Brazilian mothers` socialization goals: Intracultural differences in seven Brazilian cities

The present study aimed to investigate Brazilian mothers` socialization goals. The participants in the study were 349 primiparous mothers, whose ages ranged from 17 to 47 years (mean = 26.6 years), who had children aged between 1 and 48 months (mean = 16.4 months). The families were living in seven different cities representing each of the five geographical regions of the country. A scale to evaluate the families` socio-economic status and an interview about socialization goals were used. The answers were coded in five categories defined in previous studies: self-maximization, self-control, lovingness, proper demeanor, and decency. Comparison of the means showed that Brazilian mothers gave more emphasis to self-maximization and proper demeanor than to the other categories, presenting a pattern that fosters the development of children`s autonomous-relational selves. The intracultural variation found was related to the different cities studied. GLM results showed main effects of both city size and mothers` educational level on their socialization goals. These findings contribute to the understanding of characteristics of socialization goals related with autonomy and sociocentrism.

Modelling shared attention through relational reinforcement learning

Shared attention is a type of communication very important among human beings. It is sometimes reserved for the more complex form of communication being constituted by a sequence of four steps: mutual gaze, gaze following, imperative pointing and declarative pointing. Some approaches have been proposed in Human-Robot Interaction area to solve part of shared attention process, that is, the most of works proposed try to solve the first two steps. Models based on temporal difference, neural networks, probabilistic and reinforcement learning are methods used in several works. In this article, we are presenting a robotic architecture that provides a robot or agent, the capacity of learning mutual gaze, gaze following and declarative pointing using a robotic head interacting with a caregiver. Three learning methods have been incorporated to this architecture and a comparison of their performance has been done to find the most adequate to be used in real experiment. The learning capabilities of this architecture have been analyzed by observing the robot interacting with the human in a controlled environment. The experimental results show that the robotic head is able to produce appropriate behavior and to learn from sociable interaction.

MR-Radix: a multi-relational data mining algorithm

Abstract Background Once multi-relational approach has emerged as an alternative for analyzing structured data such as relational databases, since they allow applying data mining in multiple tables directly, thus avoiding expensive joining operations and semantic losses, this work proposes an algorithm with multi-relational approach. Methods Aiming to compare traditional approach performance and multi-relational for mining association rules, this paper discusses an empirical study between PatriciaMine - an traditional algorithm - and its corresponding multi-relational proposed, MR-Radix. Results This work showed advantages of the multi-relational approach in performance over several tables, which avoids the high cost for joining operations from multiple tables and semantic losses. The performance provided by the algorithm MR-Radix shows faster than PatriciaMine, despite handling complex multi-relational patterns. The utilized memory indicates a more conservative growth curve for MR-Radix than PatriciaMine, which shows the increase in demand of frequent items in MR-Radix does not result in a significant growth of utilized memory like in PatriciaMine. Conclusion The comparative study between PatriciaMine and MR-Radix confirmed efficacy of the multi-relational approach in data mining process both in terms of execution time and in relation to memory usage. Besides that, the multi-relational proposed algorithm, unlike other algorithms of this approach, is efficient for use in large relational databases.