How well can we estimate immigration trends using Google data?

For a country to efficiently monitor international migration, quick access to information on migration flows is helpful. However, traditional data sources fail to provide immediate information on migration flows and do not facilitate the correct anticipation of these flows in the short term. To tackle this issue, this paper evaluates the predictive capacity of big data to estimate the current level or to predict short-term flows. The results show that Google Trends can provide information that reflects the attractiveness of Switzerland for to immigrants from different countries and predict, to some extent, current and future (short-term) migration flows of adults arriving from Spain or Italy. However, the predictions appear not to be satisfactory for other flows (from France and Germany). Additional studies based on alternative approaches are needed to validate or overturn our study results.

Do Bilingual Infants Possess Enhanced Cognitive Skills?

Prior studies have reported that bilingualism enhancescognitive ability due to the regular conflict management oftwo language systems (Bialystok, 2015). Here, we explorewhether infant bilingualism improves cognitive ability at 9.5months. Twenty-four monolingual English and 23 bilingualFrench-English infants were first trained to predict a rewardon the right based on a set of tone-shape rule structure (AABpattern). Infants were later trained to predict a differentreward on the left based on another set of new rule structure(ABB pattern). Correct anticipation of reward locationsindicates successful learning. If bilingualism improvesinfants’ cognitive skills, bilingual infants would be better atlearning a new pattern-reward association. However, we didnot find evidence that bilinguals looked at the correct locationmore than monolinguals or learned the new pattern-rewardassociation faster. Thus, our results suggest bilingualism maynot enhance cognitive ability at 9.5 months, as least using thecurrent paradigm.

Acquiring the user’s opinion by using a generalized Context-aware Recommender System for real-world applications

Acquiring the user’s opinion on specific things undoubtedly changes according to the given context. A context-aware or Multidimensional Recommender System can adapt its behaviour according to the user’s personal or environmental context. The same user may express or use completely different decision-making ways for various contexts to express the opinion .So, correct anticipation of user need depends upon the amount to which the relevant discourse data is in incorporated within the user’s opinion type. Here, we propose a generalized Context-aware recommender system that is suitable for all applications where a contextual segment plays a major role to find user’s opinion in real-world applications.

Verbal Approval and Anxiety in Serial-Verbal Learning

In two experiments, each using 40 college student Ss, level of manifest anxiety and the presence or absence of verbal approval by E for a correct anticipation were varied. Verbal approval led to a decrement in performance for high-anxiety Ss and superior learning for low-anxiety Ss, whereas without verbal approval either high-anxiety Ss were superior or there was no difference. The motivational effects of verbal approval are viewed as being similar to those of ego-orienting or stress-producing instructions.