The illusion of insight: detailed warnings reduce but do not prevent false "Aha!" moments.

False "Aha!" moments can be elicited experimentally using the False Insight Anagram Task (FIAT), which combines semantic priming and visual similarity manipulations to lead participants into having "Aha!" moments for incorrect anagram solutions. In a preregistered experiment (N = 255), we tested whether warning participants and explaining to them exactly how they were being deceived, would reduce their susceptibility to false insights. We found that simple warnings did not reduce the incidence of false insights. On the other hand, participants who were given a detailed explanation of the methods used to deceive them experienced a small reduction in false insights compared to participants given no warning at all. Our findings suggest that the FIAT elicits a robust false insight effect that is hard to overcome, demonstrating the persuasive nature of false insights when the conditions are ripe for them.

Thinking style and psychosis proneness do not predict false insights.

The FIAT paradigm (Grimmer et al., 2021) is a novel method of eliciting 'Aha' moments for incorrect solutions to anagrams in the laboratory, i.e. false insights. There exist many documented reports of psychotic symptoms accompanying strong feelings of 'Aha!' (Feyaerts, Henriksen, Vanheule, Myin-Germeys, & Sass, 2021; Mishara, 2010; Tulver, Kaup, Laukkonen, & Aru, 2021), suggesting that the newly developed FIAT could reveal whether people who have more false insights are more prone to psychosis and delusional belief. To test this possibility, we recruited 200 participants to take an adapted version of the FIAT and complete measures of thinking style and psychosis proneness. We found no association between experimentally induced false insights and measures of Schizotypy, Need for Cognition, Jumping to Conclusions, Aberrant Salience, Faith in Intuition, or the Cognitive Reflection Task. We conclude that experiencing false insights might not be constrained to any particular type of person, but rather, may arise for anyone under the right circumstances.

Association between DNA methylation variability and self-reported exposure to heavy metals.

Individuals encounter varying environmental exposures throughout their lifetimes. Some exposures such as smoking are readily observed and have high personal recall; others are more indirect or sporadic and might only be inferred from long occupational histories or lifestyles. We evaluated the utility of using lifetime-long self-reported exposures for identifying differential methylation in an amyotrophic lateral sclerosis cases-control cohort of 855 individuals. Individuals submitted paper-based surveys on exposure and occupational histories as well as whole blood samples. Genome-wide DNA methylation levels were quantified using the Illumina Infinium Human Methylation450 array. We analyzed 15 environmental exposures using the OSCA software linear and MOA models, where we regressed exposures individually by methylation adjusted for batch effects and disease status as well as predicted scores for age, sex, cell count, and smoking status. We also regressed on the first principal components on clustered environmental exposures to detect DNA methylation changes associated with a more generalised definition of environmental exposure. Five DNA methylation probes across three environmental exposures (cadmium, mercury and metalwork) were significantly associated using the MOA models and seven through the linear models, with one additionally across a principal component representing chemical exposures. Methylome-wide significance for four of these markers was driven by extreme hyper/hypo-methylation in small numbers of individuals. The results indicate the potential for using self-reported exposure histories in detecting DNA methylation changes in response to the environment, but also highlight the confounded nature of environmental exposure in cohort studies.

Significant out-of-sample classification from methylation profile scoring for amyotrophic lateral sclerosis.

We conducted DNA methylation association analyses using Illumina 450K data from whole blood for an Australian amyotrophic lateral sclerosis (ALS) case-control cohort (782 cases and 613 controls). Analyses used mixed linear models as implemented in the OSCA software. We found a significantly higher proportion of neutrophils in cases compared to controls which replicated in an independent cohort from the Netherlands (1159 cases and 637 controls). The OSCA MOMENT linear mixed model has been shown in simulations to best account for confounders. When combined in a methylation profile score, the 25 most-associated probes identified by MOMENT significantly classified case-control status in the Netherlands sample (area under the curve, AUC = 0.65, CI95% = [0.62-0.68], p = 8.3 × 10-22). The maximum AUC achieved was 0.69 (CI95% = [0.66-0.71], p = 4.3 × 10-34) when cell-type proportion was included in the predictor.

Prospects for building large timetrees using molecular data with incomplete gene coverage among species.

Scientists are assembling sequence data sets from increasing numbers of species and genes to build comprehensive timetrees. However, data are often unavailable for some species and gene combinations, and the proportion of missing data is often large for data sets containing many genes and species. Surprisingly, there has not been a systematic analysis of the effect of the degree of sparseness of the species-gene matrix on the accuracy of divergence time estimates. Here, we present results from computer simulations and empirical data analyses to quantify the impact of missing gene data on divergence time estimation in large phylogenies. We found that estimates of divergence times were robust even when sequences from a majority of genes for most of the species were absent. From the analysis of such extremely sparse data sets, we found that the most egregious errors occurred for nodes in the tree that had no common genes for any pair of species in the immediate descendant clades of the node in question. These problematic nodes can be easily detected prior to computational analyses based only on the input sequence alignment and the tree topology. We conclude that it is best to use larger alignments, because adding both genes and species to the alignment augments the number of genes available for estimating divergence events deep in the tree and improves their time estimates.