Causal Models of Clinically Significant Behaviors in Angelman, Cornelia de Lange, Prader-Willi and Smith-Magenis Syndromes

The operant learning theory account of behaviors of clinical significance in people with intellectual disability (ID) has dominated the field for nearly 50 years. However, in the last two decades, there has been a substantial increase in published research that describes the behavioral phenotypes of genetic disorders and shows that behaviors such as self-injury and aggression are more common in some syndromes than might be expected given group characteristics. These cross-syndrome differences in prevalence warrant explanation, not least because this observation challenges an exclusively operant learning theory account. To explore this possible conflict between theoretical account and empirical observation, we describe the genetic cause and physical, social, cognitive and behavioral phenotypes of four disorders associated with ID (Angleman, Cornelia de Lange, Prader-Willi and Smith-Magenis syndromes) and focus on the behaviors of clinical significance in each syndrome. For each syndrome we then describe a model of the interactions between physical characteristics, cognitive and motivational endophenotypes and environmental factors (including operant reinforcement) to account for the resultant behavioral phenotype. In each syndrome it is possible to identify pathways from gene to physical phenotype to cognitive or motivational endophenotype to behavior to environment and back to behavior. We identify the implications of these models for responsive and early intervention and the challenges for research in this area. We identify a pressing need for meaningful dialog between different disciplines to construct better informed models that can incorporate all relevant and robust empirical evidence.

Encouraging children to think counterfactually enhances blocking in a causal learning task

According to a higher order reasoning account, inferential reasoning processes underpin the widely observed cue competition effect of blocking in causal learning. The inference required for blocking has been described as modus tollens (if p then q, not q therefore not p). Young children are known to have difficulties with this type of inference, but research with adults suggests that this inference is easier if participants think counterfactually. In this study, 100 children (51 five-year-olds and 49 six- to seven-year-olds) were assigned to two types of pretraining groups. The counterfactual group observed demonstrations of cues paired with outcomes and answered questions about what the outcome would have been if the causal status of cues had been different, whereas the factual group answered factual questions about the same demonstrations. Children then completed a causal learning task. Counterfactual pretraining enhanced levels of blocking as well as modus tollens reasoning but only for the younger children. These findings provide new evidence for an important role for inferential reasoning in causal learning.

Blocking in children's causal learning depends on working memory and reasoning abilities.

A sample of 99 children completed a causal learning task that was an analogue of the food allergy paradigm used with adults. The cue competition effects of blocking and unovershadowing were assessed under forward and backward presentation conditions. Children also answered questions probing their ability to make the inference posited to be necessary for blocking by a reasoning account of cue competition. For the first time, children's working memory and general verbal ability were also measured alongside their causal learning. The magnitude of blocking and unovershadowing effects increased with age. However, analyses showed that the best predictor of both blocking and unovershadowing effects was children's performance on the reasoning questions. The magnitude of the blocking effect was also predicted by children's working memory abilities. These findings provide new evidence that cue competition effects such as blocking are underpinned by effortful reasoning processes. 

When does information about causal structure improve statistical reasoning?

Base rate neglect on the mammography problem can be overcome by explicitly presenting a causal basis for the typically vague false-positive statistic. One account of this causal facilitation effect is that people make probabilistic judgements over intuitive causal models parameterized with the evidence in the problem. Poorly defined or difficult-to-map evidence interferes with this process, leading to errors in statistical reasoning. To assess whether the construction of parameterized causal representations is an intuitive or deliberative process, in Experiment 1 we combined a secondary load paradigm with manipulations of the presence or absence of an alternative cause in typical statistical reasoning problems. We found limited effects of a secondary load, no evidence that information about an alternative cause improves statistical reasoning, but some evidence that it reduces base rate neglect errors. In Experiments 2 and 3 where we did not impose a load, we observed causal facilitation effects. The amount of Bayesian responding in the causal conditions was impervious to the presence of a load (Experiment 1) and to the precise statistical information that was presented (Experiment 3). However, we found less Bayesian responding in the causal condition than previously reported. We conclude with a discussion of the implications of our findings and the suggestion that there may be population effects in the accuracy of statistical reasoning.

Whose statistical reasoning is improved by information about causal structure?

People often struggle when making Bayesian probabilistic estimates on the basis of competing sources of statistical evidence. Recently, Krynski and Tenenbaum (Journal of Experimental Psychology: General, 136, 430–450, 2007) proposed that a causal Bayesian framework accounts for peoples’ errors in Bayesian reasoning and showed that, by clarifying the causal relations among the pieces of evidence, judgments on a classic statistical reasoning problem could be significantly improved. We aimed to understand whose statistical reasoning is facilitated by the causal structure intervention. In Experiment 1, although we observed causal facilitation effects overall, the effect was confined to participants high in numeracy. We did not find an overall facilitation effect in Experiment 2 but did replicate the earlier interaction between numerical ability and the presence or absence of causal content. This effect held when we controlled for general cognitive ability and thinking disposition. Our results suggest that clarifying causal structure facilitates Bayesian judgments, but only for participants with sufficient understanding of basic concepts in probability and statistics.

Causal knowledge and the development of inductive reasoning

We explored the development of sensitivity to causal relations in children’s inductive reasoning. Children (5-, 8-, and 12-year-olds) and adults were given trials in which they decided whether a property known to be possessed by members of one category was also possessed by members of (a) a taxonomically related category or (b) a causally related category. The direction of the causal link was either predictive (prey → predator) or diagnostic (predator → prey), and the property that participants reasoned about established either a taxonomic or causal context. There was a causal asymmetry effect across all age groups, with more causal choices when the causal link was predictive than when it was diagnostic. Furthermore, context-sensitive causal reasoning showed a curvilinear development, with causal choices being most frequent for 8-year-olds regardless of context. Causal inductions decreased thereafter because 12-year-olds and adults made more taxonomic choices when reasoning in the taxonomic context. These findings suggest that simple causal relations may often be the default knowledge structure in young children’s inductive reasoning, that sensitivity to causal direction is present early on, and that children over-generalize their causal knowledge when reasoning.

Temporal and statistical information in causal structure learning

Three experiments examined children’s and adults’ abilities to use statistical and temporal information to distinguish between common cause and causal chain structures. In Experiment 1, participants were provided with conditional probability information and/or temporal information and asked to infer the causal structure of a three-variable mechanical system that operated probabilistically. Participants of all ages preferentially relied on the temporal pattern of events in their inferences, even if this conflicted with statistical information. In Experiments 2 and 3, participants observed a series of interventions on the system, which in these experiments operated deterministically. In Experiment 2, participants found it easier to use temporal pattern information than statistical information provided as a result of interventions. In Experiment 3, in which no temporal pattern information was provided, children from 6-7 years, but not younger children, were able to use intervention information to make causal chain judgments, although they had difficulty when the structure was a common cause. The findings suggest that participants, and children in particular, may find it more difficult to use statistical information than temporal pattern information because of its demands on information processing resources. However, there may also be an inherent preference for temporal information.

Genetic Evidence for a Causal Role of Obesity in Diabetic Kidney Disease

Obesity has been posited as an independent risk factor for diabetic kidney disease (DKD), but establishing causality from observational data is problematic. We aimed to test whether obesity is causally related to DKD using Mendelian randomization, which exploits the random assortment of genes during meiosis. In 6,049 subjects with type 1 diabetes, we used a weighted genetic risk score (GRS) comprised of 32 validated BMI loci as an instrument to test the relationship of BMI with macroalbuminuria, end-stage renal disease (ESRD), or DKD defined as presence of macroalbuminuria or ESRD. We compared these results with cross-sectional and longitudinal observational associations. Longitudinal analysis demonstrated a U-shaped relationship of BMI with development of macroalbuminuria, ESRD, or DKD over time. Cross-sectional observational analysis showed no association with overall DKD, higher odds of macroalbuminuria (for every 1 kg/m(2) higher BMI, odds ratio [OR] 1.05, 95% CI 1.03-1.07, P < 0.001), and lower odds of ESRD (OR 0.95, 95% CI 0.93-0.97, P < 0.001). Mendelian randomization analysis showed a 1 kg/m(2) higher BMI conferring an increased risk in macroalbuminuria (OR 1.28, 95% CI 1.11-1.45, P = 0.001), ESRD (OR 1.43, 95% CI 1.20-1.72, P < 0.001), and DKD (OR 1.33, 95% CI 1.17-1.51, P < 0.001). Our results provide genetic evidence for a causal link between obesity and DKD in type 1 diabetes. As obesity prevalence rises, this finding predicts an increase in DKD prevalence unless intervention should occur.

Age- and Sex-Specific Causal Effects of Adiposity on Cardiovascular Risk Factors

Observational studies have reported different effects of adiposity on cardiovascular risk factors across age and sex. Since cardiovascular risk factors are enriched in obese individuals, it has not been easy to dissect the effects of adiposity from those of other risk factors. We used a Mendelian randomization approach, applying a set of 32 genetic markers to estimate the causal effect of adiposity on blood pressure, glycemic indices, circulating lipid levels, and markers of inflammation and liver disease in up to 67,553 individuals. All analyses were stratified by age (cutoff 55 years of age) and sex. The genetic score was associated with BMI in both nonstratified analysis (P = 2.8 × 10(-107)) and stratified analyses (all P < 3.3 × 10(-30)). We found evidence of a causal effect of adiposity on blood pressure, fasting levels of insulin, C-reactive protein, interleukin-6, HDL cholesterol, and triglycerides in a nonstratified analysis and in the <55-year stratum. Further, we found evidence of a smaller causal effect on total cholesterol (P for difference = 0.015) in the ≥55-year stratum than in the <55-year stratum, a finding that could be explained by biology, survival bias, or differential medication. In conclusion, this study extends previous knowledge of the effects of adiposity by providing sex- and age-specific causal estimates on cardiovascular risk factors.

Children's use of interventions to learn causal structure

Children aged between 5 and 8 years freely intervened on a three-variable causal system, with their task being to discover whether it was a common-cause structure or one of two causal chains. From 6-7 years, children were able to use information from their interventions to correctly disambiguate the structure of a causal chain. We used a Bayesian model to examine children’s interventions on the system; this showed that with development children became more efficient in producing the interventions needed to disambiguate the causal structure and that the quality of interventions, as measured by their informativeness, improved developmentally. The latter measure was a significant predictor of children’s correct inferences about the causal structure. A second experiment showed that levels of performance were not reduced in a task in which children did not select and carry out interventions themselves, indicating no advantage for self-directed learning. However, children’s performance was not related to intervention quality in these circumstances, suggesting that children learn in a different way when they carry out interventions themselves.

Moral asymmetries in judgments of agency withstand ludicrous causal deviance

Americans have been shown to attribute greater intentionality to immoral than to amoral actions in cases of causal deviance, that is, cases where a goal is satisfied in a way that deviates from initially planned means (e.g., a gunman wants to hit a target and his hand slips, but the bullet ricochets off a rock into the target). However, past research has yet to assess whether this asymmetry persists in cases of extreme causal deviance. Here, we manipulated the level of mild to extreme causal deviance of an immoral versus amoral act. The asymmetry in attributions of intentionality was observed at all but the
most extreme level of causal deviance, and, as we hypothesized, was mediated by attributions of Blame/credit and judgments of action performance. These findings are discussed as they support a multiple-concepts interpretation of the asymmetry, wherein blame renders a naïve concept of intentional action (the outcome matches the intention) more salient than a composite concept (the outcome matches the intention and was brought about by planned means), and in terms of their implications for cross-cultural research on judgments of agency.

The Influence of Causal Explanations and Diagnostic Labeling on Psychology Students’ Beliefs About Treatments, Prognosis, Dangerousness and Unpredictability in Schizophrenia

This study explored views of 566 Italian psychology students about schizophrenia. The most frequently cited causes were psychological traumas (68%) and heredity (54%). Thirty-three percent of students firmly believed that people with the condition could recover. Reporting heredity among the causes, and identifying schizophrenia were both associated with prognostic pessimism, greater confidence in pharmacological treatments and lower confidence in psychological treatments. Schizophrenia labeling was also associated with higher perception of unpredictability and dangerousness. Compared to first year students, fourth/fifth year students more frequently reported heredity among the causes, and were more pessimistic about schizophrenia recovery. Stigma topics should be included in future psychologists’ education.