scholarly journals Contraction bias in temporal estimation

2021 ◽  
Author(s):  
Noam Tal-Perry ◽  
Shlomit Yuval-Greenberg
Keyword(s):  
Bayesian Inference ◽  
Perceptual Bias ◽  
Time Duration ◽  
Perceptual Features ◽  
Temporal Domain ◽  
Temporal Estimation ◽  
The Difference ◽  
Bayesian Inference Framework ◽  
Estimation Of Time

When asked to compare the perceptual features of two serially presented objects, participants are often biased to over- or under-estimate the difference in magnitude between the stimuli. Overestimation occurs consistently when a) the two stimuli are relatively small in magnitude and the first stimulus is larger in magnitude than the second; or b) the two stimuli are relatively large in magnitude and the first stimulus is smaller in magnitude than the second; underestimation consistently occurs in the complementary cases. This systematic perceptual bias, known as the contraction bias, was demonstrated for a multitude of perceptual features and in various modalities, but it is yet unknown whether it also exists in the temporal domain. Here, we tested whether estimation of time-duration is affected by the contraction bias. In each trial of three experiments (n=20 each), participants compared the duration of two visually presented stimuli. Findings revealed over- and under-estimation effects as predicted by the contraction bias. In addition, we found that the bias was asymmetrical, indicating that, in some cases, the subjective center of the distribution was shifted to the left. Here, we discuss this asymmetry and describe how these findings can be explained via a Bayesian inference framework.

The Impact of Video Speed on the Estimation of Time Duration in Sport

2021 ◽  
pp. 1-11
Author(s):  
Lisa-Marie Schütz ◽  
Geoffrey Schweizer ◽  
Henning Plessner
Keyword(s):  
Real Time ◽  
Time Estimation ◽  
Slow Motion ◽  
Sport Performance ◽  
Time Duration ◽  
Video Presentation ◽  
Sporting Activities ◽  
The Impact ◽  
Estimation Of Time

The authors investigated the impact of video speed on judging the duration of sport performance. In three experiments, they investigated whether the speed of video presentation (slow motion vs. real time) has an influence on the accuracy of time estimation of sporting activities (n1 = 103; n2 = 100; n3 = 106). In all three studies, the time estimation was more accurate in real time than in slow motion, in which time was overestimated. In two studies, the authors initially investigated whether actions in slow motion are perceived to last longer because the distance they cycled or ran is perceived to be longer (n4 = 92; n5 = 106). The results support the hypothesis that the duration of sporting activities is estimated more accurately when they are presented in real time than in slow motion. Sporting officials’ judgments that require accurate time estimation may thus be biased when based on slow-motion displays.

Likelihood-free Bayesian inference framework for sizing kiwifruit from orchard imaging surveys

2021 ◽  
Author(s):  
Louis Ranjard ◽  
James Bristow ◽  
Zulfikar Hossain ◽  
Alvaro Orsi ◽  
Henry J. Kirkwood ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Bayesian Inference Framework

scholarly journals A Bayesian Inference Framework for Procedural Material Parameter Estimation

2020 ◽  
Vol 39 (7) ◽  
pp. 255-266
Author(s):  
Y. Guo ◽  
M. Hašan ◽  
L. Yan ◽  
S. Zhao
Keyword(s):  
Parameter Estimation ◽  
Bayesian Inference ◽  
Material Parameter ◽  
Bayesian Inference Framework

scholarly journals Bayesian Inference for the Difference of Two Proportion Parameters in Over-Reported Two-Sample Binomial Data Using the Doubly Sample

Stats ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 111-120 ◽  
Author(s):  
Dewi Rahardja
Keyword(s):  
Bayesian Inference ◽  
Closed Form ◽  
Bayesian Approach ◽  
Interval Estimation ◽  
Real Data ◽  
Simulation Studies ◽  
Posterior Distributions ◽  
Binomial Data ◽  
The Difference ◽  
Data Subject

We construct a point and interval estimation using a Bayesian approach for the difference of two population proportion parameters based on two independent samples of binomial data subject to one type of misclassification. Specifically, we derive an easy-to-implement closed-form algorithm for drawing from the posterior distributions. For illustration, we applied our algorithm to a real data example. Finally, we conduct simulation studies to demonstrate the efficiency of our algorithm for Bayesian inference.

scholarly journals An efficient Bayesian inference framework for coalescent-based nonparametric phylodynamics

2015 ◽  
Vol 31 (20) ◽  
pp. 3282-3289 ◽  
Author(s):  
Shiwei Lan ◽  
Julia A. Palacios ◽  
Michael Karcher ◽  
Vladimir N. Minin ◽  
Babak Shahbaba
Keyword(s):  
Bayesian Inference ◽  
Bayesian Inference Framework

Estimation of time-varying respiratory mechanical parameters by recursive least squares

1991 ◽  
Vol 71 (3) ◽  
pp. 1159-1165 ◽  
Author(s):  
A. M. Lauzon ◽  
J. H. Bates
Keyword(s):  
Least Squares ◽  
Time Course ◽  
Weighted Average ◽  
Recursive Least Squares ◽  
Dynamic Hyperinflation ◽  
Time Varying ◽  
Mechanical Parameters ◽  
Tracheal Pressure ◽  
The Difference ◽  
Estimation Of Time

Continuous estimation of time-varying respiratory mechanical parameters is required to fully characterize the time course of bronchoconstriction. To achieve such estimation, we developed an estimator that uses the recursive linear least-squares algorithm to fit the equation Ptr = RV + EV + K to measurements of tracheal pressure (Ptr) and flow (V). The volume (V) is obtained by numerical integration of V. The estimator has a finite memory with length into the past at each point in time that varies inversely with the difference between the current measurement of Ptr and that predicted by the model, to allow the algorithm to track rapidly varying parameters (R, E, and K). V usually exhibits significant drift and must be corrected. Of the several correction methods investigated, subtraction of the recursively weighted average of V before integration to V was found to perform best. The estimator was tested on simulated noisy data where it successfully followed a fivefold increase in R and a twofold increase in E occurring over 10 s. Three dogs and two cats were anesthetized, paralyzed, tracheostomized, and challenged with a bolus of methacholine (approximately 13 mg/kg iv). Increases of 3- to 10-fold were observed in R and 2- to 3-fold in E, beginning within 10–40 s after the bolus injection. In some animals we found that the increase in E occurred more slowly than that in R, which the V signal suggested was due to dynamic hyperinflation of the lungs. These results demonstrate that our recursive estimator is able to track rapid changes in respiratory mechanical parameters during bronchoconstrictor challenge.

scholarly journals Time and the event: The semantics of Russian prefixes

Nordlyd ◽  
10.7557/12.72 ◽  
2005 ◽  
Vol 32 (2) ◽  
Author(s):  
Gillian Ramchand
Keyword(s):  
Argument Structure ◽  
Temporal Structure ◽  
Event Structure ◽  
Temporal Domain ◽  
Structure Level ◽  
Verb Forms ◽  
Basic Distinction ◽  
The Difference ◽  
The One ◽  
Structure Domain

In this paper, I draw on data from prefixation in Russian to argue for a basic distinction between event structure and temporal struc- ture. I present a linguistic semantics of verb and argument structure interpretation on the one hand, and a formal semantic implementa- tion of 'telicity' on the other, which makes sense of the generalisations apparently common to both domains. I will claim that the temporal domain embeds the event structure domain, and that the latter con- strains the former. At the same time, the different formal primitives that operate at the levels proposed form the basis for a principled linguistic distinction between the two tiers of composition: the event structure level encodes subevental relations and predicational rela- tions within those subevents; the temporal structure level introduces a t variable explicitly and relates it to the structure built up by the event level. Whether the event structure is homogenous or not will have an impact on whether the temporal variable chosen will be 'def- inite' or 'indefinite.' This latter claim then forms the basis for a new conception of the difference between perfective and imperfective verb forms in Russian.

scholarly journals Wilson statistics: derivation, generalization and applications to electron cryomicroscopy

2021 ◽  
Vol 77 (5) ◽  
Author(s):  
Amit Singer
Keyword(s):  
Bayesian Inference ◽  
Theoretical Foundation ◽  
Three Dimensional ◽  
Electron Cryomicroscopy ◽  
High Frequencies ◽  
X Ray ◽  
X Ray Crystallography ◽  
Bayesian Inference Framework ◽  
Higher Order Spectra ◽  
Mathematical Derivation

The power spectrum of proteins at high frequencies is remarkably well described by the flat Wilson statistics. Wilson statistics therefore plays a significant role in X-ray crystallography and more recently in electron cryomicroscopy (cryo-EM). Specifically, modern computational methods for three-dimensional map sharpening and atomic modelling of macromolecules by single-particle cryo-EM are based on Wilson statistics. Here the first rigorous mathematical derivation of Wilson statistics is provided. The derivation pinpoints the regime of validity of Wilson statistics in terms of the size of the macromolecule. Moreover, the analysis naturally leads to generalizations of the statistics to covariance and higher-order spectra. These in turn provide a theoretical foundation for assumptions underlying the widespread Bayesian inference framework for three-dimensional refinement and for explaining the limitations of autocorrelation-based methods in cryo-EM.

scholarly journals Statistical Inference for Clustering Results Interpretation in Clinical Practice

2021 ◽  
Author(s):  
Alexander Kanonirov ◽  
Ksenia Balabaeva ◽  
Sergey Kovalchuk
Keyword(s):  
Machine Learning ◽  
Clinical Practice ◽  
Bayesian Inference ◽  
Statistical Inference ◽  
Clinical Pathways ◽  
Learning Models ◽  
The Difference ◽  
Characteristic Features ◽  
Machine Learning Models

The relevance of this study lies in improvement of machine learning models understanding. We present a method for interpreting clustering results and apply it to the case of clinical pathways modeling. This method is based on statistical inference and allows to get the description of the clusters, determining the influence of a particular feature on the difference between them. Based on the proposed approach, it is possible to determine the characteristic features for each cluster. Finally, we compare the method with the Bayesian inference explanation and with the interpretation of medical experts [1].

scholarly journals A universal Bayesian inference framework for complicated creep constitutive equations

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yoh-ichi Mototake ◽  
Hitoshi Izuno ◽  
Kenji Nagata ◽  
Masahiko Demura ◽  
Masato Okada
Keyword(s):  
Bayesian Inference ◽  
Constitutive Equations ◽  
Bayesian Inference Framework
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