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ENeuro Jan 2023A central question in neuroscience is how sensory inputs are transformed into percepts. At this point, it is clear that this process is strongly influenced by prior... (Review)
Review
A central question in neuroscience is how sensory inputs are transformed into percepts. At this point, it is clear that this process is strongly influenced by prior knowledge of the sensory environment. Bayesian ideal observer models provide a useful link between data and theory that can help researchers evaluate how prior knowledge is represented and integrated with incoming sensory information. However, the statistical prior employed by a Bayesian observer cannot be measured directly, and must instead be inferred from behavioral measurements. Here, we review the general problem of inferring priors from psychophysical data, and the simple solution that follows from assuming a prior that is a Gaussian probability distribution. As our understanding of sensory processing advances, however, there is an increasing need for methods to flexibly recover the shape of Bayesian priors that are not well approximated by elementary functions. To address this issue, we describe a novel approach that applies to arbitrary prior shapes, which we parameterize using mixtures of Gaussian distributions. After incorporating a simple approximation, this method produces an analytical solution for psychophysical quantities that can be numerically optimized to recover the shapes of Bayesian priors. This approach offers advantages in flexibility, while still providing an analytical framework for many scenarios. We provide a MATLAB toolbox implementing key computations described herein.
Topics: Bayes Theorem; Probability; Sensation; Normal Distribution
PubMed: 36316119
DOI: 10.1523/ENEURO.0144-22.2022 -
Analysis of occupational radiation dose data and determination of suitable probability distribution.Radiation Protection Dosimetry Jul 2023The first study on fitting dose data for workers was performed by Gale( 1) in 1965 where log-normal and normal distributions were used. Since then, various models of...
The first study on fitting dose data for workers was performed by Gale( 1) in 1965 where log-normal and normal distributions were used. Since then, various models of dose distribution have been proposed. The log-normal distribution and its different forms have been widely used for fitting the dose data. Most of the studies included one or two distributions under consideration. In this study five distributions are considered for fitting and four distributions are selected based on observation of Cullen-Frey graph. The Akaike's Information criteria (AIC) and Bayesian Information criteria (BIC) are applied to find the suitable distribution to fit the occupational dose data. The maximum likelihood method was used for calculation of AIC, BIC values and parameter estimation. A computer code is written in R-language and environment for statistical computing and graphics for analysis of occupational dose data of three institutions.
Topics: Humans; Bayes Theorem; Probability; Normal Distribution; Statistical Distributions; Radiation Dosage
PubMed: 37259618
DOI: 10.1093/rpd/ncad160 -
Journal of Healthcare Engineering 2022At present, the incidence of emergencies in obstetric care environment is gradually increasing, and different obstetric wards often have a variety of situations....
At present, the incidence of emergencies in obstetric care environment is gradually increasing, and different obstetric wards often have a variety of situations. Therefore, it can provide great help in clinical medicine to give early warning and plan coping plans according to different situations. This paper studied an obstetrics central surveillance system based on a medical image segmentation algorithm. Images obtained by central obstetrics monitoring are segmented, magnified in detail, and image features are extracted, collated, and trained. The normal distribution rule is used to classify the features, which are included in the feature library of the obstetric central monitoring system. In the gray space of the medical image, the statistical distribution of gray features of the medical image is described by the mixture model of Rayleigh distribution and Gaussian distribution. In the gray space of the medical image, Taylor series expansion is used to describe the linear geometric structure of medicine. The eigenvalues of Hessian matrix are introduced to obtain high-order multiscale features of medicine. The multiscale feature energy function is introduced into Markov random energy objective function to realize medical image segmentation. Compared with other segmentation algorithms, the accuracy and sensitivity of the proposed algorithm are 87.98% and 86.58%, respectively, which can clearly segment small medical features.
Topics: Algorithms; Humans; Image Processing, Computer-Assisted; Normal Distribution
PubMed: 35529540
DOI: 10.1155/2022/3545831 -
Clinica Chimica Acta; International... Dec 2021The concept of reference change values (RCVs) for diagnosis and monitoring of diseases has become well established. Several models habe been developed, e. g. one...
The concept of reference change values (RCVs) for diagnosis and monitoring of diseases has become well established. Several models habe been developed, e. g. one assuming a normal distribution and another one for a log-normal distribution. RCV values calculated for some measurands with both models are compared with each other and led to similar results. A few examples led to RCV values which are not plausible for diagnostic purposes. Although statistical concepts of RCV values are well established, their clinical relevance remains questionable at least for some measurands. Studies with clinicians are required whether RCVs are of practical usefulness.
Topics: Humans; Normal Distribution; Reference Values
PubMed: 34653386
DOI: 10.1016/j.cca.2021.10.006 -
Scientific Reports Feb 2023One clear aspect of behaviour in the COVID-19 pandemic has been people's focus on, and response to, reported or observed infection numbers in their community. We...
One clear aspect of behaviour in the COVID-19 pandemic has been people's focus on, and response to, reported or observed infection numbers in their community. We describe a simple model of infectious disease spread in a pandemic situation where people's behaviour is influenced by the current risk of infection and where this behavioural response acts homeostatically to return infection risk to a certain preferred level. This homeostatic response is active until approximate herd immunity is reached: in this domain the model predicts that the reproduction rate R will be centred around a median of 1, that proportional change in infection numbers will follow the standard Cauchy distribution with location and scale parameters 0 and 1, and that high infection numbers will follow a power-law frequency distribution with exponent 2. To test these predictions we used worldwide COVID-19 data from 1st February 2020 to 30th June 2022 to calculate [Formula: see text] confidence interval estimates across countries for these R, location, scale and exponent parameters. The resulting median R estimate was [Formula: see text] (predicted value 1) the proportional change location estimate was [Formula: see text] (predicted value 0), the proportional change scale estimate was [Formula: see text] (predicted value 1), and the frequency distribution exponent estimate was [Formula: see text] (predicted value 2); in each case the observed estimate agreed with model predictions.
Topics: Humans; COVID-19; Pandemics; Reproduction; Statistical Distributions
PubMed: 36765110
DOI: 10.1038/s41598-023-28752-4 -
PloS One 2023In this study, we propose a generalized Marshall-Olkin exponentiated exponential distribution as a submodel of the family of generalized Marshall-Olkin distribution....
In this study, we propose a generalized Marshall-Olkin exponentiated exponential distribution as a submodel of the family of generalized Marshall-Olkin distribution. Some statistical properties of the proposed distribution are examined such as moments, the moment-generating function, incomplete moment, and Lorenz and Bonferroni curves. We give five estimators for the unknown parameters of the proposed distribution based on maximum likelihood, least squares, weighted least squares, and the Anderson-Darling and Cramer-von Mises methods of estimation. To investigate the finite sample properties of the estimators, a comprehensive Monte Carlo simulation study is conducted for the models with three sets of randomly selected parameter values. Finally, four different real data applications are presented to demonstrate the usefulness of the proposed distribution in real life.
Topics: Computer Simulation; Statistical Distributions; Monte Carlo Method; Least-Squares Analysis
PubMed: 36652462
DOI: 10.1371/journal.pone.0280349 -
Cerebral Cortex (New York, N.Y. : 1991) Aug 2023Numbers of neurons and their spatial variation are fundamental organizational features of the brain. Despite the large corpus of cytoarchitectonic data available in the...
Numbers of neurons and their spatial variation are fundamental organizational features of the brain. Despite the large corpus of cytoarchitectonic data available in the literature, the statistical distributions of neuron densities within and across brain areas remain largely uncharacterized. Here, we show that neuron densities are compatible with a lognormal distribution across cortical areas in several mammalian species, and find that this also holds true within cortical areas. A minimal model of noisy cell division, in combination with distributed proliferation times, can account for the coexistence of lognormal distributions within and across cortical areas. Our findings uncover a new organizational principle of cortical cytoarchitecture: the ubiquitous lognormal distribution of neuron densities, which adds to a long list of lognormal variables in the brain.
Topics: Animals; Neurons; Brain; Mammals; Cerebral Cortex; Statistical Distributions
PubMed: 37409647
DOI: 10.1093/cercor/bhad160 -
Journal of the Academy of Nutrition and... Jan 2021The Multiple Source Method (MSM) and the National Cancer Institute (NCI) method estimate usual dietary intake from short-term dietary assessment instruments, such as... (Comparative Study)
Comparative Study
Comparing Methods from the National Cancer Institute vs Multiple Source Method for Estimating Usual Intake of Nutrients in the Hispanic Community Health Study/Study of Latino Youth.
BACKGROUND
The Multiple Source Method (MSM) and the National Cancer Institute (NCI) method estimate usual dietary intake from short-term dietary assessment instruments, such as 24-hour recalls. Their performance varies according to sample size and nutrients distribution. A comparison of these methods among a multiethnic youth population, for which nutrient composition and dietary variability may differ from adults, is a gap in the literature.
OBJECTIVE
To compare the performance of the NCI method relative to MSM in estimating usual dietary intakes in Hispanic/Latino adolescents.
DESIGN
Data derived from the cross-sectional population-based Hispanic Community Health Study/Study of Latino Youth, an ancillary study of offspring of participants in the adult Hispanic Community Health Study/Study of Latino Youth cohort. Dietary data were obtained by two 24-hour recalls.
PARTICIPANTS/SETTING
One thousand four hundred fifty-three Hispanic/Latino youth (aged 8 to 16 years) living in four urban US communities (Bronx, NY; Chicago, IL; Miami, FL; and San Diego, CA) during 2012 through 2014.
MAIN OUTCOME MEASURES
The NCI method and the MSM were applied to estimate usual intake of total energy, macronutrients, minerals and vitamins, added sugar, and caffeine.
STATISTICAL ANALYSES
Mean, standard deviation, minimum and maximum values, coefficient of variation, variance ratio, and differences between NCI and MSM methods and the 2-day mean were estimated in several percentiles of the distribution, as well as concordance correlation coefficients and Bland-Altman plot analysis.
RESULTS
The distributions of all nutrients studied were very similar between NCI and MSM. The correlation between NCI and MSM was >0.80 for all nutrients (P<0.001), except dietary cholesterol, vitamin C, and n-3 fatty acids. In individual estimations, NCI method predicted higher estimates and lower variance than the MSM. The lowest level of agreement was observed in the values at the tails of the distribution, and for nutrients with high variance ratio.
CONCLUSIONS
Overall, both MSM and NCI method provided acceptable estimates of the usual intake distribution using 24-hour recall, and they better represented the usual intake compared with 2-day mean, correcting for intraindividual variability.
Topics: Adolescent; Caffeine; Child; Cohort Studies; Cross-Sectional Studies; Diet Records; Diet Surveys; Dietary Sugars; Eating; Energy Intake; Female; Hispanic or Latino; Humans; Male; Micronutrients; National Cancer Institute (U.S.); Nutrients; Nutritional Status; Statistical Distributions; United States; Urban Population
PubMed: 32773213
DOI: 10.1016/j.jand.2020.03.010 -
European Geriatric Medicine Aug 2022Day-care services contribute to maintaining the daily living ability of older people cared for at home. This study aimed to detect factors that could impede the...
PURPOSE
Day-care services contribute to maintaining the daily living ability of older people cared for at home. This study aimed to detect factors that could impede the continuation of day-care services.
METHODS
We collected clinical data of 132 older users (age = 82.8 ± 7.5 years; male:female = 49:83) utilizing our day-care center from April 2019 to March 2020. We evaluated age, sex, underlying disease, medication, family background, care level, food texture, physical ability, reasons for frequenting day-care centers, and combined medical/nursing care plans. Participants were divided into two groups: continuation (n = 51) and suspension (n = 81). The collected items were evaluated statistically using the chi-square test, Mann-Whitney test, and unpaired t test. Multivariate logistic analysis (forward-backward stepwise selection method) was added to the statistically significant items. Statistical significance was defined as p < 0.05.
RESULTS
The comparison test detected statistical significance in Parkinson disease/Parkinsonism, pain complaints, day-service use, short-stay service use, day-care center use to reduce care burden, physical ability including ambulation, and availability of the major caregiver (p < 0.05). Day-care service use to reduce care burden (odds ratio 5.646, p < 0.05), use of short-stay and day-care services (odds ratio 4.798, p < 0.05), and low independent ambulation (odds ratio 0.585, p < 0.05) were the likely factors for suspended use (percentage of correct classification = 68.5%).
CONCLUSION
An unreplaceable and effective program for day-service and short-stay services to improve the activities of daily living of older users and reduce care burden is required in day-care centers.
Topics: Activities of Daily Living; Aged; Aged, 80 and over; Caregiver Burden; Caregivers; Chi-Square Distribution; Female; Humans; Male; Walking
PubMed: 35217981
DOI: 10.1007/s41999-022-00621-9 -
Psychological Research Sep 2022Humans are surprisingly good at learning the statistical characteristics of their visual environment. Recent studies have revealed that not only can the visual system...
Humans are surprisingly good at learning the statistical characteristics of their visual environment. Recent studies have revealed that not only can the visual system learn repeated features of visual search distractors, but also their actual probability distributions. Search times were determined by the frequency of distractor features over consecutive search trials. The search displays applied in these studies involved many exemplars of distractors on each trial and while there is clear evidence that feature distributions can be learned from large distractor sets, it is less clear if distributions are well learned for single targets presented on each trial. Here, we investigated potential learning of probability distributions of single targets during visual search. Over blocks of trials, observers searched for an oddly colored target that was drawn from either a Gaussian or a uniform distribution. Search times for the different target colors were clearly influenced by the probability of that feature within trial blocks. The same search targets, coming from the extremes of the two distributions were found significantly slower during the blocks where the targets were drawn from a Gaussian distribution than from a uniform distribution indicating that observers were sensitive to the target probability determined by the distribution shape. In Experiment 2, we replicated the effect using binned distributions and revealed the limitations of encoding complex target distributions. Our results demonstrate detailed internal representations of target feature distributions and that the visual system integrates probability distributions of target colors over surprisingly long trial sequences.
Topics: Attention; Humans; Learning; Normal Distribution; Probability; Reaction Time; Visual Perception
PubMed: 34997327
DOI: 10.1007/s00426-021-01621-3