Creating Concepts from Converging Features in Human Cortex

To make sense of the world around us, our brain must remember the overlapping features of millions of objects. Crucially, it must also represent each object's unique feature-convergence. Some theories propose that an integration area (or convergence zone) binds together separate features. We report an investigation of our knowledge of objects' features and identity, and the link between them. We used functional magnetic resonance imaging to record neural activity, as humans attempted to detect a cued fruit or vegetable in visual noise. Crucially, we analyzed brain activity before a fruit or vegetable was present, allowing us to interrogate top-down activity. We found that pattern-classification algorithms could be used to decode the detection target's identity in the left anterior temporal lobe (ATL), its shape in lateral occipital cortex, and its color in right V4. A novel decoding-dependency analysis revealed that identity information in left ATL was specifically predicted by the temporal convergence of shape and color codes in early visual regions. People with stronger feature-and-identity dependencies had more similar top-down and bottom-up activity patterns. These results fulfill three key requirements for a neural convergence zone: a convergence result (object identity), ingredients (color and shape), and the link between them.