Superstitious beliefs and the associative mind

Persistence of superstitions in the modern era could be justified by considering them as a by-product of the brain’s capacity to detect associations and make assumptions about cause-effect relationships. This ability, which supports predictive behaviour, directly relates to associative learning. We tested whether variability in superstitious behaviour reflects individual variability in the efficiency of mechanisms akin to habit learning. Forty-eight individuals performed a Serial Reaction Time Task (SRTT) or an Implicit Cuing Task (ICT). In the SRTT, participants were exposed to a hidden sequence and progressively learnt to optimize responses, a process akin to skill learning. In the ICT participants met with a hidden association, which (if detected) provided a benefit (cf. habit learning). An index of superstitious beliefs was also collected. A correlation emerged between susceptibility to personal superstitions and performance at the ICT only. This novel finding is discussed in view of current ideas on how superstitions are instated.     

Causal inferences with group based trajectory models

A central theme of research on human development and psychopathology is whether a therapeutic intervention or a turning-point event, such as a family break-up, alters the trajectory of the behavior under study. This paper lays out and applies a method for using observational longitudinal data to make more confident causal inferences about the impact of such events on developmental trajectories. The method draws upon two distinct lines of research: work on the use of finite mixture modeling to analyze developmental trajectories and work on propensity scores. The essence of the method is to use the posterior probabilities of trajectory group membership from a finite mixture modeling framework, to create balance on lagged outcomes and other covariates established prior to t for the purpose of inferring the impact of first-time treatment at t on the outcome of interest. The approach is demonstrated with an analysis of the impact of gang membership on violent delinquency based on data from a large longitudinal study conducted in Montreal. The research has been supported by the National Science Foundation (NSF) (SES-99113700) and the National Institute of Mental Health (RO1 MH65611-01A2). It also made heavy use of data collected with the support from Québec’s CQRS and FCAR funding agencies, Canada’s NHRDP and SSHRC funding agencies, and the Molson Foundation. We thank Stephen Fienberg, Susan Murphy, Paul Rosenbaum, the editor, Paul De Boeck, and two anonymous reviewers for their insightful suggestions.     

The psychology and philosophy of luck

There has been a great deal of interest in the concept of luck in the recent psychological and philosophical literature. In philosophy, this interest has tended to focus not upon luck simpliciter but rather upon the role that luck plays in ethical and epistemological debates concerning (respectively) moral and epistemic luck. In psychology, in contrast, a number of studies have explicitly examined our everyday conceptions of luck and the manner in which these conceptions influence our lives. This article surveys both the recent psychological and philosophical literature on this topic and argues that (to different degrees) the work of both disciplines in this area has been hampered by a failure to be clearer about what luck involves. Accordingly, this article offers a specification of what is core to the notion of luck and highlights how this analysis can aid further research in this area by both psychologists and philosophers.     

Explanatory models of illness: a study of within-culture variation

The current studies explore causal models of heart attack and depression generated from American healers whom use distinct explanatory frameworks. Causal chains leading to two illnesses, heart attack and depression, were elicited from participant groups: registered nurses (RNs), energy healers, RN energy healers, and undergraduates. The domain-specificity hypothesis predicted that psycho-social and physical causes would not interact in illness models. Across illnesses, RNs and undergraduates rarely cited interactions between mental and physical causes, consistent with the domain specificity hypothesis. In contrast, energy healers frequently mentioned interactions. Study 2 showed that these differences were not due to salience. These results suggest that domain-specificity theory is supported for groups with extensive exposure to western medicine but does not explain energy models of illness. Implications for other cultural models of illness are discussed.     

Models of causation and the semantics of causal verbs

This research examines the relationship between the concept of CAUSE as it is characterized in psychological models of causation and the meaning of causal verbs, such as the verb cause itself. According to focal set models of causation (; ), the concept of CAUSE should be more similar to the concepts of ENABLE and PREVENT than either is to each other. According to a model based on theory of force dynamics, the force dynamic model, the concepts of CAUSE, ENABLE, and PREVENT should be roughly equally similar to one another. The relationship between these predictions and the meaning of causal verbs was examined by having participants sort causal verbs and rate them with respect to the dimensions specified by the two models. The results from five experiments indicated that the force dynamic model provides a better account of the meaning of causal verbs than do focal set models of causation. Implications for causal inference and induction are discussed.     

Identifying Causal Estimands for Time-Varying Treatments Measured with Time-Varying (Age or Grade-Based) Instruments

This commentary discusses causal estimands of same-age and same-grade comparisons for assessing grade-retention effects on student ability and performance. Using potential outcomes notation, we show that same-age and same-grade comparisons refer to different retention–promotion contrasts and therefore assess different causal questions. We also comment on deleting versus censoring records of students who dropped out of the study or do not belong to the treatment regimes under investigation. Whereas deleting entire student records potentially induces collider bias, censoring circumvents bias if censoring is ignorable given the observed pretreatment covariates.     

A Bayesian Theory of Sequential Causal Learning and Abstract Transfer

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause‐effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre‐training (or even post‐training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue‐outcome co‐occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge.     

The Invisible Hand: Toddlers Connect Probabilistic Events With Agentive Causes

Children posit unobserved causes when events appear to occur spontaneously (e.g., Gelman & Gottfried, 1996). What about when events appear to occur probabilistically? Here toddlers (M = 20.1 months) saw arbitrary causal relationships (Cause A generated Effect A; Cause B generated Effect B) in a fixed, alternating order. The relationships were then changed in one of two ways. In the Deterministic condition, the event order changed (Event B preceded Event A); in the Probabilistic condition, the causal relationships changed (Cause A generated Effect B; Cause B generated Effect A). As intended, toddlers looked equally long at both changes (Experiment 1). We then introduced a previously unseen candidate cause. Toddlers looked longer at the appearance of a hand (Experiment 2) and novel agent (Experiment 3) in the Deterministic than the Probabilistic conditions, but looked equally long at novel non‐agents (Experiment 4), suggesting that by 2 years of age, toddlers connect probabilistic events with unobserved agents.