Infants’ Use of Shared Experience in Declarative Pointing

In this study, we asked whether 14‐ and 18‐month‐old infants use the experiences they have previously shared with others when deciding what to point to for them declaratively. After sharing a particular type of referent with an adult in an excited manner, 18‐month‐olds subsequently found a picture of that type of referent more worthy of declarative pointing than some other picture—but only for that adult, not for a different adult. Mixed results were found with 14‐month‐olds. We thus show that by 18 months, infants accurately track their shared experiences with specific individuals and use this to make communicative decisions. These results also demonstrate that infants sometimes use declarative pointing to indicate not totally “new” things, as in the classic formulation, but things which are “old” in the sense that “we” should recognize them as similar to something we have previously shared.     

The Balance Point: Keeping Serial Bibliographic Records Viable in a Digital Age

Abstract

Michael Norman, head of serials cataloging at University of Illinois at Urbana-Champaign (UIUC) library, describes his library's development of an integrated management system for serials, the Online Research Resources (ORR). The ORR was designed to draw together and deliver to the public a wide range of information related to the library's serial holdings that previously could only be obtained by consulting a number of sources. With a single search, UIUC patrons can now consult the ORR for serials information such as variant titles, online availability, subject categories, print summary statements, ISI impact factor, where the title is indexed, and whether it is peer reviewed—an innovation sorely needed and highly welcomed by librarians and patrons alike.     

A Multidisciplinary Cognitive Behavioural Framework of Impulse Buying: A Systematic Review of the Literature

Impulse buying (IB) is one of the most fragmented concepts in the marketing and consumer literature. This has resulted in some contradictory findings and a lack of an overarching theoretical framework for understanding the IB process and its outcomes. Based on a systematic review of the literature published over the past 60 years, the authors synthesize various research perspectives into a comprehensive multidisciplinary framework of IB – linking antecedents, triggers, the buying act and post‐purchase outcomes. The paper makes four specific contributions relevant to both academic researchers and practitioners. First, it provides a comprehensive understanding of IB as a process and outcome, by systematically reviewing the existing literature. Second, it specifically recognizes and discusses the triggers of IB, by drawing on literature related to the buying process, intentions and motivations. Third, this is the first meta‐analysis review in the area. Finally, it considers the potential consequences of IB that lead to subsequent IB, and provides a framework for future investigation. This paper also addresses a problem of relevance to both academics and practitioners, proposing further research and managerial implications.     

Lottomania and other anomalies in the market for lotto

This research is concerned with the determination of the demand for “lotto” in Israel. While an important focus of our research is upon the effects on the demand for lotto of ticket pricing and jackpot announcements, we also investigate several empirical phenomena that are apparently inconsistent with expected utility theory. These include an effect we call “lottomania” which is induced by rollover, and “prize fatigue” when the jackpot does not increase. Another aberration from expected utility theory is that the underlying odds of winning have no measurable effect on sales.     

Non-linear regression models for Approximate Bayesian Computation

Approximate Bayesian inference on the basis of summary statistics is well-suited to complex problems for which the likelihood is either mathematically or computationally intractable. However the methods that use rejection suffer from the curse of dimensionality when the number of summary statistics is increased. Here we propose a machine-learning approach to the estimation of the posterior density by introducing two innovations. The new method fits a nonlinear conditional heteroscedastic regression of the parameter on the summary statistics, and then adaptively improves estimation using importance sampling. The new algorithm is compared to the state-of-the-art approximate Bayesian methods, and achieves considerable reduction of the computational burden in two examples of inference in statistical genetics and in a queueing model.     

Practical Treatments Considering the Role of Sociocultural Factors on Problem Gambling

Reviews of problem gambling (PG) literature increasingly recognize the ways in which different ethnic groups are affected by gambling behaviors, yet discourse, which considers sociocultural factors within PG, remains limited. Literature on this topic is influenced by large inconsistencies amongst research studies, overlapping terminology, and variability in the validity of research studies, making conclusions difficult to draw. Despite these discrepancies, this paper explores how sociocultural factors influence PG among diverse cultural groups and provides specific practical implications for treating PG disorders in culturally diverse populations. Notable factors that exacerbate PG behavior cross-culturally include substance abuse, low socioeconomic status, lack of social activities, and geographic location. Culturally sensitive treatment options such as Motivational Interviewing and Cognitive Behavioral Therapy have been identified as effective treatments for PG with culturally diverse populations. This paper extends current thought by providing practical treatment recommendations that consider the subtle nuances of diverse populations. It explains how treatment providers and behavioral addiction professionals can increase their understanding and skills when conceptualizing PG clients from a diverse background.     

A fortran implementation of univariate fourier series density estimation

A set of Fortran-77 subroutines is described which compute a nonparametric density estimator expressed as a Fourier series. In addition, a subroutine is given for the estimation of a cumulative distribution. Performance measures are given based on samples from a Weibull distribution. Due to small size and modest space demands, these subroutines are easily implemented on most small computers.     

z Test for the significance of the mean of a stable probability distribution with 1<α≤2

The analysis of data using a stable probability distribution with tail parameter α<2 (sometimes called a Pareto–Levy distribution) seems to have been avoided in the past in part because of the lack of a significance test for the mean, even though it appears to be the correct distribution to use for describing returns in the financial markets. A z test for the significance of the mean of a stable distribution with tail parameter 1<α≤2 is defined. Tables are calculated and displayed for the 5% and 1% significance levels for a range of tail and skew parameters α and β. Through the use of maximum likelihood estimates, the test becomes a practical tool even when α and β are not that accurately determined. As an example, the z test is applied to the daily closing prices for the Dow Jones Industrial average from 2 January 1940 to 19 March 2010.