The paper describes the EXIST 2026 Lab on sexism identification in social networks, which is planned to take place at the CLEF 2026 conference and represents the sixth edition of the EXIST challenge. The lab comprises six tasks addressing three core problems—sexism identification, source intention detection, and sexism categorization—across two types of multimodal data: memes (image and text) and TikTok videos (video and text). Both the meme-based and video-based tasks are multilingual, covering English and Spanish. This multimodal and multilingual setup enables the analysis of how sexist content manifests across different media formats and interaction contexts, while also providing deeper insight into the social and communicative dynamics that shape the production and interpretation of sexist discourse online. As in previous EXIST editions, the datasets will include annotations from multiple annotators, showing different or even conflicting opinions. This helps models learn from diverse perspectives. The novelty of the 2026 edition lies in the enrichment of the dataset with physiological and neurophysiological signals – specifically, heart rate, eye tracking, and electroencephalogram (EEG) data collected from different subjects while viewing the materials, that will be provided as training material. The aim is twofold: to explore how implicit emotional and cognitive responses correlate with the perception of sexist content, and to evaluate whether these signals can improve the automatic detection and classification of sexism. By incorporating features derived from users’ unconscious reactions, machine learning models may capture subtle cues of bias or discomfort that are not evident in textual or visual content alone, leading to more accurate and human-aligned systems for identifying sexism across media.