Employment discrimination against transgender people and self-esteem.

The evocation of gender identity in company anti-discrimination policies is still very rare. This observation is also true forscientific studies. Very few researches have focused exclusively on transgender employees. Transgender are neither sick nor lesscompetent, and yet, the feeling of being strongly discriminated is shared by many transgender people. Such discrimination and thetype of causal attribution do not remain without any effect on the well-being of the concerned individuals. According to Crocker &Quinn (1998), the attribution of the discrimination to the existing prejudices may be a way to protect one-self from the negativeimpact on self-esteem. In this theoretical scope, the "rejection-identification" model (Branscombe, Schmitt & Harvey, 1999) has beenhighly mobilized. It emphasizes the importance of ingroup identification in the causal relationship between perceived discriminationsituation and well-being. Previous studies which did test this model show that the identification to a certain group can counteract thenegative effects on well-being. Following this theoretical frame, the presented study examines the impact of different types of causalattributions on self-esteem: internal causes (e.g. lack of skills), external causes (e.g. economic crisis), and gender identity relatedissues. For that purpose, an online survey has been created and fulfilled by 110 transgender people. Different scales were used to testthe model: the Rosenberg self-esteem scale, a causal attribution scale, the perceived discrimination of the transgender population inthe workplace scale and a group identification scale. The results show that transgender people feel still highly stigmatized today andattribute, significantly, the causes of their situation to the prejudices they are victim of. Also, in accordance with the “rejectionidentification”model, three links are observed: (1) a negative link between perceived discrimination and self-esteem; (2) a positivelink between perceived discrimination and ingroup identification; and (3) a positive link between ingroup identification and selfesteem.This situation reflects a lack in diversity considerations. Nevertheless, the attribution made to group stigmatization seems toplay a protective role towards transgender people self-esteem.

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.