The Effect of Learning Experiences and Context on Infant Imitation and Generalization

Over the first years of life, infants gradually develop the ability to retrieve their memories across cue and contextual changes. Whereas maturational factors drive some of these developments in memory ability, experiences occurring within the learning event may also impact infants' ability to retrieve memories in new situations. In 2 experiments we examined whether it was possible to facilitate 12‐month‐old infants' generalization of learning in the deferred imitation paradigm by varying experiences before or during the demonstration session, or during the retention interval. In Experiment 1, altering the length, timing, or variability of training had no impact on generalization; infants showed a low, but consistent level of memory retrieval. In Experiment 2, infants who experienced a unique context for encoding and retrieval exhibited generalization; infants who experienced the context prior to the demonstration session, or during the retention interval, did not. Specificity is a robust feature of infant memory and is not substantially altered by encoding experiences in an observational learning paradigm. Previous history with a learning environment can, however, impact the flexibility of memory retrieval.     

A spatiotemporal auto-regressive moving average model for solar radiation

Summary.  To investigate the variability in energy output from a network of photovoltaic cells, solar radiation was recorded at 10 sites every 10 min in the Pentland Hills to the south of Edinburgh. We identify spatiotemporal auto-regressive moving average models as the most appropriate to address this problem. Although previously considered computationally prohibitive to work with, we show that by approximating using toroidal space and fitting by matching auto-correlations, calculations can be substantially reduced. We find that a first-order spatiotemporal auto-regressive (STAR(1)) process with a first-order neighbourhood structure and a Matern noise process provide an adequate fit to the data, and we demonstrate its use in simulating realizations of energy output.     

Bayesian inference for a discretely observed stochastic kinetic model

The ability to infer parameters of gene regulatory networks is emerging as a key problem in systems biology. The biochemical data are intrinsically stochastic and tend to be observed by means of discrete-time sampling systems, which are often limited in their completeness. In this paper we explore how to make Bayesian inference for the kinetic rate constants of regulatory networks, using the stochastic kinetic Lotka-Volterra system as a model. This simple model describes behaviour typical of many biochemical networks which exhibit auto-regulatory behaviour. Various MCMC algorithms are described and their performance evaluated in several data-poor scenarios. An algorithm based on an approximating process is shown to be particularly efficient.     

A simulation study of a discrete-time multiserver retrial queue with finite population

In this paper, we deal with a discrete-time multiserver retrial queue with finite population. Firstly, we study the Markov chain at the epochs immediately after slot boundaries making emphasis on the computation of its steady-state distribution. Then, the main performance measures are investigated. Besides, we simulate the waiting time of a customer in the retrial group under three different queueing policies. Some numerical examples are given to illustrate the analysis.     

A bayesian estimator for the dependence function of a bivariate extreme‐value distribution

Any continuous bivariate distribution can be expressed in terms of its margins and a unique copula. In the case of extreme‐value distributions, the copula is characterized by a dependence function while each margin depends on three parameters. The authors propose a Bayesian approach for the simultaneous estimation of the dependence function and the parameters defining the margins. They describe a nonparametric model for the dependence function and a reversible jump Markov chain Monte Carlo algorithm for the computation of the Bayesian estimator. They show through simulations that their estimator has a smaller mean integrated squared error than classical nonparametric estimators, especially in small samples. They illustrate their approach on a hydrological data set.     

Trait Expressiveness and Marital Satisfaction: The Role of Idealization Processes

This study examined the processes that underlie the association between trait expressiveness and marital satisfaction. A total of 168 newlywed couples participated in a four‐wave, 13‐year longitudinal study of marriage. Cross‐sectional and longitudinal path analyses suggested that expressiveness promotes satisfaction by leading spouses to engage in affectionate behavior and by leading them to idealize their partner. Expressive people formed idealized images of their partner because they brought out the best in their partner's behavior and because they interpreted their partner's behavior in a favorable light. The study extends previous research by showing that the benefits of trait expressiveness extend into the second decade of marriage and by providing a plausible explanation of the connections between trait expressiveness and marital satisfaction.     

Reconsidering the Evolution of Nonlinguistic Communication: The Case of Laughter

Nonlinguistic communication is typically proposed to convey representational messages, implying that particular signals are associated with specific signaler emotions, intentions, or external referents. However, common signals produced by both nonhuman primates and humans may not exhibit such specificity, with human laughter for example showing significant diversity in both acoustic form and production context. We therefore outline an alternative to the representational approach, arguing that laughter and other nonlinguistic vocalizations are used to influence the affective states of listeners, thereby also affecting their behavior. In the case of laughter, we propose a primary function of accentuating or inducing positive affect in the perceiver in order to promote a more favorable stance toward the laugher. Two simple strategies are identified, namely producing laughter with acoustic features that have an immediate impact on listener arousal, and pairing these sounds with positive affect in the listener to create learned affective responses. Both depend on factors like the listener's current emotional state and past interactions with the vocalizer, with laughers predicted to adjust their sounds accordingly. This approach is used to explain findings from two experimental studies that examined the use of laughter in same-sex and different-sex dyads composed of either friends or strangers, and may be applicable to other forms of nonlinguistic communication.     

Urban Power Structures and Publicly Financed Stadiums

Urban elites are increasingly addressing local social problems though policies that turn their cities into tourist destinations. Often at the heart of these policies are new publicly financed sports stadiums. Ironically, this strategy is flourishing despite near-unanimous academic criticism, and increasing public skepticism, about this approach. Our research addresses this contradiction by exploring how and why powerful decisionmakers continue supporting publicly financed stadiums. We rely on local growth coalition theory to explore this topic because it offers analytical advantages, including looking beyond local sports teams as the focal point of these initiatives, addressing the variation in the outcomes of these initiatives, and acknowledging that policymakers are predisposed toward supporting these initiatives but that this predisposition does not always result in success.     

Adult Drug Treatment Courts: A Review

Drug treatment courts (DTCs), an alternative to traditional criminal courts, provide an innovative way to legally process some drug offenders. The origin and recent growth of the drug court system in the USA can be explained as an unintended consequence of a failing 'war on drugs'. In this article, we discuss the spread of adult DTCs throughout the USA, the main components of drug courts, controversies surrounding DTCs, and some criticisms of drug courts. We summarize the recent evaluations of adult DTCs, and highlight various DTC factors that have been found to reduce individual drug use and criminal activity. We also offer suggestions for future research on DTCs and conclude with policy recommendations.     

Joint tests of non-nested models and general error specifications

This paper is concerned with joint tests of non-nested models and simultaneous departures from homoskedasticity, serial independence and normality of the disturbance terms. Locally equivalent alternative models are used to construct joint tests since they provide a convenient way to incorporate more than one type of departure from the classical conditions. The joint tests represent a simple asymptotic solution to the “pre-testing” problem in the context of non-nested linear regression models. Our simulation results indicate that the proposed tests have good finite sample properties.