False Inferences Research Articles

Misleading Health Consumers Through Violations of Communicative Norms: A Case Study of Online Diabetes Education

Communication is a cooperative endeavor that goes well beyond decoding sentences’ literal meaning. Listeners actively construe the meaning of utterances from both their literal meanings and the pragmatic principles that govern communication. When communicators make pragmatically infelicitous statements, the effects can be similar to paltering—misleading speech that evokes false inferences from true statements. The American Diabetes Association’s (ADA’s) “Diabetes Myths” website provides a real-world case study in such misleading communications. Calling something a myth implies that it is clearly false. Instead, the ADA’s “myths” are false only because of some technicality or uncharitable reading. We compared participants’ baseline knowledge of diabetes with that of participants who read either the ADA’s myths or the myths rewritten as questions that do not presuppose the statement is false. As predicted, exposure to the ADA’s “myths,” but not to the rephrased questions, reduced basic knowledge of diabetes. Our findings underscore the need to consider psycholinguistic principles in mass communications.

F245. Hallucinations and Self-Generated Imprecision: A Markov Decision Process Model of Auditory Hallucinations

Auditory verbal hallucinations (AVH) are often distressing symptoms of several neuropsychiatric conditions. The information processing deficits underlying AVH are not yet clear. Using a Markov Decision Process, a Bayesian Active Inference model, we have produced a novel, neurobiologically-informed model of AVH as false (positive) inferences.

Computational Neuropsychology and Bayesian Inference.

Computational theories of brain function have become very influential in neuroscience. They have facilitated the growth of formal approaches to disease, particularly in psychiatric research. In this paper, we provide a narrative review of the body of computational research addressing neuropsychological syndromes, and focus on those that employ Bayesian frameworks. Bayesian approaches to understanding brain function formulate perception and action as inferential processes. These inferences combine ‘prior’ beliefs with a generative (predictive) model to explain the causes of sensations. Under this view, neuropsychological deficits can be thought of as false inferences that arise due to aberrant prior beliefs (that are poor fits to the real world). This draws upon the notion of a Bayes optimal pathology – optimal inference with suboptimal priors – and provides a means for computational phenotyping. In principle, any given neuropsychological disorder could be characterized by the set of prior beliefs that would make a patient’s behavior appear Bayes optimal. We start with an overview of some key theoretical constructs and use these to motivate a form of computational neuropsychology that relates anatomical structures in the brain to the computations they perform. Throughout, we draw upon computational accounts of neuropsychological syndromes. These are selected to emphasize the key features of a Bayesian approach, and the possible types of pathological prior that may be present. They range from visual neglect through hallucinations to autism. Through these illustrative examples, we review the use of Bayesian approaches to understand the link between biology and computation that is at the heart of neuropsychology.

The Impact of Extension Services on Farm‐level Income: An Instrumental Variable Approach to Combat Endogeneity Concerns

AbstractAgricultural extension is an important policy instrument utilized to diffuse knowledge and increase profitability among farmers. However, analyses on impact are subject to endogeneity concerns, causing multiple biases. Failure to combat endogeneity can lead to false inferences on impact. This article addresses this issue by applying an instrumental variable approach with distance to local advisory office and a policy change chosen as instruments for extension participation. The results show that participation significantly increased farm income and that OLS estimates underestimated the impact. Therefore, a superior estimate of impact is achieved which can be leveraged to better support accurate policy making.

Drawing False Inferences from Mandated Disclosures

Disclosure mandates are pervasive. Though designed to inform consumers, such mandates may lead consumers to draw false inferences – for example, that a product is harmful when it is not. When deciding to require disclosure of an ingredient in or characteristic of a product, regulators may be motivated by evidence that the ingredient or characteristic is harmful to consumers. But they may also be motivated by a belief that consumers have a right to know what they are buying or by interest-group pressure. Consumers who misperceive the regulator’s true motive, or mix of motives, will draw false inferences from the mandated disclosure. If consumers think that the disclosure is motivated by evidence of harm, when in fact it is motivated by a belief in a right-to-know or by interest-group pressure, then they will be inefficiently deterred from purchasing the product. We analyze this general concern about disclosure mandates. We also offer survey evidence demonstrating that the risk of false inferences is serious and real. Our framework has implications for the ongoing debate over the labeling of food with genetically modified organisms (GMOs); it suggests that the relevant labels might prove misleading to some or many consumers, producing a potentially serious welfare loss. Under prevailing executive orders, regulators must consider that loss and if feasible, quantify it.

Drawing false inferences from mandated disclosures

AbstractDisclosure mandates are pervasive. Though designed to inform consumers, such mandates may lead consumers to draw false inferences – for example, that a product is harmful when it is not. When deciding to require disclosure of an ingredient in or characteristic of a product, regulators may be motivated by evidence that the ingredient or characteristic is harmful to consumers. But they may also be motivated by a belief that consumers have a right to know what they are buying or by interest-group pressure. Consumers who misperceive the regulator's true motive, or mix of motives, will draw false inferences from the mandated disclosure. If consumers think that the disclosure is motivated by evidence of harm, when in fact it is motivated by a belief in a right to know or by interest-group pressure, then they will be inefficiently deterred from purchasing the product. We analyze this general concern about disclosure mandates. We also offer survey evidence demonstrating that the risk of false inferences is serious and real.

Evaluation of statistical methods for quantifying fractal scaling in water-quality time series with irregular sampling

Abstract. River water-quality time series often exhibit fractal scaling, which here refers to autocorrelation that decays as a power law over some range of scales. Fractal scaling presents challenges to the identification of deterministic trends because (1) fractal scaling has the potential to lead to false inference about the statistical significance of trends and (2) the abundance of irregularly spaced data in water-quality monitoring networks complicates efforts to quantify fractal scaling. Traditional methods for estimating fractal scaling – in the form of spectral slope (β) or other equivalent scaling parameters (e.g., Hurst exponent) – are generally inapplicable to irregularly sampled data. Here we consider two types of estimation approaches for irregularly sampled data and evaluate their performance using synthetic time series. These time series were generated such that (1) they exhibit a wide range of prescribed fractal scaling behaviors, ranging from white noise (β = 0) to Brown noise (β = 2) and (2) their sampling gap intervals mimic the sampling irregularity (as quantified by both the skewness and mean of gap-interval lengths) in real water-quality data. The results suggest that none of the existing methods fully account for the effects of sampling irregularity on β estimation. First, the results illustrate the danger of using interpolation for gap filling when examining autocorrelation, as the interpolation methods consistently underestimate or overestimate β under a wide range of prescribed β values and gap distributions. Second, the widely used Lomb–Scargle spectral method also consistently underestimates β. A previously published modified form, using only the lowest 5 % of the frequencies for spectral slope estimation, has very poor precision, although the overall bias is small. Third, a recent wavelet-based method, coupled with an aliasing filter, generally has the smallest bias and root-mean-squared error among all methods for a wide range of prescribed β values and gap distributions. The aliasing method, however, does not itself account for sampling irregularity, and this introduces some bias in the result. Nonetheless, the wavelet method is recommended for estimating β in irregular time series until improved methods are developed. Finally, all methods' performances depend strongly on the sampling irregularity, highlighting that the accuracy and precision of each method are data specific. Accurately quantifying the strength of fractal scaling in irregular water-quality time series remains an unresolved challenge for the hydrologic community and for other disciplines that must grapple with irregular sampling.

The Analysis of Polyploid Genetic Data.

Though polyploidy is an important aspect of the evolutionary genetics of both plants and animals, the development of population genetic theory of polyploids has seriously lagged behind that of diploids. This is unfortunate since the analysis of polyploid genetic data-and the interpretation of the results-requires even more scrutiny than with diploid data. This is because of several polyploidy-specific complications in segregation and genotyping such as tetrasomy, double reduction, and missing dosage information. Here, we review the theoretical and statistical aspects of the population genetics of polyploids. We discuss several widely used types of inferences, including genetic diversity, Hardy-Weinberg equilibrium, population differentiation, genetic distance, and detecting population structure. For each, we point out how the statistical approach, expected result, and interpretation differ between different ploidy levels. We also discuss for each type of inference what biases may arise from the polyploid-specific complications and how these biases can be overcome. From our overview, it is clear that the statistical toolbox that is available for the analysis of genetic data is flexible and still expanding. Modern sequencing techniques will soon be able to overcome some of the current limitations to the analysis of polyploid data, though the techniques are lagging behind those available for diploids. Furthermore, the availability of more data may aggravate the biases that can arise, and increase the risk of false inferences. Therefore, simulations such as we used throughout this review are an important tool to verify the results of analyses of polyploid genetic data.

Possible Sediment Mixing and the Disparity between Field Measurements and Paleolimnological Inferences in Shallow Iowa Lakes in the Midwestern United States

Field measurements of water quality in Iowa lakes contradict paleolimnological studies that used 210Pb dating techniques in 33 lakes to infer accelerating eutrophication and sediment accumulation in recent decades. We tested this hypothesis by analyzing a series of water quality measurements taken in 24 of these lakes during the period 1972–2010. There was little change in the trophic state variables. Total phosphorus and algal chlorophylls did not increase, and Secchi depths did not decrease with no evidence that the lakes had become more eutrophic. Changes in daily sediment loads in the Raccoon River also did not match the paleolimnological inferred rates of soil erosion for the period 1905–2005, and an independent estimate of soil erosion rates showed a decline of 40% in the 1977 to 2012 period rather than an increase. We hypothesized that sediment mixing by benthivorous fish could be responsible for violating the basic assumption of 210Pb sediment dating that the sediments are not disturbed once they are laid down. We developed a mathematical model that demonstrated that sediment mixing could lead to false inferences about sediment dates and sediment burial rates. This study raises the possibility that sediment mixing in Iowa lakes and similar shallow, eutrophic lakes with benthivorous fish may cause significant sediment mixing that can compromise dating using 210Pb dating of sediment cores.

Continuous traits and speciation rates: Alternatives to state‐dependent diversification models

Abstract Many quantitative traits, for example body size, have been hypothesized to influence the diversification dynamics of lineages over macroevolutionary time‐scales. The Quantitative State Speciation‐Extinction (QuaSSE) model and related methods provide an elegant framework for jointly modelling the relationship between change in continuous traits and diversification. However, model misspecification and phylogenetic pseudoreplication can result in elevated false discovery rates in this and other state‐dependent speciation‐extinction models. Here, we evaluate alternative trait‐dependent diversification methods that do not formally model the relationship between traits and diversification, but instead test for correlations between summary statistics of phylogenetic branching patterns and trait variation at the tips of a phylogenetic tree (hereafter tip‐rate correlations or TRCs). We compare alternative branching pattern statistics and significance tests, and we evaluate their performance relative to QuaSSE under a range of evolutionary scenarios. We found that a simple statistic derived from branch lengths (inverse equal splits) can detect trait‐associated rate variation, and that a simulation‐based method performs better than phylogenetic generalized least squares for testing the significance of trait‐rate correlations. This test (ES‐sim) had better power to detect trait‐dependent diversification than other TRCs. By testing the approach across a diverse set of simulation scenarios, we found that ES‐sim is similar to QuaSSE in statistical power. However, the approach rarely led to false inferences of trait‐dependent diversification, even under conditions that are problematic for formal state‐dependent models. We illustrate the application of ES‐sim to real data by re‐assessing the relationship between dispersal ability and diversification in Furnariid birds. We conclude that simple, semi‐parametric tests like ES‐sim represent a promising approach for trait‐dependent diversification analyses in groups with heterogeneous diversification histories and provide a useful alternative or complement to formal state‐dependent speciation‐extinction models.

Constructing National Interests: Narrating the Suez Crisis

ABSTRACTThe year 2016 marked the sixtieth anniversary of the Suez crisis. Proposing a metaphor of a chain of hypothetical propositions, this article examines the construction of national interests through a case study of the Suez crisis. This metaphor offers a useful way to step back from common sense and to think of Britain’s official representation of “the Suez crisis” as numerous antecedents and consequents coupled together. It then enables researchers to: (1) detect each individual proposition hidden in plain sight through the identification of inference indicators; (2) examine the actual truth or falsity of each individual proposition; (3) see a striking parallel between the deduction of one proposition from another and the construction of national interests; and (4) apprehend why valid yet false inferences obtained by pure deduction could appear as incontrovertible truths even in the absence of empirical foundations.

Phylogenetic relationships among tribes of the green lacewing subfamily Chrysopinae recovered based on mitochondrial phylogenomics

Chrysopidae (green lacewings) is the second largest family in Neuroptera, and it includes medium-size lacewings largely recognized by the presence of golden-colored eyes, bright green bodies and delicate wings with dense venation patterns. The subfamily Chrysopinae includes 97% of the species diversity in the family and it is currently divided into four tribes: Ankylopterygini, Belonopterygini, Chrysopini and Leucochrysini. Here we sequenced and annotated the nearly complete mitochondrial genomes of four species of each these tribes: Abachrysa eureka, Italochrysa insignis, Leucochrysa pretiosa, Parankyloteryx sp. We then reconstructed the phylogenetic relationships with estimated divergence times among tribes of Chrysopinae based on the mt genomic data. Our results suggest that Chrysopinae sans Nothancyla verreauxi evolved as two reciprocally monophyletic lineages formed by stem members of the tribes Leucochrysini plus Belonopterygini on one hand, and the stem members of Ankylopterygini plus Chrysopini on the other. Our estimations of divergence times place the diversification of stem Chrysopinae into the extant tribes during the Middle Jurassic to Late Cretaceous. The relatively young ages previously estimated for the green lacewing divergences were probably underestimated due to false inferences of homology between non-sister taxa that are later correctly identified as homoplasy after more taxa are added.

Visual Hallucinations Are Characterized by Impaired Sensory Evidence Accumulation: Insights From Hierarchical Drift Diffusion Modeling in Parkinson’s Disease

BackgroundModels of hallucinations emphasize imbalance between sensory input and top-down influences over perception, as false perceptual inference can arise when top-down predictions are afforded too much precision (certainty) relative to sensory evidence. Visual hallucinations in Parkinson’s disease (PD) are associated with lower-level visual and attentional impairments, accompanied by overactivity in higher-order association brain networks. PD therefore provides an attractive framework to explore contributions of bottom-up versus top-down disturbances in hallucinations. MethodsWe characterized sensory processing during perceptual decision making in patients with PD with (n = 20) and without (n = 25) visual hallucinations and control subjects (n = 12), by fitting a hierarchical drift diffusion model to an attentional task. The hierarchical drift diffusion model uses Bayesian estimates to decompose task performance into parameters reflecting drift rates of evidence accumulation, decision thresholds, and nondecision time. ResultsWe observed slower drift rates in patients with hallucinations, which were less sensitive to changes in task demand. In contrast, wider decision boundaries and shorter nondecision times relative to control subjects were found in patients with PD regardless of hallucinator status. Inefficient and less flexible sensory evidence accumulation emerges as a unique feature of PD hallucinators. ConclusionsWe integrate these results with evidence accumulation and predictive coding models of hallucinations, suggesting that in PD sensory evidence is less informative and may therefore be down-weighted, resulting in overreliance on top-down influences. Considering impaired drift rates as an approximation of reduced sensory precision, our findings provide a novel computational framework to specify impairments in sensory processing that contribute to development of visual hallucinations.

Mixed signals: Charity reporting when donations signal generosity and income

False inference may be drawn in asymmetric information environments where the type space is multi-dimensional and the analysis is restricted to only one of those dimensions. We demonstrate this by studying donation-visibility in a charitable-giving environment. Past studies show that donation-visibility increases giving and argue that this is consistent with donations signaling generosity or income and thereby improving donors' status. However, this explanation relies on status being one-dimensional, acquired from only one attribute: generosity or income. The response may differ when instead status is multi-dimensional, depending on both attributes. Donors who prefer to be perceived as poor-and-generous rather than rich-and-stingy may give less when donations are visible. Using an experiment we examine the effect of donation-visibility when donations can signal multiple attributes. Revealing concerns for both income- and generosity-status, we find, in contrast to the one-attribute setting, that donation-visibility does not increase giving when donations signal both attributes.

Estimation of the time delay of coupling between oscillators from time realizations of oscillation phases for different properties of phase dynamics

The problem of interval estimation of the time delay of the coupling between oscillatory systems from observed time series is considered. It is shown that the known asymptotic estimates based on the empirical model in the form of a system of first-order phase oscillators and the maximum likelihood formalism can lead to false inferences of the value of the time delay in two typical situations: 1) nonlinear low-dimensional systems whose phases are well-determined but, as a result of significant amplitude fluctuations, the phase approximation is insufficient for describing the dynamics and 2) systems whose phases are defined not quite well because of too large amplitude fluctuations. A method for empirical diagnostics of problematic situations and its modification (coarse estimation) providing a low probability of false inferences in these situation are proposed. The efficiency of the diagnostic criterion and coarse estimation suggested is demonstrated on reference systems with different dynamic properties (linear stochastic oscillators, van der Pol oscillators, and Ressler and Lorenz chaotic systems).

Pleading, Discovering, and Proving a Sherman Act Agreement: Harmonizing Twombly and Matsushita

Twombly and Matsushita have created a staged decision-theoretic inquiry in which courts ask at the pleading stage and, if necessary, again after discovery whether the plaintiff has made a sufficiently “plausible” showing to justify the expected direct and indirect costs of further inquiry. Rather than pass contentions of fact through the stages of litigation with minimal review, the framework increases the burdens on pretrial motions, professedly to avoid unjustified direct costs of discovery and indirect costs of “false inferences.” In cases like Twombly and Matsushita themselves, that allege price fixing or other per se violations of Section 1 of the Sherman Act, the issue on these motions turns on whether the plaintiff has made a plausible showing of “agreement” rather than mere interdependent oligopolistic pricing. As I have shown elsewhere, courts distinguish agreement from interdependent pricing by the sorts of communications the rivals used to coordinate their actions: plaintiffs must allege facts and later offer evidence that raises a plausible inference that the rivals communicated privately about their future competitive choices. Courts differ, however, on whether plausibility means different things at the two stages. Some reason that, because the plaintiff has to find evidence of private communication among rivals, the showing necessary to reach discovery should be less onerous than the showing necessary to reach a jury. I examine the consequences of these different standards, and consider whether predismissal discovery might mitigate the problems of estimation in evaluating plausibility.

Stochastic Conceptual Cost Estimating of Highway Projects to Communicate Uncertainty Using Bootstrap Sampling

AbstractConceptual cost estimating is typically completed early in the project lifecycle when little design work has been completed. Because little information is known at this early stage, the estimate usually deviates substantially from the actual construction cost. When expressed as a deterministic value, an estimate often leads to a false inference of accuracy by those not familiar with the vagaries of conceptual cost estimating, making it difficult for an agency to explain cost growth. A stochastic conceptual estimate allows an agency to produce a probability distribution of the likely construction costs and address the level of confidence in a given estimate. Named probability distributions are readily available for developing a stochastic estimate in a great deal of commercial software. However, instead of fitting available distributions, this research generates an empirical distribution to express a cost estimate range. Creating empirical distributions eliminates assumptions required for selecting...

Signatures of Archaic Adaptive Introgression in Present-Day Human Populations.

Comparisons of DNA from archaic and modern humans show that these groups interbred, and in some cases received an evolutionary advantage from doing so. This process—adaptive introgression—may lead to a faster rate of adaptation than is predicted from models with mutation and selection alone. Within the last couple of years, a series of studies have identified regions of the genome that are likely examples of adaptive introgression. In many cases, once a region was ascertained as being introgressed, commonly used statistics based on both haplotype as well as allele frequency information were employed to test for positive selection. Introgression by itself, however, changes both the haplotype structure and the distribution of allele frequencies, thus confounding traditional tests for detecting positive selection. Therefore, patterns generated by introgression alone may lead to false inferences of positive selection. Here we explore models involving both introgression and positive selection to investigate the behavior of various statistics under adaptive introgression. In particular, we find that the number and allelic frequencies of sites that are uniquely shared between archaic humans and specific present-day populations are particularly useful for detecting adaptive introgression. We then examine the 1000 Genomes dataset to characterize the landscape of uniquely shared archaic alleles in human populations. Finally, we identify regions that were likely subject to adaptive introgression and discuss some of the most promising candidate genes located in these regions.

Compositional uncertainty should not be ignored in high-throughput sequencing data analysis

High throughput sequencing generates sparse compositional data, yet these datasets are rarely analyzed using a compositional approach. In addition, the variation inherent in these datasets is rarely acknowledged, but ignoring it can result in many false positive inferences. We demonstrate that examination of point estimates of the data can result in false positive results, even with appropriate zero replacement approaches, using an in vitro selection dataset with an outside standard of truth. The variation inherent in real high-throughput sequencing datasets is demonstrated, and we show that this varia- tion can be approximated, and hence accounted for, by Monte-Carlo sampling from the Dirichlet distribution. This approximation when used by itself is itself problematic, but becomes useful when coupled with a log-ratio approach commonly used in compositional data analysis. Thus, the approach illustrated here that merges Bayesian estimation with principles of compositional data analysis should be generally useful for high-dimensional count compositional data of the type generated by high throughput sequencing.

Improvement of Parameter Estimations in Tumor Growth Inhibition Models on Xenografted Animals: Handling Sacrifice Censoring and Error Caused by Experimental Measurement on Larger Tumor Sizes.

The purpose of this study was to explore the impact of censoring due to animal sacrifice on parameter estimates and tumor volume calculated from two diameters in larger tumors during tumor growth experiments in preclinical studies. The type of measurement error that can be expected was also investigated. Different scenarios were challenged using the stochastic simulation and estimation process. One thousand datasets were simulated under the design of a typical tumor growth study in xenografted mice, and then, eight approaches were used for parameter estimation with the simulated datasets. The distribution of estimates and simulation-based diagnostics were computed for comparison. The different approaches were robust regarding the choice of residual error and gave equivalent results. However, by not considering missing data induced by sacrificing the animal, parameter estimates were biased and led to false inferences in terms of compound potency; the threshold concentration for tumor eradication when ignoring censoring was 581ng.ml(-1), but the true value was 240ng.ml(-1).