Colony sex ratios vary with breeding structure but not relatedness asymmetry in the facultatively polygynous ant Pheidole pallidula

We investigated sex allocation in a Mediterranean population of the facultatively polygynous (multiple queen per colony) ant Pheidole pallidula. This species shows a strong split sex ratio, with most colonies producing almost exclusively a single-sex brood. Our genetic (microsatellite) analyses reveal that P. pallidula has an unusual breeding system, with colonies being headed by a single or a few unrelated queens. As expected in such a breeding system, our results show no variation in relatedness asymmetry between monogynous (single queen per colony) and polygynous colonies. Nevertheless, sex allocation was tightly associated with the breeding structure, with monogynous colonies producing a male-biased brood and polygynous colonies almost only females. In addition, sex allocation was closely correlated with colony total sexual productivity. Overall, our data show that when colonies become more productive (and presumably larger) they shift from monogyny to polygyny and from male production to female production, a pattern that has never been reported in social insects.

Sex chromosome abnormalities after intracytoplasmic sperm injection.

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Assessing segmentation processes by click detection: online measure of statistical learning, or simple interference?

Statistical learning can be used to extract the words from continuous speech. Gómez, Bion, and Mehler (Language and Cognitive Processes, 26, 212–223, 2011) proposed an online measure of statistical learning: They superimposed auditory clicks on a continuous artificial speech stream made up of a random succession of trisyllabic nonwords. Participants were instructed to detect these clicks, which could be located either within or between words. The results showed that, over the length of exposure, reaction times (RTs) increased more for within-word than for between-word clicks. This result has been accounted for by means of statistical learning of the between-word boundaries. However, even though statistical learning occurs without an intention to learn, it nevertheless requires attentional resources. Therefore, this process could be affected by a concurrent task such as click detection. In the present study, we evaluated the extent to which the click detection task indeed reflects successful statistical learning. Our results suggest that the emergence of RT differences between within- and between-word click detection is neither systematic nor related to the successful segmentation of the artificial language. Therefore, instead of being an online measure of learning, the click detection task seems to interfere with the extraction of statistical regularities.