Evidence on consumers' perceptions, understanding and uses of the Nutri-Score to improve communication about its update: a qualitative study with shopping observations in France.

Background: The Nutri-Score is a front-of-pack nutrition label widely used in several European countries to improve the overall quality of consumers' diets. In the view of the upcoming update of the Nutri-Score algorithm, this study evaluates consumers' perceptions, understanding and uses of this label as well as their expectations or criticisms and opinions regarding the algorithm update.

Methods: Two complementary qualitative approaches were used in 2023.

Developmental foundations of Activity-Centered Ergonomics: knowledge encounters to construct both a critical analysis of work and developmental set-ups.

This paper presents the developmental foundation of Activity-Centered Ergonomics (ACE) that has been shaped by both: (1) a critique of conceptions of work and of its organisation in relation to economic and political models of development, and (2) developmental approaches to understand and to act on activity and work situations at different levels (individual, collective, organisational, and territorial). To do so, we examine some key reference works for ACE over 75 years with regards to these two dimensions and the methods and developmental set ups proposed. We argue that ACE analytical and transformative goals give a considerable space to knowledge encounters and workers' activity.

Predicting cognitive load with EEG using Riemannian geometry-based features.

. We show that electroencephalography (EEG)-based cognitive load (CL) prediction using Riemannian geometry features outperforms existing models. The performance is estimated using Riemannian Procrustes Analysis (RPA) with a test set of subjects unseen during training.

The potential of generative AI for personalized persuasion at scale.

Matching the language or content of a message to the psychological profile of its recipient (known as "personalized persuasion") is widely considered to be one of the most effective messaging strategies. We demonstrate that the rapid advances in large language models (LLMs), like ChatGPT, could accelerate this influence by making personalized persuasion scalable. Across four studies (consisting of seven sub-studies; total N = 1788), we show that personalized messages crafted by ChatGPT exhibit significantly more influence than non-personalized messages.