Review of The Proof and Measurement of Association between Two Things.

Meyer, Max F.

doi:10.1037/h0065390

Cited by 17 publications

(12 citation statements)

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“…A cutoff value of 0.3 for the correlation coefficient (r) reflects the lower limit for a medium effect size according to Cohen (1988), and a relatively large effect size according to Hemphill (2003; see also Gignac & Szodorai, 2016). The observed between-variant partial correlations were underestimated because of measurement errors (Spearman, 1904b;Wang, 2010), which are reflected by moderate intrarater reliabilities (see the Intrarater reliability section). To account for these measurement errors, which put an upper limit on the between-variant correlations, we also computed disattenuated partial correlations (Osborne, 2003):…”

Section: Discussionmentioning

confidence: 99%

Factors underlying visual illusions are illusion-specific but not feature-specific

et al. 2019

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Common factors are ubiquitous. For example, there is a common factor, g, for intelligence. In vision, there is much weaker evidence for such common factors. For example, visual illusion magnitudes correlate only weakly with each other. Here, we investigated whether illusions are hyper-specific as in perceptual learning. First, we tested 19 variants of the Ebbinghaus illusion that differed in color, shape, or texture. Correlations between the illusion magnitudes of the different variants were mostly significant. Second, we reanalyzed a dataset from a previous experiment where 10 illusions were tested under four conditions of luminance and found significant correlations between the different luminance conditions of each illusion. However, there were only very weak correlations between the 10 different illusions. Third, five visual illusions were tested with four orientations. Again, there were significant correlations between the four orientations of each illusion, but not across different illusions. The weak inter-illusion correlations suggest that there is no unique common mechanism for the tested illusions. We suggest that most illusions make up their own factor.

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Section: Discussionmentioning

confidence: 99%

Factors underlying visual illusions are illusion-specific but not feature-specific

et al. 2019

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“…The third and fourth metrics are based on rank-order correlation. Spearman's rank correlation coefficient (SRCC) [22] assesses how well the relationship between two variables can be described using a monotonic function, and Kendall's rank correlation coefficient (KRCC) [23] improves on SRCC by reflecting the strength of the dependence between the variables being compared. We evaluated the performance of the proposed method with these four criteria over the LIVE image database, which consists of 29 reference images and 779 distorted images for five types of distortion: JPEG, JPEG2000, White noise, Gaussian blur, and Fastfading [14].…”

Section: Resultsmentioning

confidence: 99%

Optimal Image Quality Assessment based on Distortion Classification and Color Perception

2016

KSII TIIS

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The Structural SIMilarity (SSIM) index is one of the most widely-used methods for perceptual image quality assessment (IQA). It is based on the principle that the human visual system (HVS) is sensitive to the overall structure of an image. However, it has been reported that indices predicted by SSIM tend to be biased depending on the type of distortion, which increases the deviation from the main regression curve. Consequently, SSIM can result in serious performance degradation. In this study, we investigate the aforementioned phenomenon from a new perspective and review a constant that plays a big role within the SSIM metric but has been overlooked thus far. Through an experimental study on the influence of this constant in evaluating images with SSIM, we are able to propose a new solution that resolves this issue. In the proposed IQA method, we first design a system to classify different types of distortion, and then match an optimal constant to each type. In addition, we supplement the proposed method by adding color perception-based structural information. For a comprehensive assessment, we compare the proposed method with 15 existing IQA methods. The experimental results show that the proposed method is more consistent with the HVS than the other methods.

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“…Such multipliers were derived from a second normal distribution with mean = 1 and with a variable and randomly generated standard deviation between 0.01 and 0.3. As a result, the Spearman correlation coefficient (Spearman 1987) between the original vector N and the 100,000 artificial vectors of QMD values ranged from -1.0 to -0.2 (Fig. 2).…”

Section: Methodsmentioning

confidence: 98%

Nonlinear versus linearised model on stand density model fitting and stand density index calculation: analysis of coefficients estimation via simulation

Marchi

2019

J. For. Res.

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The stand density index, one of the most important metrics for managing site occupancy, is generally calculated from empirical data by means of a coefficient derived from the ''self-thinning rule'' or stand density model. I undertook an exploratory analysis of model fitting based on simulated data. I discuss the use of the logarithmic transformation (i.e., linearisation) of the relationship between the total number of trees per hectare (N) and the quadratic mean diameter of the stand (QMD). I compare the classic method used by Reineke (J Agric Res 46:627-638, 1933), i.e., linear OLS model fitting after logarithmic transformation of data, with the ''pure'' powerlaw model, which represents the native mathematical structure of this relationship. I evaluated the results according to the correlation between N and QMD. Linear OLS and nonlinear fitting agreed in the estimation of coefficients only for highly correlated (between -1 and -0.85) or poorly correlated ([ -0.39) datasets. At average correlation values (i.e., between -0.75 and -0.4), it is probable that for practical applications, the differences were relevant, especially concerning the key coefficient for Reineke's stand density index calculation. This introduced a non-negligible bias in SDI calculation. The linearised log-log model always estimated a lower slope term than did the exponent of the nonlinear function except at the extremes of the correlation range. While the logarithmic transformation is mathematically correct and always equivalent to a nonlinear model in case of prediction of the dependent variable, the difference detected in my studies between the two methods (i.e., coefficient estimation) was directly related to the correlation between N and QMD in each simulated/disturbed dataset. In general, given the power law as the ''natural'' structure of the N versus QMD relationship, the nonlinear model is preferred, with a logarithmic transformation used only in the case of violation of parametric assumptions (e.g. data distributed non-normally).

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Review of The Proof and Measurement of Association between Two Things.

Cited by 17 publications

References 0 publications

Factors underlying visual illusions are illusion-specific but not feature-specific

Factors underlying visual illusions are illusion-specific but not feature-specific

Optimal Image Quality Assessment based on Distortion Classification and Color Perception

Nonlinear versus linearised model on stand density model fitting and stand density index calculation: analysis of coefficients estimation via simulation

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