Classifying Discipline-Specific Content Using a General-Content Brain-Response Model

A content classification apparatus, comprising:

• an interface, which is configured to:
• receive a set of content items belonging to multiple predefined categories of a specific discipline, and extracting respective features from each content item of the specific discipline;
• and
• receive a labeling of the content items of the specific discipline, performed by one or more human viewers, the labeling indicating, for each content item, a respective category assigned to the content item by the one or more human viewers from among the multiple predefined categories;
• and
• a processor, which is configured to:
• upload a general-content brain-response model estimated using measurements of brains of humans presented with a general-content database, wherein the general-content database is defined using a set of features and comprises a mapping between the set of features and a set of extracted brain activities;
• apply the general-content brain-response model to the extracted features, to calculate, using the labeling, a set of per-category brain-responses for the specific discipline;
• and
• given a new content item associated with the discipline, estimate a category that best matches the new content item from among the multiple predefined categories, based on the general-content brain-response model and the discipline-specific brain responses.