Topic: predictive models

AI systems increasingly influence both which images people encounter and predictions of how those images attract attention. This session examines the relationship between automated image selection, predicted visual attention and measured human responses. Peter Steidl presents Visual GEO and a two-stage account of visual communication: an image must first be recognized and selected by a machine before human attention can be measured. Hedda Martina Šola compares machine predictions with human measurements in two published studies using the same material, including a case where an AI-created des

This NIH BRAIN Initiative funding opportunity supports new or substantially advanced theories, mechanistic or predictive models, and computational or statistical methods that improve quantitative understanding of brain function across scales. Tools must address complex neural and behavioral data and be made broadly available to the neuroscience research community.

Natural language is strongly context-dependent and can be perceived through different sensory modalities. For example, humans can easily comprehend the meaning of complex narratives presented through auditory speech, written text, or visual images. To understand how complex language-related information is represented in the human brain there is a necessity to map the different linguistic and non-linguistic information perceived under different modalities across the cerebral cortex. To map this information to the brain, I suggest following a naturalistic approach and observing the human brain p