From genetics to AI: Integrated approaches to decoding human language in the brain
Meeting Announcement
Updates every hour. Last Updated: 20-May-2026 16:15 ET (20-May-2026 20:15 GMT/UTC)
Learning French, reading the latest Andy Weir novel, hanging out with friends for St. Patrick’s Day — language is central to all these everyday activities. Seemingly effortless from childhood, language, it turns out, is quite complex, not constrained to one set of genes or one region in the brain. Cognitive neuroscientists are now using a diverse arsenal of tools, including novel genetic analyses and AI, to gain insights into both healthy and disordered communication across individuals, as will be presented at the annual meeting of the Cognitive Neuroscience Society (CNS) in Vancouver, British Columbia.
A study by the University of Cordoba confirms the job vulnerability of riders in Europe and highlights the need for specific regulations governing their work within the framework of digital platforms.
First study to use crowdsourced comments to assess effects of heat underground. Researchers collected comments from X and Google Reviews published between 2008 and 2024. Study focused on subway systems in Boston, New York and London. As above-ground temperatures rise, below-ground thermal complaints increase. Knowing when people are uncomfortable could inform targeted interventions.
Psychiatric diagnosis still relies on symptom checklists that were never designed to reflect biology. A peer-reviewed invited review published in Brain Medicine now synthesizes recent advances across four converging domains: conceptual frameworks that move beyond categorical labels, molecular and neurobiological biomarkers, digital phenotyping through smartphones and wearable devices, and machine learning approaches capable of integrating these heterogeneous data streams. The review authors, based at the University of Cambridge, argue that combining objective biological measurement with clinical judgment could yield diagnostic subtypes that predict illness trajectory and guide personalized treatment. They also identify formidable barriers, from data scarcity and algorithmic opacity to regulatory fragmentation and the risk of deepening health inequities.