The propositional nature of human associative learning

The past 50 years have seen an accumulation of evidence suggesting that associative learning depends oil high-level cognitive processes that give rise to propositional knowledge. Yet, many learning theorists maintain a belief in a learning mechanism in which links between mental representations are formed automatically. We characterize and highlight the differences between the propositional and link approaches, and review the relevant empirical evidence. We conclude that learning is the consequence of propositional reasoning processes that cooperate with the unconscious processes involved in memory retrieval and perception. We argue that this new conceptual framework allows many of the important recent advances in associative learning research to be retained, but recast in a model that provides a firmer foundation for both immediate application and future research

Assessing causal relationships in genomics: From Bradford-Hill criteria to complex gene-environment interactions and directed acyclic graphs

Observational studies of human health and disease (basic, clinical and epidemiological) are vulnerable to methodological problems -such as selection bias and confounding- that make causal inferences problematic. Gene-disease associations are no exception, as they are commonly investigated using observational designs. A rich body of knowledge exists in medicine and epidemiology on the assessment of causal relationships involving personal and environmental causes of disease; it includes seminal causal criteria developed by Austin Bradford Hill and more recently applied directed acyclic graphs (DAGs). However, such knowledge has seldom been applied to assess causal relationships in clinical genetics and genomics, even in studies aimed at making inferences relevant for human health. Conversely, incorporating genetic causal knowledge into clinical and epidemiological causal reasoning is still a largely unexplored area

Distinguishing cause from effect using observational data: methods and benchmarks

The discovery of causal relationships from purely observational data is a fundamental problem in science. The most elementary form of such a causal discovery problem is to decide whether X causes Y or, alternatively, Y causes X, given joint observations of two variables X, Y. An example is to decide whether altitude causes temperature, or vice versa, given only joint measurements of both variables. Even under the simplifying assumptions of no confounding, no feedback loops, and no selection bias, such bivariate causal discovery problems are challenging. Nevertheless, several approaches for addressing those problems have been proposed in recent years. We review two families of such methods: Additive Noise Methods (ANM) and Information Geometric Causal Inference (IGCI). We present the benchmark CauseEffectPairs that consists of data for 100 different cause-effect pairs selected from 37 datasets from various domains (e.g., meteorology, biology, medicine, engineering, economy, etc.) and motivate our decisions regarding the "ground truth" causal directions of all pairs. We evaluate the performance of several bivariate causal discovery methods on these real-world benchmark data and in addition on artificially simulated data. Our empirical results on real-world data indicate that certain methods are indeed able to distinguish cause from effect using only purely observational data, although more benchmark data would be needed to obtain statistically significant conclusions. One of the best performing methods overall is the additive-noise method originally proposed by Hoyer et al. (2009), which obtains an accuracy of 63+-10 % and an AUC of 0.74+-0.05 on the real-world benchmark. As the main theoretical contribution of this work we prove the consistency of that method.Comment: 101 pages, second revision submitted to Journal of Machine Learning Researc

Persistent environmental pollutants and risk of cardiovascular disease

Persistent chemicals emitted in the environment can have a considerable impact on ecosystems and human health, now and in the future. One notorious group of persistent organic pollutants (POPs) is the per- and polyfluoroalkyl substances (PFAS). Since their production in 1940s for household and consumer products, they have accumulated in the environment and in humans via consumption of contaminated drinking water and food. They are hypothesized to induce metabolic disturbances, due to shared chemical similarities with fatty acids. Consequently, PFAS may have high societal and economic impact by increasing risk of obesity, type 2 diabetes (T2D) and cardiovascular disease (CVD). However, reports on these associations are scarce, and the underlying molecular pathways are still unclear. Therefore, in this PhD project, we aimed to i) investigate associations between PFAS and risk of several cardiometabolic diseases and ii) explore potential underlying pathways. In Paper I, we investigated cross-sectional associations between PFAS mixtures and body mass index (BMI) in European teenagers using meta-regression. Results showed a tendency for inverse associations between PFAS and BMI and indicated a potential for diverging contributions between PFAS compounds. In Paper II, using a nested casecontrol study on T2D including metabolomics data in Swedish adults, we found that PFAS correlated positively with glycerophospholipids and diacylglycerols. But whilst glycerophospholipids associated with lower T2D risk, diacylglycerols associated with higher T2D risk. This indicates a potential for diverging effects on disease risk. In Paper III, we investigated whether genetic polymorphisms in peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PPARGC1A), which encodes a master regulator of pathways potentially disrupted by PFAS exposure, associated with secondary cardiovascular events in a large consortium study. However, we did not find clear evidence for such associations. In Paper IV, we assessed associations of PFAS with blood lipids and incident CVD using case-control studies nested in two Swedish adult cohorts. We observed overall null associations with stroke, but a tendency for inverse associations with myocardial infarction as well as associations with higher HDL-cholesterol and lower triglycerides, but also with higher LDL-cholesterol. In Paper V, we included OMICs data (metabolites, proteins and genes), which linked PFAS to lower myocardial infarction risk via lipid and inflammatory pathways. Likewise, a group of ‘old POPs’, the organochlorine compounds (OCs), were linked to higher myocardial infarction risk via the same pathways and to higher stroke risk via mitochondrial pathways. Thus, although we found no evidence for associations between PFAS and increased cardiometabolic disease risk, the overall findings indicate associations of PFAS with metabolic disturbances, particularly lipid metabolism. This is a potential adverse effect on human physiology and warrants further attention

Target Trial Emulation and Bias Through Missing Eligibility Data: An Application to a Study of Palivizumab for the Prevention of Hospitalization due to Infant Respiratory Illness

Target trial emulation (TTE) applies the principles of randomised controlled trials to the causal analysis of observational datasets. On challenge that is rarely considered in TTE is the sources of bias that may arise if the variables involved in the definition of eligibility into the trial are missing. We highlight patterns of bias that might arise when estimating the causal effect of a point exposure when restricting the target trial (TT) to individuals with complete eligibility data. Simulations consider realistic scenarios where the variables affecting eligibility modify the causal effect of the exposure and are Missing at Random (MAR) or Missing Not at Random (MNAR). We discuss multiple means to address these patterns of bias, namely, (i) controlling for the collider bias induced by the missing dataon eligibility, and (ii) imputing the missing values of the eligibility variables prior to selection into the TT. Results are compared to when TTE is performed ignoring the impact of missing eligibility. A study of Palivizumab, a monoclonal antibody recommended for the prevention of respiratory hospital admissions due to Respiratory Synctial Virus in high risk infants, is used for illustrations