Effects of peer group rejection and a new group's norms on children's intergroup attitudes

This study examined the effects of peer group rejection and a new group's norms on 7‐ and 9‐year old children's intergroup attitudes. Children (N =82) were rejected or accepted by an initial group, then accepted by a new group that had a norm of inclusion versus exclusion towards others. Results showed that rejected compared with accepted children had a more negative attitude towards the initial group, that they were equally positive towards their new group, but that the rejected children were more negative towards an outgroup. Results also revealed an age × target group × group norm effect that indicated that the younger children's attitudes towards the three groups were more negative in the exclusion versus acceptance norm condition. The older children were also more negative towards the initial and new groups in the exclusion condition, but their more positive attitudes towards the outgroup were unaffected by the group norms. The bases of the effects of peer group rejection and group norms are discussed.     

Hacking the Akashic Records: The Next Domain for Military Intelligence Operations?

Abstract

This paper outlines a hypothetical six-dimension doctrine for military intelligence-gathering in the Akashic domain. The Akashic records are described by esotericists and mystics as a permanent record of all thoughts, feelings, and actions, stored in a kind of cosmic memory bank outside of space and time. Psychics, clairvoyants, and other intuitives purport to read the records, suggesting that development of an operational strategy for accessing such information may be possible. Command oversight, however, would present significant moral challenges, as “hacking” into this information would be a personally intrusive invasion of privacy with serious repercussions for the operators and state sponsors.     

Short report: Reaction time analysis with outlier exclusion: Bias varies with sample size

To remove the influence of spuriously long response times, many investigators compute “restricted means”, obtained by throwing out any response time more than 2.0, 2.5, or 3.0 standard deviations from the overall sample average. Because reaction time distributions are skewed, however, the computation of restricted means introduces a bias: the restricted mean underestimates the true average of the population of response times. This problem may be very serious when investigators compare restricted means across conditions with different numbers of observations, because the bias increases with sample size. Simulations show that there is substantial differential bias when comparing conditions with fewer than 10 observations against conditions with more than 20. With strongly skewed distributions and a cutoff of 3.0 standard deviations, differential bias can influence comparisons of conditions with even more observations.