False Inferences Research Articles

Hill-Robertson interference may bias the inference of fitness effects of new mutations in highly selfing species.

The accurate estimation of the distribution of fitness effects (DFE) of new mutations is critical for population genetic inference but remains a challenging task. While various methods have been developed for DFE inference using the site frequency spectrum of putatively neutral and selected sites, their applicability in species with diverse life history traits and complex demographic scenarios is not well understood. Selfing is common among eukaryotic species and can lead to decreased effective recombination rates, increasing the effects of selection at linked sites, including interference between selected alleles. We employ forward simulations to investigate the limitations of current DFE estimation approaches in the presence of selfing and other model violations, such as linkage, departures from semidominance, population structure, and uneven sampling. We find that distortions of the site frequency spectrum due to Hill-Robertson interference in highly selfing populations lead to mis-inference of the deleterious DFE of new mutations. Specifically, when inferring the distribution of selection coefficients, there is an overestimation of nearly neutral and strongly deleterious mutations and an underestimation of mildly deleterious mutations when interference between selected alleles is pervasive. In addition, the presence of cryptic population structure with low rates of migration and uneven sampling across subpopulations leads to the false inference of a deleterious DFE skewed towards effectively neutral/mildly deleterious mutations. Finally, the proportion of adaptive substitutions estimated at high rates of selfing is substantially overestimated. Our observations apply broadly to species and genomic regions with little/no recombination and where interference might be pervasive.

False Inference in Religious Islamic texts

This research addresses a set of examples that illustrate false inference methods by various entities, whether it is inference by authorities, groups, or individuals. The researcher has directed the inference to follow its correct path, steering clear of distortions by malicious individuals and the exaggerations of extremists. This examination is necessary because, in the past, there has been false inference from texts in the Quran and Sunnah, and this misuse continues, highlighting the importance of providing corrections and drawing attention to the proper understanding.

Nondifferentiable activity in the brain.

Spike raster plots of numerous neurons show vertical stripes, indicating that neurons exhibit synchronous activity in the brain. We seek to determine whether these coherent dynamics are caused by smooth brainwave activity or by something else. By analyzing biological data, we find that their cross-correlograms exhibit not only slow undulation but also a cusp at the origin, in addition to possible signs of monosynaptic connectivity. Here we show that undulation emerges if neurons are subject to smooth brainwave oscillations while a cusp results from nondifferentiable fluctuations. While modern analysis methods have achieved good connectivity estimation by adapting the models to slow undulation, they still make false inferences due to the cusp. We devise a new analysis method that may solve both problems. We also demonstrate that oscillations and nondifferentiable fluctuations may emerge in simulations of large-scale neural networks.

Selection leads to false inferences of introgression using popular methods.

Detecting introgression between closely related populations or species is a fundamental objective in evolutionary biology. Existing methods for detecting migration and inferring migration rates from population genetic data often assume a neutral model of evolution. Growing evidence of the pervasive impact of selection on large portions of the genome across diverse taxa suggests that this assumption is unrealistic in most empirical systems. Further, ignoring selection has previously been shown to negatively impact demographic inferences (e.g. of population size histories). However, the impacts of biologically realistic selection on inferences of migration remain poorly explored. Here, we simulate data under models of background selection, selective sweeps, balancing selection, and adaptive introgression. We show that ignoring selection sometimes leads to false inferences of migration in popularly used methods that rely on the site frequency spectrum. Specifically, balancing selection and some models of background selection result in the rejection of isolation-only models in favor of isolation-with-migration models and lead to elevated estimates of migration rates. BPP, a method that analyzes sequence data directly, showed false positives for all conditions at recent divergence times, but balancing selection also led to false positives at medium-divergence times. Our results suggest that such methods may be unreliable in some empirical systems, such that new methods that are robust to selection need to be developed.

Generative AI for corpus approaches to discourse studies: A critical evaluation of ChatGPT

This paper explores the potential of generative artificial intelligence technology, specifically ChatGPT, for advancing corpus approaches to discourse studies. The contribution of artificial intelligence technologies to linguistics research has been transformational, both in the contexts of corpus linguistics and discourse analysis. However, shortcomings in the efficacy of such technologies for conducting automated qualitative analysis have limited their utility for corpus approaches to discourse studies. Acknowledging that new technologies in data analysis can replace and supplement existing approaches, and in view of the potential affordances of ChatGPT for automated qualitative analysis, this paper presents three replication case studies designed to investigate the applicability of ChatGPT for supporting automated qualitative analysis within studies using corpus approaches to discourse analysis.The findings indicate that, generally, ChatGPT performs reasonably well when semantically categorising keywords; however, as the categorisation is based on decontextualised keywords, the categories can appear quite generic, limiting the value of such an approach for analysing corpora representing specialised genres and/or contexts. For concordance analysis, ChatGPT performs poorly, as the results include false inferences about the concordance lines and, at times, modifications of the input data. Finally, for function-to-form analysis, ChatGPT also performs poorly, as it fails to identify and analyse direct and indirect questions. Overall, the results raise questions about the affordances of ChatGPT for supporting automated qualitative analysis within corpus approaches to discourse studies, signalling issues of repeatability and replicability, ethical challenges surrounding data integrity, and the challenges associated with using non-deterministic technology for empirical linguistic research.

Using network analysis to identify leverage points based on causal loop diagrams leads to false inference

Network analysis is gaining momentum as an accepted practice to identify which factors in causal loop diagrams (CLDs)—mental models that graphically represent causal relationships between a system’s factors—are most likely to shift system-level behaviour, known as leverage points. This application of network analysis, employed to quantitatively identify leverage points without having to use computational modelling approaches that translate CLDs into sets of mathematical equations, has however not been duly reflected upon. We evaluate whether using commonly applied network analysis metrics to identify leverage points is justified, focusing on betweenness- and closeness centrality. First, we assess whether the metrics identify the same leverage points based on CLDs that represent the same system but differ in inferred causal structure—finding that they provide unreliable results. Second, we consider conflicts between assumptions underlying the metrics and CLDs. We recognise six conflicts suggesting that the metrics are not equipped to take key information captured in CLDs into account. In conclusion, using betweenness- and closeness centrality to identify leverage points based on CLDs is at best premature and at worst incorrect—possibly causing erroneous identification of leverage points. This is problematic as, in current practice, the results can inform policy recommendations. Other quantitative or qualitative approaches that better correspond with the system dynamics perspective must be explored.

Fractionation of neural reward processing into independent components by novel decoding principle

How to retrieve latent neurobehavioural processes from complex neurobiological signals is an important yet unresolved challenge. Here, we develop a novel approach, orthogonal-Decoding multi-Cognitive Processes (DeCoP), to reveal underlying latent neurobehavioural processing and show that its performance is superior to traditional non-orthogonal decoding in terms of both false inference and robustness. Processing value and salience information are two fundamental but mutually confounded pathways of reward reinforcement essential for decision making. During reward/punishment anticipation, we applied DeCoP to decode brain-wide responses into spatially overlapping, yet functionally independent, evaluation and readiness processes, which are modulated differentially by meso‑limbic vs nigro-striatal dopamine systems. Using DeCoP, we further demonstrated that most brain regions only encoded abstract information but not the exact input, except for dorsal anterior cingulate cortex and insula. Furthermore, we anticipate our novel analytical principle to be applied generally in decoding multiple latent neurobehavioral processes and thus advance both the design and hypothesis testing for cognitive tasks.

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation.

Performance estimation is a necessary step in the development and validation of Brain-Computer Interface (BCI) systems. Unfortunately, even modern BCI systems are slow, making collecting sufficient data for validation a time-consuming task for end users and experimenters alike. Yet without sufficient data, the random variation in performance can lead to false inferences about how well a BCI is working for a particular user. For example, P300 spellers commonly operate around 1-5 characters per minute. To estimate accuracy with a 5% resolution requires 20 characters (4-20 min). Despite this time investment, the confidence bounds for accuracy from 20 characters can be as much as ±23% depending on observed accuracy. A previously published method, Classifier-Based Latency Estimation (CBLE), was shown to be highly correlated with BCI accuracy. This work presents a protocol for using CBLE to predict a user's P300 speller accuracy from relatively few characters (~3-8) of typing data. The resulting confidence bounds are tighter than those produced by traditional methods. The method can thus be used to estimate BCI performance more quickly and/or more accurately.

Quantifying and addressing uncertainty in the measurement of interdisciplinarity

AbstractA common method for quantifying the interdisciplinarity of a publication is to measure the diversity of the publication’s cited references based on their disciplines. Here we examine the criteria that must be satisfied to develop a meaningful interdisciplinary measure based on citations and discuss the stages where uncertainty or bias may be introduced. In addition, using the Rao-Stirling diversity measure as an exemplar for such citation-based measures, we show how bootstrapping can be used to estimate a confidence interval for interdisciplinarity. Using an academic publication database, this approach is used to develop and assess a reliability measure for interdisciplinarity that extends current methods. Our results highlight issues with citation analysis for measuring interdisciplinarity and offer an approach to improve the confidence in assessing this concept. Specific guidelines for assessing the confidence in the Rao-Stirling diversity measure and subsequently other similar diversity measures are presented, hopefully reducing the likelihood of drawing false inferences about interdisciplinarity in the future.

Defining standards for rankings: An investigation of global university rankings according to the Berlin Principles

AbstractThe role of universities as the engines of knowledge‐based economies drives global internationalisation of higher education. This contributes to a competitive environment where higher education rankings indicate market value. Even though rankings are influential and are used a lot, ranking systems have been heavily critiqued. One of the problems is that there are few if any external checks on how rankings are created. The purpose of the study on which this article reports was to evaluate ranking systems. Within the scope of the study, we have sought to reveal to what extent current ranking systems comply with the Berlin Principles—prepared to create certain rules for rankings, and to ensure that rankings represent quality. A document analysis of publicly available documents online was carried out together with a review of printed and electronic publications on ranking systems. An evaluation form was prepared and used in this study for field experts to fill in. Findings show that ranking systems comply with the Berlin Principles in terms of methodology, transparency, and acceptability at a level that ranges from moderate to low. Overall, rankings do not consider differences in higher education and are not transparent about the processes by which rankings are developed. Rankings should for this reason be interpreted carefully and methodological weaknesses of rankings that can lead to false inferences should be recognised.

Foreign language reduces false memories by increasing memory monitoring.

People have false memories that distort their recollection of past events. Language is an important source of such memories, from providing false inferences to outright misinformation. Here we investigate the impact of using a native or foreign language on bilinguals' susceptibility to false memories. Although language has been argued to impact false memories in multiple ways, our study was inspired by recent work in the decision-making literature, which leads to the novel hypothesis that foreign language encourages people to engage in careful memory monitoring that could reduce false memories. This hypothesis contrasts with a processing load account, which predicts that a foreign language would increase false memories because it is naturally more difficult to process information in a foreign language. We tested these hypotheses using two false memory tasks. Using the DRM task, Experiment 1 found that individuals were more accurate in identifying false memories when using their foreign language compared with their native tongue, consistent with the memory monitoring hypothesis. Using the misinformation task, Experiment 2 found that processing misleading information in one's foreign language eliminated false memories, again supporting the hypothesis that a foreign language increases the use of memory monitoring. These findings support a monitoring hypothesis that has been overlooked in prior studies on bilingualism and false memory, with implications for billions of people who regularly use a foreign language. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Functional principles of genetically encoded fluorescent biosensors for metabolism and their quantitative use.

Genetically encoded fluorescent biosensors provide an attractive means of measuring chemical changes in single cells on fast timescales (milliseconds to seconds). While their most prominent application has been in tracking neural activity and neurotransmitter release, there has been growing interest in developing and deploying new versions of these tools to study brain metabolism. However, the careful use of these tools and the interpretation of the data they provide remain challenging. Many biosensors are subject to interferences that can alter sensor responses within a single cell or between cells, producing ambiguous results. This presents a challenge for quantitation and for our ability to accurately interpret sensor responses. This review describes current methods of sensor quantitation, with a focus on cellular interferences that commonly affect sensor performance, ways to avoid false inferences, and recent advances in sensor optimization to make them more robust.

Econometric Modeling of Input-Driven Output Risk through a Versatile CES Production Function

The conventional functional form of the Constant-Elasticity-of-Substitution (CES) production function is a general production function nesting a number of other forms of production functions. Examples of such functions include Leontief, Cobb–Douglas, and linear production functions. Nevertheless, the conventional form of the CES production specification is still restrictive in multiple aspects. One example is the fact that the marginal effect of increasing input use always has to be to increase the variability of output quantity by the conventional construction of this function. This paper proposes a generalized variant of the CES production function that allows for various input effects on the probability distribution of output. Failure to allow for this possible input–output risk structure is indeed one of the limitations of the conventional form of the CES production function. This limitation may result in false inferences about input-driven output risk. In light of this, the present paper proposes a solution to this problem. First, it is shown that the familiar CES formulation suffers from very restrictive structural assumptions regarding risk considerations, and that such restrictions may lead to biased and inefficient estimates of production quantity and production risk. Following the general theme of Just and Pope’s approach, a CES-based production-function specification that overcomes this shortcoming of the original CES production function is introduced, and a three-stage Nonlinear Least-Squares (NLS) estimation procedure for the estimation of the proposed functional form is presented. To illustrate the proposed approaches in this paper, two empirical applications in irrigation and fertilizer response using the famous Hexem–Heady experimental dataset are provided. Finally, implications for modeling input-driven production risks are discussed.

Impact of Phylogenetic Tree Completeness and Mis-specification of Sampling Fractions on Trait Dependent Diversification Models.

Understanding the origins of diversity and the factors that drive some clades to be more diverse than others are important issues in evolutionary biology. Sophisticated SSE (state-dependent speciation and extinction) models provide insights into the association between diversification rates and the evolution of a trait. The empirical data used in SSE models and other methods is normally imperfect, yet little is known about how this can affect these models. Here, we evaluate the impact of common phylogenetic issues on inferences drawn from SSE models. Using simulated phylogenetic trees and trait information, we fitted SSE models to determine the effects of sampling fraction (phylogenetic tree completeness) and sampling fraction mis-specification on model selection and parameter estimation (speciation, extinction, and transition rates) under two sampling regimes (random and taxonomically biased). As expected, we found that both model selection and parameter estimate accuracies are reduced at lower sampling fractions (i.e., low tree completeness). Furthermore, when sampling of the tree is imbalanced across sub-clades and tree completeness is ≤ 60%, rates of false positives increase and parameter estimates are less accurate, compared to when sampling is random. Thus, when applying SSE methods to empirical datasets, there are increased risks of false inferences of trait dependent diversification when some sub-clades are heavily under-sampled. Mis-specifying the sampling fraction severely affected the accuracy of parameter estimates: parameter values were over-estimated when the sampling fraction was specified as lower than its true value, and under-estimated when the sampling fraction was specified as higher than its true value. Our results suggest that it is better to cautiously under-estimate sampling efforts, as false positives increased when the sampling fraction was over-estimated. We encourage SSE studies where the sampling fraction can be reasonably estimated and provide recommended best practices for SSE modeling. [Trait dependent diversification; SSE models; phylogenetic tree completeness; sampling fraction.].

The Science of Literature Reviews: Searching, Identifying, Selecting, and Synthesising

The ability to conduct an explicit and robust literature review by students, scholars or scientists is critical in producing excellent journal articles, academic theses, academic dissertations or working papers. A literature review is an evaluation of existing research works on a specific academic topic, theme or subject to identify gaps and propose future research agenda. Many postgraduate students in higher education institutions lack the necessary skills and understanding to conduct in-depth literature reviews. This may lead to the presentation of incorrect, false or biased inferences in their theses or dissertations. This study offers scientific knowledge on how literature reviews in different fields of study could be conducted to mitigate against biased inferences such as unscientific analogies and baseless recommendations. The literature review is presented as a process that involves several activities including searching, identifying, reading, summarising, compiling, analysing, interpreting and referencing. We hope this article serves as reference material to improve the academic rigour in the literature review chapters of postgraduate students’ theses or dissertations. This article prompts established scholars to explore more innovative ways through which scientific literature reviews can be conducted to identify gaps (empirical, knowledge, theoretical, methodological, application and population gap) and propose a future research agenda.

Inferring directional interactions in collective dynamics: a critique to intrinsic mutual information

Pairwise interactions are critical to collective dynamics of natural and technological systems. Information theory is the gold standard to study these interactions, but recent work has identified pitfalls in the way information flow is appraised through classical metrics—time-delayed mutual information and transfer entropy. These pitfalls have prompted the introduction of intrinsic mutual information to precisely measure information flow. However, little is known regarding the potential use of intrinsic mutual information in the inference of directional influences to diagnose interactions from time-series of individual units. We explore this possibility within a minimalistic, mathematically tractable leader–follower model, for which we document an excess of false inferences of intrinsic mutual information compared to transfer entropy. This unexpected finding is linked to a fundamental limitation of intrinsic mutual information, which suffers from the same sins of time-delayed mutual information: a thin tail of the null distribution that favors the rejection of the null-hypothesis of independence.

Navigating the seven challenges of taxonomic reference databases in metabarcoding analyses.

Assessment of biodiversity using metabarcoding data, such as from bulk or environmental DNA sampling, is becoming increasingly relevant in ecology, biodiversity sciences and monitoring. Thereby, the taxonomic identification of species from their DNA sequences relies strongly on reference databases that link genetic sequences to taxonomic names. These databases vary in completeness and availability, depending on the taxonomic group studied and the genetic region targeted. The incompleteness of reference databases is an important argument to explain the nondetection by metabarcoding of species supposedly present. However, there exist further and generally overlooked problems with reference databases that can lead to false or inaccurate inferences of taxonomic assignment. Here, we synthesize all possible problems inherent to reference databases. In particular, we identify a complete, mutually nonexclusive list of seven classes of challenges when it comes to selecting, developing and using a reference database for taxonomic assignment. These are: (i) mislabelling, (ii) sequencing errors, (iii) sequence conflict, (iv) taxonomic conflict, (v) low taxonomic resolution, (vi) missing taxa and (vii) missing intraspecific variants. For each problem identified, we provide a description of possible consequences on the taxonomic assignment process. We illustrate the respective problem with examples taken from the literature or obtained by quantitative analyses of public databases, such as GenBank or BOLD. Finally, we discuss possible solutions to the identified problems and how to navigate them. Only by raising users' awareness of the limitations of metabarcoding data and DNA reference databases will adequate interpretations of these data be achieved.

CEDA: integrating gene expression data with CRISPR-pooled screen data identifies essential genes with higher expression.

Clustered regularly interspaced short palindromic repeats (CRISPR)-based genetic perturbation screen is a powerful tool to probe gene function. However, experimental noises, especially for the lowly expressed genes, need to be accounted for to maintain proper control of false positive rate. We develop a statistical method, named CRISPR screen with Expression Data Analysis (CEDA), to integrate gene expression profiles and CRISPR screen data for identifying essential genes. CEDA stratifies genes based on expression level and adopts a three-component mixture model for the log-fold change of single-guide RNAs (sgRNAs). Empirical Bayesian prior and expectation-maximization algorithm are used for parameter estimation and false discovery rate inference. Taking advantage of gene expression data, CEDA identifies essential genes with higher expression. Compared to existing methods, CEDA shows comparable reliability but higher sensitivity in detecting essential genes with moderate sgRNA fold change. Therefore, using the same CRISPR data, CEDA generates an additional hit gene list. Supplementary data are available at Bioinformatics online.

THE EFFECT OF CULTURE-SPECIFIC DIFFERENCES IN URBAN STREETSCAPES ON THE INFERENCE ACCURACY OF DEEP LEARNING MODELS

Abstract. Owing to the increasing focus on places in urban planning and design, methods to evaluate the quality and value of urban places is crucially needed. Many studies use deep learning models to identify the proportion and composition of landscape elements in images for evaluation. The accuracy of semantic segmentation achieved with such models is often validated using Cityscapes, a street-level image dataset taken from German cities. However, few studies have quantitatively revealed the inference accuracy decrease caused by culture-specific characteristics of streetscapes.In this study, we calculated by-class intersection over union (IoU) and newly-defined indices of false inferences to demonstrate how and to what extent deep learning models can infer each landscape element falsely when applied to Japanese street-level images. Our analysis revealed that certain landscape elements are more difficult to infer correctly based on specific causes, such as their appearances in images and unique characteristics of the fixed physical configuration of Japanese streets. By applying the false inference categorization framework presented in this study, researchers can adjust their approaches considering two aspects: a decrease in inference accuracies of deep learning models and the impact of culture-specific characteristics of streetscapes on people's perception and valuation of urban places. Based on the results and analyses, a future research direction is to develop and implement more accurate image recognition models considering culture-specific characteristics to understand people's perceptions of urban spaces and assess the value of urban places by using the big data including street-level images.

Temporal dynamics of emotional processing: Parsing trial-wise variance of the late positive potential using Generalizability Theory.

The reliability of individual trial event-related potential (ERP) components extracted from electroencephalogram has been consistently questioned since ERP research began. This ambivalence is based on misunderstood assumptions stemming from Cronbach and Classical Test Theory. Contemporary methods allow for the reliability of individual ERP trials to be estimated and for analyses of these trial-level ERP components to be meaningfully parsed. We illustrate the use of Generalizability Theory procedures in estimating the reliability of trial-level ERPs using the late positive potential (LPP), a neural measure of motivated attention toward emotionally evocative stimuli. Individuals (N=88) completed a passive viewing task while continuous EEG was recorded. Variability in trial-level LPP responses was decomposed into facets corresponding to individual differences, chronological trial within block, stimulus type, their two-way interactions, and specific stimuli. We estimated various reliability coefficients and found that both overall and category-specific person-level LPP estimates have good-to-excellent reliability, while the reliability of within-person differences (i.e., change) between arousal categories was fair for the early LPP. These results were generally consistent across time windows, but were highest early in the LPP time course. We argue that investigating reliability using trial-level data allows researchers to pursue hypotheses focused on neurophysiological dynamics that unfold over the course of an experiment and not risk false inferences (i.e., ecological fallacy) when using person-level aggregates to deduce such processes. Moreover, such analyses provide information that allows researchers to optimize their protocols by potentially reducing the number of individual trials, burden on participants, and cost, while retaining sufficient reliability.