Newborns' Face Recognition: The Role of Facial Movement

This study investigates the role of dynamic information in identity face recognition at birth. More specifically we tested whether semi‐rigid motion, conveyed by a change in facial expression, facilitates identity recognition. In Experiment 1, two‐day‐old newborns, habituated to a static happy or fearful face (static condition) have been tested using a pair of novel and familiar identity static faces with a neutral expression. Results indicated that newborns manifest a preference for the familiar stimulus, independently of the facial expression. In contrast, in Experiment 2 newborns habituated to a semi‐rigid moving face (dynamic condition) manifest a preference for the novel face, independently of the facial expression. In Experiment 3, a multistatic image of a happy face with different degrees of intensity was utilized to test the role of different amount of static pictorial information in identity recognition (multistatic image condition). Results indicated that newborns were not able to recognize the face to which they have been familiarized. Overall, results clearly demonstrate that newborns' face recognition is strictly dependent on the learning conditions and that the semi‐rigid motion conveyed by facial expressions facilitates identity recognition since birth.     

Central Bank Macroeconomic Forecasting During the Global Financial Crisis: The European Central Bank and Federal Reserve Bank of New York Experiences

This article documents macroeconomic forecasting during the global financial crisis by two key central banks: the European Central Bank and the Federal Reserve Bank of New York. The article is the result of a collaborative effort between staff at the two institutions, allowing us to study the time-stamped forecasts as they were made throughout the crisis. The analysis does not exclusively focus on point forecast performance. It also examines methodological contributions, including how financial market data could have been incorporated into the forecasting process.     

Education and Union Formation as Simultaneous in Italy and Spain

Both the time spent in education and the age at first union have substantially increased in Italy and Spain. We show that exit from education and union formation are simultaneous processes. We use the European Community Household Panel providing standardised information. The effect of unobserved characteristics on both processes is controlled for using simultaneous hazard modelling. We find that the exit from education increases the risk of entering the first union, and that individual unobserved characteristics influence the occurrence of the two processes simultaneously. Ignoring these characteristics would overestimate the effect of exit from education on union formation.     

Comparison of sample size formulae for 2 × 2 cross‐over designs applied to bioequivalence studies

We consider the comparison of two formulations in terms of average bioequivalence using the 2 × 2 cross‐over design. In a bioequivalence study, the primary outcome is a pharmacokinetic measure, such as the area under the plasma concentration by time curve, which is usually assumed to have a lognormal distribution. The criterion typically used for claiming bioequivalence is that the 90% confidence interval for the ratio of the means should lie within the interval (0.80, 1.25), or equivalently the 90% confidence interval for the differences in the means on the natural log scale should be within the interval (?0.2231, 0.2231). We compare the gold standard method for calculation of the sample size based on the non‐central t distribution with those based on the central t and normal distributions. In practice, the differences between the various approaches are likely to be small. Further approximations to the power function are sometimes used to simplify the calculations. These approximations should be used with caution, because the sample size required for a desirable level of power might be under‐ or overestimated compared to the gold standard method. However, in some situations the approximate methods produce very similar sample sizes to the gold standard method. Copyright © 2005 John Wiley & Sons, Ltd.     

USING BASEBALL CARD PRICES TO MEASURE STAR QUALITY AND MONOPSONY

Baseball card prices are used to capture star quality in a new measure of productivity in Major League Baseball. Star quality, which impacts revenues, is determined from a player's baseball card price as the residual in a fit of card prices to performance statistics. This measure is entered into the computation of individual player marginal revenue product and compared to players' salaries using data from the four years leading up to the 1994 MLB strike. Results are examined for monopsonistic exploitation by market size, free agency status, type of player, and other relevant categories. Underpaid and overpaid players are identified.     

Talking about Beauty: A Study of Everyday Aesthetics among Low‐Income Citizens of Milan

This paper reports on research undertaken into the aesthetics of the everyday. As well as the subject matter of aesthetic philosophy, art criticism and of the sociology of art, beauty, and beautiful are of course very ordinary matters too. To shed light on the meanings of beauty as used in everyday settings and in natural language, we use the data collected in a study conducted with a group of low‐income residents of the city of Milan. In this study we were interested in analyzing their lifestyle in terms of their relationship with aesthetics, i.e., with “beautiful” objects and/or experiences. Participants' self‐reported aesthetic appreciations suggest that conceptions of “beauty” are used as devices to narrate pieces of identity, memories, experiences, etc. Their aesthetic judgements take on an anthropological function, creating a framework of meanings that help the participants make sense of the world of objects and of their own lives with/through them.     

Approximate Methods for State-Space Models

State-space models provide an important body of techniques for analyzing time-series, but their use requires estimating unobserved states. The optimal estimate of the state is its conditional expectation given the observation histories, and computing this expectation is hard when there are nonlinearities. Existing filtering methods, including sequential Monte Carlo, tend to be either inaccurate or slow. In this paper, we study a nonlinear filter for nonlinear/non-Gaussian state-space models, which uses Laplace's method, an asymptotic series expansion, to approximate the state's conditional mean and variance, together with a Gaussian conditional distribution. This Laplace-Gaussian filter (LGF) gives fast, recursive, deterministic state estimates, with an error which is set by the stochastic characteristics of the model and is, we show, stable over time. We illustrate the estimation ability of the LGF by applying it to the problem of neural decoding and compare it to sequential Monte Carlo both in simulations and with real data. We find that the LGF can deliver superior results in a small fraction of the computing time.