PDSTD - The Portsmouth Dynamic Spontaneous Tears Database

The vast majority of research on human emotional tears has relied on posed and static stimulus materials. In this paper, we introduce the Portsmouth Dynamic Spontaneous Tears Database (PDSTD), a free resource comprising video recordings of 24 female encoders depicting a balanced representation of sadness stimuli with and without tears. Encoders watched a neutral film and a self-selected sad film and reported their emotional experience for 9 emotions. Extending this initial validation, we obtained norming data from an independent sample of naïve observers (N = 91, 45 females) who watched videos of the encoders during three time phases (neutral, pre-sadness, sadness), yielding a total of 72 validated recordings. Observers rated the expressions during each phase on 7 discrete emotions, negative and positive valence, arousal, and genuineness. All data were analyzed by means of general linear mixed modelling (GLMM) to account for sources of random variance. Our results confirm the successful elicitation of sadness, and demonstrate the presence of a tear effect, i.e., a substantial increase in perceived sadness for spontaneous dynamic weeping. To our knowledge, the PDSTD is the first database of spontaneously elicited dynamic tears and sadness that is openly available to researchers. The stimuli can be accessed free of charge via OSF from https://osf.io/uyjeg/?view_only=24474ec8d75949ccb9a8243651db0abf.

PDSTD - The Portsmouth Dynamic Spontaneous Tears Database

The vast majority of research on human emotional tears has relied on posed and static stimulus materials. In this paper, we introduce the Portsmouth Dynamic Spontaneous Tears Database (PDSTD), a free resource comprising video recordings of 24 female encoders depicting a balanced representation of sadness stimuli with and without tears. Encoders watched a neutral film and a self-selected sad film and reported their emotional experience for 9 emotions. Extending this initial validation, we obtained norming data from an independent sample of naïve observers (N = 91, 45 females) who watched videos of the encoders during three time phases (neutral, pre-sadness, sadness), yielding a total of 72 validated recordings. Observers rated the expressions during each phase on 7 discrete emotions, negative and positive valence, arousal, and genuineness. All data were analyzed by means of general linear mixed modelling (GLMM) to account for sources of random variance. Our results confirm the successful elicitation of sadness, and demonstrate the presence of a tear effect, i.e., a substantial increase in perceived sadness for spontaneous dynamic weeping. To our knowledge, the PDSTD is the first database of spontaneously elicited dynamic tears and sadness that is openly available to researchers. The stimuli can be accessed free of charge via OSF from https://osf.io/uyjeg/?view_only=24474ec8d75949ccb9a8243651db0abf.

Some Implications of Distinguishing Between Unexplained Variance That Is Systematic or Random

Because error variance alternatively can be considered to be the sum of systematic variance associated with unknown variables and randomness, a tripartite assumption is proposed that total variance in the dependent variable can be partitioned into three variance components. These are variance in the dependent variable that is explained by the independent variable, variance in the dependent variable that is unexplained but systematic (associated with variance in unknown variables), and random variance. Based on the tripartite assumption, classical measurement theory, and simple mathematics, it is shown that these components can be estimated using observable data. Mathematical and computer simulations illustrate some of the important issues and implications.

Asymptotics for randomly reinforced urns with random barriers

An urn contains black and red balls. Let Zn be the proportion of black balls at time n and 0≤L<U≤1 random barriers. At each time n, a ball bn is drawn. If bn is black and Zn-1<U, then bn is replaced together with a random number Bn of black balls. If bn is red and Zn-1>L, then bn is replaced together with a random number Rn of red balls. Otherwise, no additional balls are added, and bn alone is replaced. In this paper we assume that Rn=Bn. Then, under mild conditions, it is shown that Zn→a.s.Z for some random variable Z, and Dn≔√n(Zn-Z)→𝒩(0,σ2) conditionally almost surely (a.s.), where σ2 is a certain random variance. Almost sure conditional convergence means that ℙ(Dn∈⋅|𝒢n)→w 𝒩(0,σ2) a.s., where ℙ(Dn∈⋅|𝒢n) is a regular version of the conditional distribution of Dn given the past 𝒢n. Thus, in particular, one obtains Dn→𝒩(0,σ2) stably. It is also shown that L<Z<U a.s. and Z has nonatomic distribution.

Lady Justice Versus Cult of Statistical Significance

Economics and other sciences use null hypothesis statistical significance testing without a loss function and avoid asking “how big is a big loss or gain?.” Statistical significance is not equivalent to economic significance; the mistake is evident when one reflects that the estimated payoff from a lottery is not the same as the odds of winning that lottery. Yet a widespread failure to make the distinction between an estimate of human consequence and an estimate of its probability—between the meaning of an estimated average and the random variance around it—is killing people in medicine and impoverishing people in economics. The ethical problem created by a test of statistical significance is made worse by the method’s blatant illogic at the foundational level, a fact unacknowledged by most of those depending on it. Several changes to the literature and a recent Supreme Court decision could help.

Progress Risk Analysis Based on the Improved Learning Curve

The learning curve is the curve which indicates individuals and organizations’ effectiveness of learning. Reasonable learning curve not only can use scientific analysis the time-consuming of process changes, accurately forecast the end of time, but also can help policy makers identify risks and risk analysis of the incident on the progress of the project impact.This article summarized the results of previous studies, constructed forecasting model of dynamic random variance time series, based on the learning curve, explained the state of volatility of learning curve in the construction of projects.