Strange but true: Corroboration and base rate neglect

How do we deal with unlikely witness testimonies? Whether in legal or everyday reasoning, corroborative evidence is generally considered a strong marker of support for the reported hypothesis. However, questions remain regarding how the prior probability, or base rate, of that hypothesis interacts with corroboration. Using a Bayesian network model, we illustrate an inverse relationship between the base rate of a hypothesis, and the support provided by corroboration. More precisely, as the base rate of hypothesis becomes more unlikely (and thus there is lower expectation of corroborating testimony), each piece of confirming testimony provides a nonlinear increase in support, relative to a more commonplace hypothesis-assuming independence between witnesses. We show across 3 experiments that lay reasoners consistently fail to account for this impact of (rare) base rates in both diagnostic and intercausal reasoning, resulting in substantial underestimation in belief updating. We consider this a novel demonstration of an inverted form of base rate neglect. We highlight the implications of this work for any scenario in which one cannot assume the confirmation or disconfirmation of a reported hypothesis is uniform.

The factors driving evolved herbicide resistance at a national scale

Repeated use of xenobiotic chemicals has selected for the rapid evolution of resistance threatening health and food security at a global scale. Strategies for preventing the evolution of resistance include cycling and mixtures of chemicals and diversification of management. We currently lack large-scale studies that evaluate the efficacy of these different strategies for minimizing the evolution of resistance. Here we use a national scale dataset of occurrence of the weed Alopecurus myosuroides (Blackgrass) in the UK to address this. Weed densities are correlated with assays of evolved resistance, supporting the hypothesis that resistance is driving weed abundance at a national scale. Resistance was correlated with the frequency of historical herbicide applications suggesting that evolution of resistance is primarily driven by intensity of exposure to herbicides, but was unrelated directly to other cultural techniques. We find that populations resistant to one herbicide are likely to show resistance to multiple herbicide classes. Finally, we show that the economic costs of evolved resistance are considerable: loss of control through resistance can double the economic costs of weeds. This research highlights the importance of managing threats to food production and healthcare systems using an evolutionarily informed approach in a proactive not reactive manner

The Open Anchoring Quest Dataset: Anchored Estimates from 96 Studies on Anchoring Effects

People’s estimates are biased toward previously considered numbers (anchoring). We have aggregated all available data from anchoring studies that included at least two anchors into one large dataset. Data were standardized to comprise one estimate per row, coded according to a wide range of variables, and are available for download and analyses online (https://metaanalyses.shinyapps.io/OpAQ/). Because the dataset includes both original and meta-data it allows for fine-grained analyses (e.g., correlations of estimates for different tasks) but also for meta-analyses (e.g., effect sizes for anchoring effects)

Failures to replicate a key result of the selective accessibility theory of anchoring

Numerical anchoring effects describe the assimilative effect of a previously presented number on subsequent numerical estimates. Such effects are robust and consequential. A number of different accounts have been proposed to explain these effects. What is currently unclear is under which situations different mechanisms play more or less critical roles. An extant test from the literature is proposed as a ‘signature test’ for the operation of selective accessibility mechanisms. Four experiments were conducted to ascertain the evidence for selective accessibility with this test, tests that subsequently failed. A fifth experiment employed a different methodology, and again failed to show evidence for selective accessibility. Subsequent discussion suggests that the robustness of anchoring effects is remarkable, but the theoretical basis for some previous tests of the selective accessibility account of anchoring is shaky, and we advise against its use in this capacity

The open anchoring quest dataset:anchored estimates from 96 studies on anchoring effects

People’s estimates are biased toward previously considered numbers (anchoring). We have aggregated all available data from anchoring studies that included at least two anchors into one large dataset. Data were standardized to comprise one estimate per row, coded according to a wide range of variables, and are available for download and analyses online (https://metaanalyses.shinyapps.io/OpAQ/). Because the dataset includes both original and meta-data it allows for fine-grained analyses (e.g., correlations of estimates for different tasks) but also for meta-analyses (e.g., effect sizes for anchoring effects)

Biophysical ambiguities prevent accurate genetic prediction

A goal of biology is to predict how mutations combine to alter phenotypes, fitness and disease. It is often assumed that mutations combine additively or with interactions that can be predicted. Here, we show using simulations that, even for the simple example of the lambda phage transcription factor CI repressing a gene, this assumption is incorrect and that perfect measurements of the effects of mutations on a trait and mechanistic understanding can be insufficient to predict what happens when two mutations are combined. This apparent paradox arises because mutations can have different biophysical effects to cause the same change in a phenotype and the outcome in a double mutant depends upon what these hidden biophysical changes actually are. Pleiotropy and non-monotonic functions further confound prediction of how mutations interact. Accurate prediction of phenotypes and disease will sometimes not be possible unless these biophysical ambiguities can be resolved using additional measurements.This work was supported by a European Research Council (ERC) Consolidator grant (616434), the Spanish Ministry of Economy and Competitiveness (BFU2017-89488-P and SEV-2012-0208), the Bettencourt Schueller Foundation, Agencia de Gestio d’Ajuts Universitaris i de Recerca (AGAUR, 2017 SGR 1322), and the CERCA Program/Generalitat de Catalunya. We also acknowledge the support of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership and the Centro de Excelencia Severo Ochoa