The interplay between social motivation, social experience, and developmental neural specialization for social perception

From birth, infants preferentially attend to human motion, which allows them to learn to interpret other peoples’ facial expressions and mental states. Evidence from adults shows that selectivity of the amygdala and the posterior superior temporal sulcus (pSTS) to biological motion correlates with social network size. Social motivation—one’s desire to orient to the social world, to seek and find reward in social interaction, and to maintain social relationships—may also contribute to neural specialization for biological motion and to social network characteristics. The current study aimed to determine whether neural selectivity for biological motion relates to social network characteristics, and to gain preliminary evidence as to whether social motivation plays a role in this relation. Findings suggest that neural selectivity for biological motion in the pSTS is positively related to social network size in middle childhood and that this relation is moderated by social motivation.

Temporal variations of IEEE 802.11b signal strengths in an in-building environment

Recent popularity of the IEEE 802.11b Wireless Local Area Networks (WLANs) in a host of current-day applications has instigated a suite of research challenges. The 802.11b WLANs are highly reliable and wide spread. In this work, we study the temporal characteristics of RSSI in the real-working environment by conducting a controlled set of experiments. Our results indicate that a significant variability in the RSSI can occur over time. Some of this variability in the RSSI may be due to systematic causes while the other component can be expressed as stochastic noise. We present an analysis of both these aspects of RSSI. We treat the moving average of the RSSI as the systematic causes and the noise as the stochastic causes. We give a reasonable estimate for the moving average to compute the noise accurately. We attribute the changes in the environment such as the movement of people and the noise associated with the NIC circuitry and the network access point as causes for this variability. We find that the results of our analysis are of primary importance to active research areas such as location determination of users in a WLAN. The techniques used in some of the RF-based WLAN location determination systems, exploit the characteristics of the RSSI presented in this work to infer the location of a wireless client in a WLAN. Thus our results form the building blocks for other users of the exact characteristics of the RSSI.