commits him to saying that such universals are irreducible, not supervenient upon the particular causal relations they fix. However, Armstrong also wants to avoid Plato’s view that a universal can exist without being instantiated, a view which he regards as incompatible with naturalism. This construal of naturalism forces Armstrong to say that universals are abstractions from a certain class of particulars; they are abstractions from first-order states of affairs, to be more precise. It is here argued that these two tendencies in Armstrong cannot be reconciled: To say that universals are abstractions from first-order states of affairs is not compatible with saying that universals fix causal relations between particulars. Causal relations are themselves states of affairs of a sort, and Armstrong’s claim that a law is a kind of structural universal is best understood as the view that any given law logically supervenes on its corresponding causal relations. The result is an inconsistency, Armstrong having to say that laws do not supervene on particular causal relations while also being committed to the view that they do so supervene. The inconsistency is perhaps best resolved by denying that universals are abstractions from states of affairs.

Structure and strength in causal induction

We present a framework for the rational analysis of elemental causal induction-learning about the existence of a relationship between a single cause and effect-based upon causal graphical models. This framework makes precise the distinction between causal structure and causal strength: the difference between asking whether a causal relationship exists and asking how strong that causal relationship might be. We show that two leading rational models of elemental causal induction, DeltaP and causal power, both estimate causal strength, and we introduce a new rational model, causal support, that assesses causal structure. Causal support predicts several key phenomena of causal induction that cannot be accounted for by other rational models, which we explore through a series of experiments. These phenomena include the complex interaction between DeltaP and the base-rate probability of the effect in the absence of the cause, sample size effects, inferences from incomplete contingency tables, and causal learning from rates. Causal support also provides a better account of a number of existing datasets than either DeltaP or causal power.     

Kant's Universal Law Formula Revisited

Kantians are increasingly deserting the universal law formula in favor of the humanity formula. The former, they argue, is open to various decisive objections; the two are not equivalent; and it is only by appealing to the humanity formula that Kant can reliably generate substantive implications from his theory of an acceptable sort. These assessments of the universal law formula, which clash starkly with Kant's own assessment of it, are based on various widely accepted interpretative assumptions. These assumptions, it is argued in this article, depend on misleading translations of key terms; selective attention to Kant's concrete examples; not taking seriously Kant's theoretical claims about the relations among his various ideas; and a failure to take into account Kant's idiosyncratic definitions of key concepts. The article seeks to right these interpretative wrongs, and finds that the universal law formula is not open to many of the standard objections.     

Effects of Causal Structure on Decisions About Where to Intervene on Causal Systems

We investigated how people design interventions to affect the outcomes of causal systems. We propose that the abstract structural properties of a causal system, in addition to people's content and mechanism knowledge, influence decisions about how to intervene. In Experiment 1, participants preferred to intervene at specific locations (immediate causes, root causes) in a causal chain regardless of which content variables occupied those positions. In Experiment 2, participants were more likely to intervene on root causes versus immediate causes when they were presented with a long‐term goal versus a short‐term goal. These results show that the structural properties of a causal system can guide the design of interventions.     

Manipulating Morality: Third‐Party Intentions Alter Moral Judgments by Changing Causal Reasoning

The present studies investigate how the intentions of third parties influence judgments of moral responsibility for other agents who commit immoral acts. Using cases in which an agent acts under some situational constraint brought about by a third party, we ask whether the agent is blamed less for the immoral act when the third party intended for that act to occur. Study 1 demonstrates that third‐party intentions do influence judgments of blame. Study 2 finds that third‐party intentions only influence moral judgments when the agent's actions precisely match the third party's intention. Study 3 shows that this effect arises from changes in participants' causal perception that the third party was controlling the agent. Studies 4 and 5, respectively, show that the effect cannot be explained by changes in the distribution of blame or perceived differences in situational constraint faced by the agent.