School for Advanced Studies in the Social Sciences

About: School for Advanced Studies in the Social Sciences is a facility organization based out in Villejuif, France. It is known for research contribution in the topics: Politics & Context (language use). The organization has 1230 authors who have published 2084 publications receiving 57740 citations. The organization is also known as: Ecole des Hautes Etudes en Sciences Sociales & EHESS.

Social information use and social information waste.

Abstract: Social information is immensely valuable. Yet we waste it. The information we get from observing other humans and from communicating with them is a cheap and reliable informational resource. It is considered the backbone of human cultural evolution. Theories and models focused on the evolution of social learning show the great adaptive benefits of evolving cognitive tools to process it. In spite of this, human adults in the experimental literature use social information quite inefficiently: they do not take it sufficiently into account. A comprehensive review of the literature on five experimental tasks documented 45 studies showing social information waste, and four studies showing social information being over-used. These studies cover 'egocentric discounting' phenomena as studied by social psychology, but also include experimental social learning studies. Social information waste means that human adults fail to give social information its optimal weight. Both proximal explanations and accounts derived from evolutionary theory leave crucial aspects of the phenomenon unaccounted for: egocentric discounting is a pervasive effect that no single unifying explanation fully captures. Cultural evolutionary theory's insistence on the power and benefits of social influence is to be balanced against this phenomenon. This article is part of the theme issue 'Foundations of cultural evolution'.

Weakly Supervised Multi-Embeddings Learning of Acoustic Models

Abstract: We trained a Siamese network with multi-task same/different information on a speech dataset, and found that it was possible to share a network for both tasks without a loss in performance. The first task was to discriminate between two same or different words, and the second was to discriminate between two same or different talkers.