Humans can swiftly learn to recognize visual objects after just one or a few exposures. A striking example of rapid learning is the sudden recognition of a degraded black-and-white image of an object (Mooney image). These degraded Mooney images are initially unrecognizable. However, Mooney images become easily interpretable after a brief exposure to the original intact version of the image. This rapid learning process necessitates the formation of enduring neural signatures to enable subsequent recognition. Despite extensive behavioral characterization, the neuronal mechanisms underlying perceptual changes induced by rapid learning in the human brain are not well understood. Here, we recorded the spiking activity of neurons in medial occipital and temporal regions of the human brain in patients performing an image recognition task that involved rapid learning of degraded two-tone Mooney images. Neurons in the occipital cortex (OC) and medial temporal lobe (MTL) modulated their firing patterns to encode the identity of recently learned images. Population decoding revealed that occipital neurons resolved the identity of learned images at the cost of additional processing time, with delayed responses observed in MTL neurons. Our findings suggest that OC may not rely on feedback from MTL to support recognition following rapid perceptual learning. Instead, learning-induced dynamics observed in OC may reflect extensive recurrent processing, potentially involving top-down feedback from higher-order cortical areas, before signals reach the MTL. These results highlight the need for further computation beyond bottom-up visual input representations to facilitate recognition after learning and provide spatiotemporal constraints for computational models incorporating such recurrent mechanisms.

More information:

• https://brain.harvard.edu/hbi_connectome/marcelo-armendariz/