Hierarchical Bayesian Statistical Model Research Articles

A critical review and meta-analysis of xenon-on-carbon sputter yield data

A systematic review and meta-analysis of sputter yield data for xenon ions normally incident on graphite at energies below 2000 eV was undertaken to identify systematic errors, determine the best model parameter values to represent yield as a function of energy, quantify uncertainty, and determine if the data support differences in yields for different types of graphite. A critical examination of the 11 published data sets for high density graphite, pyrolytic graphite, and amorphous carbon showed that, in general, they were carefully controlled to minimize errors. The most significant quantifiable systematic errors were those caused by the neglect of doubly charged ions, chemical erosion, and the impact of secondary electron emission on ion flux measurements. The effects of gas uptake and outgassing on mass loss measurements and unrepresentative surface textures may have biased other experiments, but these effects could not be quantified. The semi-empirical Eckstein model for yield as a function of energy was fit to data for the three graphite types using a hierarchical Bayesian statistical model, producing recommended fit parameters and probability distributions representing uncertainty in yields. The results showed that differences in yield for high density graphite and pyrolytic graphite were not statistically significant. Apparent differences in yield for amorphous carbon disappeared when the single data set available for energies below 150 eV was corrected for reasonable values of double ion content. Recommended procedures to avoid systematic errors and additional experiments and modeling to fill in gaps in our understanding are included.

Data-driven optimization and statistical modeling to improve meter reading for utility companies

Utility companies collect usage data from meters on a regular basis. The usage data are collected automatically using radio-frequency identification (RFID) technology. Each meter transmits signals from an RFID tag that are read by a vehicle-mounted reading device within a specified distance. Routing the vehicles can be modeled by a close-enough vehicle routing problem on a street network. In practice, there is uncertainty while reading meters. The signal transmitted by an RFID tag is discontinuous, and the range that each meter can be read is different and stochastic due to weather conditions, surrounding obstacles, interference, and decreasing battery life of the RFID tags. These factors can lead to meters not being read. A vehicle has to be sent at a later time to read the missed meters, and this leads to increased costs for a utility company due to additional operational costs and overtime payments to drivers. Our aim is to address the uncertainty issues of the RFID technology by generating routes that are both cost-effective and robust (we seek to minimize the number of missed reads). We use data analytics, optimization, and Bayesian statistical models to address the uncertainty. Simulation experiments using real data show that the hierarchical Bayesian statistical model gives better results compared to other Bayesian statistical models. Utility companies can potentially integrate the results from the hierarchical Bayesian statistical model into their route generating software as a decision-support tool to produce routes that are more cost-effective and robust than the routes that they currently generate.

Spatial Weighted Analysis of Malnutrition Among Children in Nigeria: A Bayesian Approach

Research on malnutrition in children has undergone in-depth study across different disciplines ranging from health demography to statistics. However, the effect of carbon(IV) oxide ( $$\text{CO}_2$$ ) pollution on malnutrition has not been adequately considered. While the number of industries emitting $$\text{CO}_2$$ keeps increasing in Nigeria, the fight against malnutrition has not been successful. The $$\text{CO}_2$$ generated by industrial activities is known to contribute to the decline in the nutrition content of crops and consequently increases the risk of malnutrition in the population. In order to account for the spatial impact, and identify susceptible areas in Nigeria, this study weighted the spatial variation using the average volume of $$\text{CO}_2$$ emitted from 2001 to 2018. The Conditional Auto-Regressive (CAR) spatial model was adopted to model the spatial component in a Bayesian Hierarchical statistical model. Data were acquired from the Nigeria Demographic and Health Survey, and Mongabay databases. Adjusting for demographic and socioeconomic variables, the result shows that regions with a higher concentration of $$\text{CO}_2$$ were at higher risk of malnutrition, compared to the regions with a lower concentration. However, the Northern region with a lower concentration of $$\text{CO}_2$$ was consistently at higher risk of malnutrition than other regions.

Estimating Serotype-specific Efficacy of Pneumococcal Conjugate Vaccines Using Hierarchical Models.

Pneumococcal conjugate vaccines target 10 or 13 specific serotypes. To evaluate the overall efficacy of these products, the vaccine-targeted serotypes are typically aggregated into a single group. However, it is often desirable to evaluate variations in effects for different serotypes. These serotype-specific estimates are often based on small counts, resulting in a high degree of uncertainty (i.e., large standard errors and wide confidence intervals). An alternative is to use a hierarchical Bayesian statistical model, which estimates overall effectiveness while simultaneously providing estimates of serotype-specific vaccine effects. These shrunken serotype-specific estimators often have smaller mean squared errors (MSEs) than unbiased versions due to a large decrease in posterior uncertainty. We reanalyzed published data from a randomized controlled trial on the efficacy of 13-valent pneumococcal conjugate vaccine (PCV13) against community-acquired pneumonia caused by vaccine-targeted serotype using a hierarchical model. This model provides a potential framework for obtaining estimates of serotype-specific vaccine effects with reduced MSEs.

A data-based predictive model for spatiotemporal variability in stream water quality

Abstract. Our current capacity to model stream water quality is limited – particularly at large spatial scales across multiple catchments. To address this, we developed a Bayesian hierarchical statistical model to simulate the spatiotemporal variability in stream water quality across the state of Victoria, Australia. The model was developed using monthly water quality monitoring data over 21 years and across 102 catchments (which span over 130 000 km2). The modeling focused on six key water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate–nitrite (NOx) and electrical conductivity (EC). The model structure was informed by knowledge of the key factors driving water quality variation, which were identified in two preceding studies using the same dataset. Apart from FRP, which is hardly explained (19.9 %), the model explains 38.2 % (NOx) to 88.6 % (EC) of the total spatiotemporal variability in water quality. Across constituents, the model generally captures over half of the observed spatial variability; the temporal variability remains largely unexplained across all catchments, although long-term trends are well captured. The model is best used to predict proportional changes in water quality on a Box–Cox-transformed scale, but it can have substantial bias if used to predict absolute values for high concentrations. This model can assist catchment management by (1) identifying hot spots and hot moments for waterway pollution; (2) predicting the effects of catchment changes on water quality, e.g., urbanization or forestation; and (3) identifying and explaining major water quality trends and changes. Further model improvements should focus on the following: (1) alternative statistical model structures to improve fitting for truncated data (for constituents where a large amount of data fall below the detection limit); and (2) better representation of nonconservative constituents (e.g., FRP) by accounting for important biogeochemical processes.

Towards Bayesian hierarchical inference of equilibrium climate sensitivity from a combination of CMIP5 climate models and observational data

Despite decades of research, large multi-model uncertainty remains about the Earth’s equilibrium climate sensitivity to carbon dioxide forcing as inferred from state-of-the-art Earth system models (ESMs). Statistical treatments of multi-model uncertainties are often limited to simple ESM averaging approaches. Sometimes models are weighted by how well they reproduce historical climate observations. Here, we propose a novel approach to multi-model combination and uncertainty quantification. Rather than averaging a discrete set of models, our approach samples from a continuous distribution over a reduced space of simple model parameters. We fit the free parameters of a reduced-order climate model to the output of each member of the multi-model ensemble. The reduced-order parameter estimates are then combined using a hierarchical Bayesian statistical model. The result is a multi-model distribution of reduced-model parameters, including climate sensitivity. In effect, the multi-model uncertainty problem within an ensemble of ESMs is converted to a parametric uncertainty problem within a reduced model. The multi-model distribution can then be updated with observational data, combining two independent lines of evidence. We apply this approach to 24 model simulations of global surface temperature and net top-of-atmosphere radiation response to abrupt quadrupling of carbon dioxide, and four historical temperature data sets. Our reduced order model is a 2-layer energy balance model. We present probability distributions of climate sensitivity based on (1) the multi-model ensemble alone and (2) the multi-model ensemble and observations.

The Type Ia Supernova Color–Magnitude Relation and Host Galaxy Dust: A Simple Hierarchical Bayesian Model

Abstract Conventional Type Ia supernova (SN Ia) cosmology analyses currently use a simplistic linear regression of magnitude versus color and light curve shape, which does not model intrinsic SN Ia variations and host galaxy dust as physically distinct effects, resulting in low color–magnitude slopes. We construct a probabilistic generative model for the dusty distribution of extinguished absolute magnitudes and apparent colors as the convolution of an intrinsic SN Ia color–magnitude distribution and a host galaxy dust reddening–extinction distribution. If the intrinsic color–magnitude (M B versus B − V) slope differs from the host galaxy dust law R B , this convolution results in a specific curve of mean extinguished absolute magnitude versus apparent color. The derivative of this curve smoothly transitions from in the blue tail to R B in the red tail of the apparent color distribution. The conventional linear fit approximates this effective curve near the average apparent color, resulting in an apparent slope between and R B . We incorporate these effects into a hierarchical Bayesian statistical model for SN Ia light curve measurements, and analyze a data set of SALT2 optical light curve fits of 248 nearby SNe Ia at . The conventional linear fit gives . Our model finds and a distinct dust law of , consistent with the average for Milky Way dust, while correcting a systematic distance bias of ∼0.10 mag in the tails of the apparent color distribution. Finally, we extend our model to examine the SN Ia luminosity–host mass dependence in terms of intrinsic and dust components.

Inference of Environmental Factor-Microbe and Microbe-Microbe Associations from Metagenomic Data Using a Hierarchical Bayesian Statistical Model

The inference of associations between environmental factors and microbes and among microbes is critical to interpreting metagenomic data, but compositional bias, indirect associations resulting from common factors, and variance within metagenomic sequencing data limit the discovery of associations. To account for these problems, we propose metagenomic Lognormal-Dirichlet-Multinomial (mLDM), a hierarchical Bayesian model with sparsity constraints, to estimate absolute microbial abundance and simultaneously infer both conditionally dependent associations among microbes and direct associations between microbes and environmental factors. We empirically show the effectiveness of the mLDM model using synthetic data, data from the TARA Oceans project, and a colorectal cancer dataset. Finally, we apply mLDM to 16S sequencing data from the western English Channel and report several associations. Our model can be used on both natural environmental and human metagenomic datasets, promoting the understanding of associations in the microbial community.

Identifying trends in the spatial errors of a regional climate model via clustering

Since their introduction in 1990, regional climate models (RCMs) have been widely used to study the impact of climate change on human health, ecology, and epidemiology. To ensure that the conclusions of impact studies are well founded, it is necessary to assess the uncertainty in RCMs. This is not an easy task because two major sources of uncertainties can undermine an RCM: uncertainty in the boundary conditions needed to initialize the model and uncertainty in the model itself. Using climate data for Southern Sweden over 45 years, in this paper, we present a statistical modeling framework to assess an RCM driven by analyses. More specifically, our scientific interest here is determining whether there exist time periods during which the RCM in consideration displays the same type of spatial discrepancies from the observations. The proposed model can be seen as an exploratory tool for atmospheric modelers to identify time periods that require a further in‐depth examination. Focusing on seasonal average temperature, our model relates the corresponding observed seasonal fields to the RCM output via a hierarchical Bayesian statistical model that includes a spatio‐temporal calibration term. The latter, which represents the spatial error of the RCM, is in turn provided with a Dirichlet process prior, enabling clustering of the errors in time. We apply our modeling framework to data from Southern Sweden spanning the period 1 December 1962 to 30 November 2007, revealing intriguing tendencies with respect to the RCM spatial errors of seasonal average temperature. Copyright © 2016 John Wiley & Sons, Ltd.

Disentangling long- and short-term changes in perennial organ functions in seasonal environments: A model of foliar chlorophyll and nitrogen in saplings of four evergreen broad-leaved trees

Perennial organ functions of trees living in seasonal environments exhibit temporal changes that can be classified as long-term interannual changes and seasonal fluctuations within single years. However, few studies have separately quantified these changes from longitudinal measurement data or analyzed the relationships between them. We developed a hierarchical Bayesian statistical model consisting of three parts: a long-term interannual change expressed by consecutive annual linear trends, seasonal fluctuations with 26 values for two-week periods in a year, and a random effect for repeated measurements. The model can extract long-term interannual changes and seasonal fluctuations from longitudinal repeated measure data. The pattern of seasonal fluctuation, the amount of seasonal fluctuation, and the net annual change are expressed by the estimated model parameters. We applied our model to foliar chlorophyll (Chl) and nitrogen (N) content measured repeatedly on more than 1-year-old leaves of saplings in four evergreen broad-leaved tree species using nondestructive optical methods. The model successfully explained large variations in the Chl and N content. In general, seasonal fluctuations corresponded to the phenology of current-year leaves; Chl and N tended to decrease from the opening to maturation of new leaves and increased during the rest period. The magnitude of the decrease in the Chl and N content in the growth period of current-year leaves (Δγ) did not decrease noticeably as leaves aged. For the Chl content, Δγ was positively correlated with the maximum value before leaf opening across species. For the N content, Δγ and the maximum value before leaf opening were not clearly correlated across species, but were positively correlated within some species. A model parameter for annual linear trends in Chl and N varied from positive (indicating increasing trends) to negative values (indicating decrease) depending on species and leaf age in years.

UNSUPERVISED TRANSIENT LIGHT CURVE ANALYSIS VIA HIERARCHICAL BAYESIAN INFERENCE

Historically, light curve studies of supernovae (SNe) and other transient classes have focused on individual objects with copious and high signal-to-noise observations. In the nascent era of wide field transient searches, objects with detailed observations are decreasing as a fraction of the overall known SN population, and this strategy sacrifices the majority of the information contained in the data about the underlying population of transients. A population level modeling approach, simultaneously fitting all available observations of objects in a transient sub-class of interest, fully mines the data to infer the properties of the population and avoids certain systematic biases. We present a novel hierarchical Bayesian statistical model for population level modeling of transient light curves, and discuss its implementation using an efficient Hamiltonian Monte Carlo technique. As a test case, we apply this model to the Type IIP SN sample from the Pan-STARRS1 Medium Deep Survey, consisting of 18,837 photometric observations of 76 SNe, corresponding to a joint posterior distribution with 9,176 parameters under our model. Our hierarchical model fits provide improved constraints on light curve parameters relevant to the physical properties of their progenitor stars relative to modeling individual light curves alone. Moreover, we directly evaluate the probability for occurrence rates of unseen light curve characteristics from the model hyperparameters, addressing observational biases in survey methodology. We view this modeling framework as an unsupervised machine learning technique with the ability to maximize scientific returns from data to be collected by future wide field transient searches like LSST. \smallskip

Characterizing Influenza surveillance systems performance: application of a Bayesian hierarchical statistical model to Hong Kong surveillance data.

BackgroundInfectious disease surveillance is a process the product of which reflects both actual disease trends and public awareness of the disease. Decisions made by patients, health care providers, and public health professionals about seeking and providing health care and about reporting cases to health authorities are all influenced by the information environment, which changes constantly. Biases are therefore imbedded in surveillance systems; these biases need to be characterized to provide better situational awareness for decision-making purposes. Our goal is to develop a statistical framework to characterize influenza surveillance systems, particularly their correlation with the information environment.MethodsWe identified Hong Kong influenza surveillance data systems covering healthcare providers, laboratories, daycare centers and residential care homes for the elderly. A Bayesian hierarchical statistical model was developed to examine the statistical relationships between the influenza surveillance data and the information environment represented by alerts from HealthMap and web queries from Google. Different models were fitted for non-pandemic and pandemic periods and model goodness-of-fit was assessed using common model selection procedures.ResultsSome surveillance systems — especially ad hoc systems developed in response to the pandemic flu outbreak — are more correlated with the information environment than others. General practitioner (percentage of influenza-like-illness related patient visits among all patient visits) and laboratory (percentage of specimen tested positive) seem to proportionally reflect the actual disease trends and are less representative of the information environment. Surveillance systems using influenza-specific code for reporting tend to reflect biases of both healthcare seekers and providers.ConclusionsThis study shows certain influenza surveillance systems are less correlated with the information environment than others, and therefore, might represent more reliable indicators of disease activity in future outbreaks. Although the patterns identified in this study might change in future outbreaks, the potential susceptibility of surveillance data is likely to persist in the future, and should be considered when interpreting surveillance data.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2458-14-850) contains supplementary material, which is available to authorized users.

Higher Glucose Linked to Increased Dementia Risk

Higher glucose levels were associated with an elevated risk of dementia in a community-based cohort study. High glucose levels were linked to this adverse effect both in people who had diabetes and in people who did not. “These data suggest that higher levels of glucose may have deleterious effects on the aging brain,” said Dr. Paul K. Crane of the University of Washington, Seattle, and his associates. In addition, “our findings underscore the potential consequences of temporal trends in obesity and diabetes, and suggest the need for interventions that reduce glucose levels,” they said. Dr. Crane and his colleagues analyzed data from a prospective, longitudinal study in which participants were followed every 2 years to identify incident cases of dementia. The study involved 2,067 randomly sampled older men and women who underwent numerous glucose tests as part of their biennial work-ups, beginning in the 1990s. The researchers described this as the first study among those investigating associations between glucose metabolism and the risk of dementia to evaluate glucose levels as a time-varying phenomenon. They used a hierarchical Bayesian statistical model that allowed them to incorporate multiple measurements taken over the course of several years – including random blood glucose tests, fasting blood glucose tests, and glycated hemoglobin tests – into a single composite estimate of glucose exposure for each study subject. Each participant underwent an average of 17 measurements of blood glucose and five of glycated hemoglobin. This yielded a total of 35,264 values for fasting and random glucose levels and 10,208 values for glycated hemoglobin levels. “The extensive clinical laboratory data available and the long-term follow-up of the cohort, in which there were hundreds of cases of dementia, afforded us the opportunity … to evaluate risk across the entire spectrum of observed glucose levels,” the investigators noted. Overall, 524 (25.4%) of the study participants developed dementia during a median follow-up period of 6.8 years. This included 21.6% of the participants who had diabetes and 26.1% of those who didn't have diabetes. Most of the patients with dementia had probable or possible Alzheimer's disease (403), followed by dementia from vascular disease (55) and dementia from other causes (66). In the subjects with diabetes, those who had the highest glucose levels were at increased risk of dementia, compared with those who had lower glucose levels. “For an average glucose level of 190 mg/dL [in the preceding 5 years], as compared with 160 mg/dL, the adjusted hazard ratio for dementia was 1.40,” Dr. Crane and his associates wrote (N. Engl. J. Med. 2013 Aug. 8 [doi:10.1056/NEJMoa1215740]). More surprisingly, in the subjects without diabetes, those who had the highest but still normal glucose levels were at increased risk of dementia, compared with those who had lower glucose levels. “For an average glucose level of 115 mg/dL [in the preceding 5 years], as compared with 100 mg/dL, the adjusted hazard ratio for dementia was 1.18,” the researchers reported. These associations remained significant in several sensitivity analyses, regardless of the subjects’ age or sex, and were independent of apolipoprotein E epsilon-4 allele status. The mechanism(s) by which higher glucose levels may contribute to dementia risk have not yet been elucidated, but both acute and chronic hyperglycemia, insulin resistance, and microvascular disease of the CNS could all play a role, according to the researchers. This study was supported in part by the National Institutes of Health. Dr. Crane reported no financial conflicts of interest; four other authors reported ties to industry sources.

Evidence for grain growth in molecular clouds: A Bayesian examination of the extinction law in Perseus

We investigate the shape of the extinction law in two 1-degree square fields of the Perseus Molecular Cloud complex. We combine deep red-optical (r, i, and z-band) observations obtained using Megacam on the MMT with UKIDSS near-infrared (J, H, and K-band) data to measure the colours of background stars. We develop a new hierarchical Bayesian statistical model, including measurement error, intrinsic colour variation, spectral type, and dust reddening, to simultaneously infer parameters for individual stars and characteristics of the population. We implement an efficient MCMC algorithm utilising generalised Gibbs sampling to compute coherent probabilistic inferences. We find a strong correlation between the extinction (Av) and the slope of the extinction law (parameterized by Rv). Because the majority of the extinction toward our stars comes from the Perseus molecular cloud, we interpret this correlation as evidence of grain growth at moderate optical depths. The extinction law changes from the diffuse value of Rv = 3 to the dense cloud value of Rv = 5 as the column density rises from Av = 2 mags to Av = 10 mags. This relationship is similar for the two regions in our study, despite their different physical conditions, suggesting that dust grain growth is a fairly universal process.

Spatio-temporal modeling of sudden infant death syndrome data

Sudden infant death syndrome (SIDS) is a classification of death for apparently healthy infants under one year old. However, its etiology is still largely a mystery. In this research, we analyze a spatio-temporal data set that contains yearly SIDS information from 1979 to 1984 for the counties of North Carolina. Cressie and Chan (1989) [10] used a purely spatial model to analyze the aggregated version of this data set. In this article, we present a spatio-temporal model from which optimal smoothing of SIDS rates can be derived. We use a Bayesian hierarchical statistical model (BHM) with a hidden dynamical Markov random field and extra-Poisson variability. Potential confounding of sources of variability is avoided by calibrating the extra-Poisson variability with the microscale variation in an approximate Gaussian model.

Soft Tissue Sarcoma Trials: One Size No Longer Fits All

Soft Tissue Sarcoma Trials: One Size No Longer Fits All

Demographic and Behavioral Modifiers of Arsenic Exposure Pathways: A Bayesian Hierarchical Analysis of NHEXAS Data

We introduce a Bayesian hierarchical statistical model that describes subpopulation-specific pathways of exposure to arsenic. Our model is fitted to data collected as part of the National Human Exposure Assessment Survey (NHEXAS) and builds on the structural-equation-based analysis of the same data by Clayton et al. (Journal of Exposure Analysis and Environmental Epidemiology, 2002, 12, 29-43). Using demographic information (e.g., gender or age) and surrogates for environmental exposure (e.g., tobacco usage or the average number of minutes spent in an enclosed workshop), we identify subgroup differences in exposure routes. Missing and censored data, as well as uncertainty due to measurement error, are handled systematically in the Bayesian framework. Our analysis indicates that household size, amount of time spent at home, use of tapwater for drinking and cooking, number of glasses of water drunk, use of central air conditioning, and use of gas equipment significantly modify the arsenic exposure pathways.

A Bayesian hierarchical method for multiple-event seismic location

SUMMARY We formulate the multiple-event seismic location problem as a Bayesian hierarchical statistical model (BAYHLoc). This statistical model has three distinct components: traveltime predictions, arrival-time measurements, and an a priori statistical model for each aspect of the multiple-event problem. The traveltime model is multifaceted, including both phase-specific adjustments to traveltime curves to improve broad-area prediction, as well as path/phase-specific traveltime adjustments. The arrival-time measurement model is decomposed into station, phase, and event components, allowing flexibility and physically interpretable error parameters. The prior model allows all available information to be brought to bear on the multiple event system. Prior knowledge on the probabilistic accuracy of event locations, traveltime predictions, and arrival-time measurements can be specified. Bayesian methodology merges all three components of the hierarchical model into a joint probability formulation. The joint posterior distribution is, in essence, an inference of all parameters given the prior constraints, self-consistency of the data set, and physics of traveltime calculation. We use the Markov Chain Monte Carlo method to draw realizations from the joint posterior distribution. The resulting samples can be used to estimate marginal distributions, for example epicentres and probability regions, as well as parameter correlations. We demonstrate BAYHLoc using the set of Nevada Test Site nuclear explosions, for which hypocentres are known, and phase measurements and traveltimes are well characterized. We find significant improvement in epicentre accuracy using BAYHLoc. Much of the improvement is attributed to the adaptive arrival-time measurement model, which controls data weighting. Regardless of the initial traveltime model, the use of an adjustment to the traveltime curves produces a level of epicentre accuracy that is generally achieved only through meticulous analysis and data culling. Further, we find that accurate hypocentres (including depth and origin-time) are achieved when either accurate traveltime curves with tight priors are used, or when prior information on a small subset of events is utilized.

Remapping in Human Visual Cortex

With each eye movement, stationary objects in the world change position on the retina, yet we perceive the world as stable. Spatial updating, or remapping, is one neural mechanism by which the brain compensates for shifts in the retinal image caused by voluntary eye movements. Remapping of a visual representation is believed to arise from a widespread neural circuit including parietal and frontal cortex. The current experiment tests the hypothesis that extrastriate visual areas in human cortex have access to remapped spatial information. We tested this hypothesis using functional magnetic resonance imaging (fMRI). We first identified the borders of several occipital lobe visual areas using standard retinotopic techniques. We then tested subjects while they performed a single-step saccade task analogous to the task used in neurophysiological studies in monkeys, and two conditions that control for visual and motor effects. We analyzed the fMRI time series data with a nonlinear, fully Bayesian hierarchical statistical model. We identified remapping as activity in the single-step task that could not be attributed to purely visual or oculomotor effects. The strength of remapping was roughly monotonic with position in the visual hierarchy: remapped responses were largest in areas V3A and hV4 and smallest in V1 and V2. These results demonstrate that updated visual representations are present in cortical areas that are directly linked to visual perception.