Magnitude Of Bias Research Articles

Working memory representations modulate the magnitude of the similarity-induced memory bias

Working memory representations modulate the magnitude of the similarity-induced memory bias

Base composition, codon usage and patterns of gene sequence evolution in butterflies.

Coding sequence evolution is influenced by both natural selection and neutral evolutionary forces. In many species, the effects of mutation bias, codon usage and GC-biased gene conversion (gBGC) on gene sequence evolution have not been detailed. Quantification of how these forces shape substitution patterns is therefore necessary to understand the strength and direction of natural selection. Here, we used comparative genomics to investigate the association between base composition and codon usage bias on gene sequence evolution in butterflies and moths (Lepidoptera), including an in-depth analysis of underlying patterns and processes in one species, Leptidea sinapis. The data revealed significant G/C to A/T substitution bias at third codon position with some variation in the strength among different butterfly lineages. However, the substitution bias was lower than expected from previously estimated mutation rate ratios, partly due to the influence of gBGC. We found that A/T-ending codons were overrepresented in most species, but there was a positive association between the magnitude of codon usage bias and GC-content in third codon positions. In addition, the tRNA-gene population in L. sinapis showed higher GC-content at third codon positions compared to coding sequences in general and less overrepresentation of A/T-ending codons. There was an inverse relationship between synonymous substitutions and codon usage bias indicating selection on synonymous sites. We conclude that the evolutionary rate in Lepidoptera is affected by a complex interaction between underlying G/C -> A/T mutation bias and partly counteracting fixation biases, predominantly conferred by overall purifying selection, gBGC and selection on codon usage.

Modeling venous bias in resting state functional MRI metrics.

Resting-state (rs) functional magnetic resonance imaging (fMRI) is used to detect low-frequency fluctuations in the blood oxygen-level dependent (BOLD) signal across brain regions. Correlations between temporal BOLD signal fluctuations are commonly used to infer functional connectivity. However, because BOLD is based on the dilution of deoxyhemoglobin, it is sensitive to veins of all sizes, and its amplitude is biased by draining veins. These biases affect local BOLD signal location and amplitude, and may also influence BOLD-derived connectivity measures, but the magnitude of this venous bias and its relation to vein size and proximity is unknown. Here, veins were identified using high-resolution quantitative susceptibility maps and utilized in a biophysical model to investigate systematic venous biases on common local rsfMRI-derived measures. Specifically, we studied the impact of vein diameter and distance to veins on the amplitude of low-frequency fluctuations (ALFF), fractional ALFF (fALFF), Hurst exponent (HE), regional homogeneity (ReHo), and eigenvector centrality values in the grey matter. Values were higher across all distances in smaller veins, and decreased with increasing vein diameter. Additionally, rsfMRI values associated with larger veins decrease with increasing distance from the veins. ALFF and ReHo were the most biased by veins, while HE and fALFF exhibited the smallest bias. Across all metrics, the amplitude of the bias was limited in voxel-wise data, confirming that venous structure is not the dominant source of contrast in these rsfMRI metrics. Finally, the models presented can be used to correct this venous bias in rsfMRI metrics.

Key concepts in clinical epidemiology: collider-conditioning bias

Collider-conditioning bias is a common concern in clinical epidemiology and can be difficult to recognize. Collider-conditioning bias is defined as the alteration in the association between an exposure and an outcome under study when conditioning on (via restriction, stratification or regression adjustment) a third factor influenced by both exposure and outcome. Collider-conditioning bias may also be induced by conditioning on a third factor influenced by causes of both the exposure and outcome of primary interest. The shared consequence of the exposure and outcome (or their causes) is referred to as a collider. Collider bias can be induced by in-selection (e.g., recruitment) or out-selection (e.g., loss-to-follow-up, mortality) processes, as well as missing data or analytic choices about which variables are included as covariates. Collider-conditioning bias is especially relevant in clinical epidemiology because it is plausible whenever examining determinants of disease progression or prognosis if the exposure under study is also a cause of the initial occurrence of the disease. Collider-conditioning bias can lead to incorrect inferences about the study sample, the source population, and external populations. Drawing directed acyclic graphs (DAGs) can aid in identification of colliders and quantitative bias analysis may be useful to estimate the magnitude of collider-conditioning bias. Many notorious examples of selection bias are attributable to collider-conditioning. Identifying the potential for collider-conditioning can help researchers avoid or correct for the bias.

The Media's Role in Fostering Knowledge of Financial Management: A Case of Pakistan

This study investigates how behavioural biases affect how individual investors make investing decisions. Drawing upon the discipline of behavioral finance, which integrates psychological insights into financial decision-making, we investigate the presence and effects of various biases on investment behavior. The study aims to contribute to understand the factors thoroughly that shape individuals' investment choices and their subsequent financial outcomes. Using a mixed-methods approach, including surveys and interviews, we explore the prevalence and magnitude of biases for instance loss aversion, overconfidence, and framing effects among individual investors. By analyzing real-world investment decisions, we assess the influence of these biases on portfolio composition, trading frequency, and performance outcomes. According to the preliminary findings, behavioral biases play a significant role in shaping investment decisions. Overconfident investors prefer to trade more often, leading to higher transaction costs and lower returns. Loss aversion biases lead to suboptimal portfolio allocation, as investors disproportionately allocate funds to low-risk assets. Additionally, framing effects impact decision-making by altering risk perceptions and preferences. The study underscores the importance of recognizing and mitigating behavioral biases in investment decision-making. By raising awareness and providing insights into the specific biases influencing individual investors, this research offers valuable implications for financial education, regulatory policies, and investment advisory services. Ultimately, understanding the role of behavioral biases can contribute to more informed and rational investment decision-making, leading to improved financial outcomes for individual investors.

Suppression of crosstalk in row–column actuator array using regulation of ferroelectric polarization

This article presents a method for suppressing electrical crosstalk in a ferroelectric row-column (RC) actuator array. In high-density two-dimensional (2D) arrays, the RC addressing scheme has advantages over the conventional method of fully addressing the array, primarily due to its requirement for fewer interconnections to drive the elements. However, electrical crosstalk is an undesirable side effect that restricts the practical use of RC arrays for sensing and actuation. To mitigate this crosstalk between elements and allow activation of the target element, an addressing method using orthogonal biasing is investigated for the RC array. Due to the voltage-dependent polarization state of the ferroelectric film, each element exhibits bias-sensitive activity. Applying a coercive voltage bias to the electrically coupled elements via row electrodes to minimize the net polarization and piezoelectric activity reduces electrical crosstalk, leaving only acoustic crosstalk in the array. The method using coercive voltage bias reduces crosstalk between the elements by 6.25 dB compared to the conventional method. Furthermore, applying a negative bias of 7 V to the target element enhances its activity and increases its dynamic displacement by 33%, thus alleviating the crosstalk by 7.25 dB. The on-off switching characteristics of the biasing method are examined to investigate the feasibility of adopting such biasing in area scanning techniques for potential applications, such as ultrasonic imaging. By adjusting the magnitude of the DC bias, on-off switching operation is demonstrated at 5 kHz while achieving a 3 dB on-off ratio.

Evaluating the OECD–FAO and USDA agricultural baseline projections

Abstract The Organization for Economic Cooperation and Development (OECD) in collaboration with the Food and Agricultural Organization (FAO), and the US Department of Agriculture (USDA) produce long-term agricultural projections for key indicators of agricultural commodities. These baseline projections play a key role in agricultural decision-making and policies. Our study compares the OECD–FAO and USDA baseline projections for harvested acres, production, and yield in the European Union, the US, and China for rice and wheat. We evaluate the accuracy by examining the projection error, including the magnitude of bias and variance as well as the significance of bias. We also compare the OECD–FAO and USDA projections’ predictive abilities using the modified Diebold–Mariano test. We find that the accuracy of the projections decreases as the projection horizon increases. Both agencies underestimate wheat projections in the European Union and China. USDA projections generally outperform OECD–FAO projections for most rice variables, while the reverse is found for longer-horizon EU wheat projections. This assessment may enable agencies to make more accurate projections and enable market participants to better understand these projections.

Just as I expected? Hindsight bias for the outcome of a national referendum is moderated by outcome valence and surprise

AbstractWhen people learn that an event has occurred, they often report that they expected it all along. The present study explored this “hindsight bias” for the outcome of a national referendum on abortion. Participants rated the probability of the referendum passing one week before polling day, and then recalled their original estimates one week and one year post‐referendum. The magnitude and direction of bias was predicted by voting choice: People on the losing side exhibited a classic hindsight bias, and remembered believing the outcome to be roughly 16% more likely than they originally reported. In contrast, people on the winning side exhibited a weak reversal of the typical bias. Hindsight bias was moderated by feelings of surprise in both groups. This study suggests that individuals on the losing side of political campaigns may be especially likely to misremember their prior expectations if the outcome is disappointing but not unexpected.

Error correction for IMERG precipitation estimates based on climatological adjustment combining the dry–wet season division and weight allocation

Error correction to obtain more accurate gridded precipitation estimates enhances the application prospects of the integrated multi-satellite retrievals for global precipitation measurement (IMERG). This study developed a climatological adjustment approach combining dry–wet season division and weight allocation (i.e., the DW approach) to achieve IMERG estimate correction without the need for contemporaneous gauge data. By learning the characteristics of an 18-year bias series from June 1, 2000 to December 31, 2017, through empirical probability calculation (the probability distribution function of every bias value in the dry and wet seasons), the bias field can be obtained without mathematical distribution assumptions for the biases. After recognizing the bias field at each of the 687 gauges in mainland China, the error adjustment can be tailored for every gauge to lower the current bias magnitude. The bias adjustment can be obtained as the product of two components, namely, the adjustment amount (AA) and the scaling factor (SF). The SF was discussed under different value intervals of the IMERG estimates to decide whether the original IMERG estimates should subtract or add the AA to generate the adjusted IMERG estimates. The quantile mapping (QM) approach was designed to be the standard method to examine the DW approach proposed. During the validation period from January 1, 2018, to December 31, 2019, the capability of two error adjustment approaches to the value reduction of evaluation indicators was inspected at gauge, climatic, and topographic region scales. The main conclusions follow: (1) The DW approach performed better than the QM approach regarding the results of all of the evaluation indicators at the gauge scale. (2) The DW approach maintained higher efficacy than the QM approach at the gauge, climatic, and topographic region scales. (3) The proportion of the DW approach as the better-performing adjustment approach was higher than the QM approach both at the gauge scale and in most subregions. (4) In the areas where the IMERG products greatly overestimate the precipitation, the DW approach will be very effective, whereas the QM approach will be very effective in the areas where the precipitation is significantly underestimated by the IMERG data.

Evaluation of a device-agnostic approach to predict sleep from raw accelerometry data collected by Apple Watch Series 7, Garmin Vivoactive 4, and ActiGraph GT9X Link in children with sleep disruptions

Goal and aimsEvaluate the performance of a sleep scoring algorithm applied to raw accelerometry data collected from research-grade and consumer wearable actigraphy devices against polysomnography. Focus method/technologyAutomatic sleep/wake classification using the Sadeh algorithm applied to raw accelerometry data from ActiGraph GT9X Link, Apple Watch Series 7, and Garmin Vivoactive 4. Reference method/technologyStandard manual PSG sleep scoring. SampleFifty children with disrupted sleep (M = 8.5 years, range = 5-12 years, 42% Black, 64% male). DesignParticipants underwent to single night lab polysomnography while wearing ActiGraph, Apple, and Garmin devices. Core analyticsDiscrepancy and epoch-by-epoch analyses for sleep/wake classification (devices vs. polysomnography). Additional analytics and exploratory analysesEquivalence testing for sleep/wake classification (research-grade actigraphy vs. commercial devices). Core outcomesCompared to polysomnography, accuracy, sensitivity, and specificity were 85.5, 87.4, and 76.8, respectively, for Actigraph; 83.7, 85.2, and 75.8, respectively, for Garmin; and 84.6, 86.2, and 77.2, respectively, for Apple. The magnitude and trend of bias for total sleep time, sleep efficiency, sleep onset latency, and wake after sleep were similar between the research and consumer wearable devices. Important additional outcomesEquivalence testing indicated that total sleep time and sleep efficiency estimates from the research and consumer wearable devices were statistically significantly equivalent. Core conclusionThis study demonstrates that raw acceleration data from consumer wearable devices has the potential to be harnessed to predict sleep in children. While further work is needed, this strategy could overcome current limitations related to proprietary algorithms for predicting sleep in consumer wearable devices.

Exchange-Biased Quantum Anomalous Hall Effect.

The quantum anomalous Hall (QAH) effect is characterized by a dissipationless chiral edge state with a quantized Hall resistance at zero magnetic field. Manipulating the QAH state is of great importance in both the understanding of topological quantum physics and the implementation of dissipationless electronics. Here, the QAH effect is realized in the magnetic topological insulator Cr-doped (Bi,Sb)2 Te3 (CBST) grown on an uncompensated antiferromagnetic insulator Al-doped Cr2 O3 . Through polarized neutron reflectometry (PNR), a strong exchange coupling is found between CBST and Al-Cr2 O3 surface spins fixing interfacial magnetic moments perpendicular to the film plane. The interfacial coupling results in an exchange-biased QAH effect. This study further demonstrates that the magnitude and sign of the exchange bias can be effectively controlled using a field training process to set the magnetization of the Al-Cr2 O3 layer. It demonstrates the use of the exchange bias effect to effectively manipulate the QAH state, opening new possibilities in QAH-based spintronics.

Multi-criteria evaluation of satellite-based precipitation estimates over agro-climatic zones of India

Satellite-based precipitation estimates (SPEs) available at a high spatio-temporal resolution with near-global coverage are substitutes for ground-based datasets. In this study, the quality of popular SPEs was evaluated at daily time scale over agro-climatic (ACL) zones of India. The SPEs were analyzed in two different groups, i.e., (i) satellite-only precipitation estimates (SOPEs) including 5 products namely, CMORPH, PERSIANN, SM2RAIN-ASCAT, SM2RAIN-CCI, and SM2RAIN-GPM, and (ii) gauge-corrected satellite precipitation estimates (GCSPEs) including 4 products namely, CHIRPS, GSMaP, IMERG and MSWEP. The analysis was carried out by quantifying the qualitative and quantitative performance metrics as well as by decomposing the errors into random and systematic components. In general, SPEs underestimated the high rainfall events and overestimated the low rainfall events. The performance of SPEs was observed to be poor over the western Himalayan region. The efficiency and agreement of Global Precipitation Measurement (GPM) based products (including SM2RAIN-GPM, GSMaP and IMERG) with observed precipitation were better. The superiority of SM2RAIN-GPM, GSMap and IMERG products has also been supported by the lower magnitude of errors and biases. The error decomposition reveals high random errors in most of the SPEs; however, SM2RAIN-based products have low random error. It was also observed that the total bias is highly underestimated due to opposite-natured bias components. Further, the association of vegetation and topographic complexity with the performance of precipitation products was investigated, and a negative/positive association of performance metrics of SPEs with topographic/vegetation characteristics was observed. A multi-criteria decision-making (MCDM) approach was then employed to rank the datasets based on multiple performance indicators. MCDM approach reveals the superiority of SM2RAIN-GPM product amongst SOPEs and GSMaP (followed by IMERG) amongst GCSPEs over a majority of the regions of India.

Environmental dynamics shape perceptual decision bias.

To interpret the sensory environment, the brain combines ambiguous sensory measurements with knowledge that reflects context-specific prior experience. But environmental contexts can change abruptly and unpredictably, resulting in uncertainty about the current context. Here we address two questions: how should context-specific prior knowledge optimally guide the interpretation of sensory stimuli in changing environments, and do human decision-making strategies resemble this optimum? We probe these questions with a task in which subjects report the orientation of ambiguous visual stimuli that were drawn from three dynamically switching distributions, representing different environmental contexts. We derive predictions for an ideal Bayesian observer that leverages knowledge about the statistical structure of the task to maximize decision accuracy, including knowledge about the dynamics of the environment. We show that its decisions are biased by the dynamically changing task context. The magnitude of this decision bias depends on the observer's continually evolving belief about the current context. The model therefore not only predicts that decision bias will grow as the context is indicated more reliably, but also as the stability of the environment increases, and as the number of trials since the last context switch grows. Analysis of human choice data validates all three predictions, suggesting that the brain leverages knowledge of the statistical structure of environmental change when interpreting ambiguous sensory signals.

What determines hindsight bias in written work? One field and three experimental studies in the context of Wikipedia.

Hindsight bias not only occurs in individual perception but in written work (e.g., Wikipedia articles) as well. To avoid the possibility that biased written representations of events distort the views of broad audiences, one needs to understand the factors that determine hindsight bias in written work. Therefore, we tested the effect of three potential determinants: the extent to which an event evokes sense-making motivation, the availability of verifiable causal information regarding the event, and the provision of content policies. We conducted one field study examining real Wikipedia articles (N = 40) and three preregistered experimental studies in which participants wrote or edited articles based on different materials (total N = 720). In each experiment, we systematically varied one determinant. Findings provide further-and even more general-support that Wikipedia articles about various events contain hindsight bias. The magnitude of hindsight bias in written work was contingent on the sense-making motivation and the availability of causal information. We did not find support for the effect of content policies. Findings are in line with causal model theory and suggest that some types and topics of written work might be particularly biased by hindsight (e.g., coverage of disasters, research reports, written expert opinions). (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Roles of magnetic coupling and spin-orbit torque in the electrical manipulation of exchange bias in a Pt/Co/IrMn heterostructure

Heavy-metal/ferromagnet/antiferromagnet heterostructures have attracted significant attention in manipulating antiferromagnetic ordering electrically. We report electric control of the exchange bias field along both the out-of-plane and in-plane directions in Pt/Co/IrMn with assist magnetic fields. Experiments with different directions and amplitudes of the assist magnetic field show the role of magnetic coupling between ferromagnet and antiferromagnet spins. Furthermore, we find that the magnitude and direction of the exchange bias can be manipulated by the magnitude of the current pulse. The observed results can be explained based on a numerical macrospin model; the antiferromagnetic spins are excited and precessed from their ground state via the spin-orbit torque-driven oscillation and are reoriented, being mediated by magnetic coupling with the ferromagnet. Our results provide an approach to achieve electrical omnidirectional exchange bias manipulation and an understanding of the spin dynamics at the antiferromagnet-ferromagnet interface.

Misclassification Bias in the Assessment of Gene-by-Environment Interactions.

Misclassification bias is a common concern in epidemiologic studies. Despite strong bias on main effects, gene-environment interactions have been shown to be biased towards the null under gene-environment independence. In the context of a recent article examining the interaction between nerve agent exposure and paraoxonase-1 gene on Gulf War Illness, we aimed to assess the impact of recall bias-a common misclassfication bias-on the identification of gene-environment interactions when the independence assumption is violated. We derive equations to quantify the bias of the interaction, and numerically illustrate these results by simulating a case-control study of 1000 cases and 1000 controls. Simulation input parameters included exposure prevalence, strength of gene-environment dependence, strength of the main effect, exposure specificity among cases, and strength of the gene-environment interaction. We show that, even if gene-environment independence is violated, we can bound possible gene-environment interactions by knowing the strength and direction of the gene-environment dependence ( ) and the observed gene-environment interaction ( )-thus often still allowing for the identification of such interactions. Depending on whether is larger or smaller than the inverse of , is a lower (if ) or upper (if ) bound for the true interaction. In addition, the bias magnitude is somewhat predictable by examining other characteristics such as exposure prevalence, the strength of the exposure main effect, and directions of the recall bias and gene-environment dependence. Even if gene-environment dependence exists, we may still be able to identify gene-environment interactions even when misclassification bias is present.

Shifts in mutation spectra enhance access to beneficial mutations

Biased mutation spectra are pervasive, with wide variation in the magnitude of mutational biases that influence genome evolution and adaptation. How do such diverse biases evolve? Our experiments show that changing the mutation spectrum allows populations to sample previously undersampled mutational space, including beneficial mutations. The resulting shift in the distribution of fitness effects is advantageous: Beneficial mutation supply and beneficial pleiotropy both increase, while deleterious load reduces. More broadly, simulations indicate that reducing or reversing the direction of a long-term bias is always selectively favored. Such changes in mutation bias can occur easily via altered function of DNA repair genes. A phylogenetic analysis shows that these genes are repeatedly gained and lost in bacterial lineages, leading to frequent bias shifts in opposite directions. Thus, shifts in mutation spectra may evolve under selection and can directly alter the outcome of adaptive evolution by facilitating access to beneficial mutations.

Sensitivities of the Asian Summer Monsoon Simulations to Physical Parameters for the Perturbed Parameter Ensemble of HadGEM3‐GC3.05

AbstractThe simulation skill of the perturbed parameter ensemble (PPE) of HadGEM3‐GC3.05 on the mean climate pattern of the Asian summer monsoon (ASM) is evaluated in this study. The sensitivities of the model bias to the perturbed parameters are investigated based on metrics. The results show that the PPE mean (PPE‐20M) could effectively capture the general ASM precipitation and wind patterns, with a correlation coefficient of 0.78. PPE‐20M mainly shows positive precipitation biases over the tropical western Pacific, southern slope of the Tibetan Plateau, Indochina Peninsula, and South China and negative precipitation biases over the Indian continent and Bay of Bengal. The magnitude of the precipitation biases is more sensitive than its pattern to the variation of the perturbed parameters. Four parameters (ent_fac_dp, qlmin, ps_cloud‐ph, and psm) are found to be crucial for simulating the ASM precipitation intensity, and their combined effects are related to the simulated precipitation biases.

Your Vestibular Thresholds May Be Lower Than You Think: Cognitive Biases in Vestibular Psychophysics.

Recently, there has been a surge of interest in measuring vestibular perceptual thresholds, which quantify the smallest motion that a subject can reliably perceive, to study physiology and pathophysiology. These thresholds are sensitive to age, pathology, and postural performance. Threshold tasks require decisions to be made in the presence of uncertainty. Since humans often rely on past information when making decisions in the presence of uncertainty, we hypothesized that (a) perceptual responses are affected by their preceding trial; (b) perceptual responses tend to be biased opposite of the "preceding response" because of cognitive biases but are not biased by the "preceding stimulus"; and (c) when fits do not account for this cognitive bias, thresholds are overestimated. To our knowledge, these hypotheses are unaddressed in vestibular and direction-recognition tasks. Results in normal subjects supported each hypothesis. Subjects tended to respond opposite of their preceding response (not the preceding stimulus), indicating a cognitive bias, and this caused an overestimation of thresholds. Using an enhanced model (MATLAB code provided) that considered these effects, average thresholds were lower (5.5% for yaw, 7.1% for interaural). Since the results indicate that the magnitude of cognitive bias varies across subjects, this enhanced model can reduce measurement variability and potentially improve the efficiency of data collection.

Feature-based attention warps the perception of visual features

Selective attention improves sensory processing of relevant information but can also impact the quality of perception. For example, attention increases visual discrimination performance and at the same time boosts apparent stimulus contrast of attended relative to unattended stimuli. Can attention also lead to perceptual distortions of visual representations? Optimal tuning accounts of attention suggest that processing is biased towards “off-tuned” features to maximize the signal-to-noise ratio in favor of the target, especially when targets and distractors are confusable. Here, we tested whether such tuning gives rise to phenomenological changes of visual features. We instructed participants to select a color among other colors in a visual search display and subsequently asked them to judge the appearance of the target color in a 2-alternative forced choice task. Participants consistently judged the target color to appear more dissimilar from the distractor color in feature space. Critically, the magnitude of these perceptual biases varied systematically with the similarity between target and distractor colors during search, indicating that attentional tuning quickly adapts to current task demands. In control experiments we rule out possible non-attentional explanations such as color contrast or memory effects. Overall, our results demonstrate that selective attention warps the representational geometry of color space, resulting in profound perceptual changes across large swaths of feature space. Broadly, these results indicate that efficient attentional selection can come at a perceptual cost by distorting our sensory experience.