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Decoding the future from past experience: learning shapes predictions in early visual cortex.

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Luft, Caroline DB 
Meeson, Alan 
Welchman, Andrew E 


Learning the structure of the environment is critical for interpreting the current scene and predicting upcoming events. However, the brain mechanisms that support our ability to translate knowledge about scene statistics to sensory predictions remain largely unknown. Here we provide evidence that learning of temporal regularities shapes representations in early visual cortex that relate to our ability to predict sensory events. We tested the participants' ability to predict the orientation of a test stimulus after exposure to sequences of leftward- or rightward-oriented gratings. Using fMRI decoding, we identified brain patterns related to the observers' visual predictions rather than stimulus-driven activity. Decoding of predicted orientations following structured sequences was enhanced after training, while decoding of cued orientations following exposure to random sequences did not change. These predictive representations appear to be driven by the same large-scale neural populations that encode actual stimulus orientation and to be specific to the learned sequence structure. Thus our findings provide evidence that learning temporal structures supports our ability to predict future events by reactivating selective sensory representations as early as in primary visual cortex.



fMRI, prediction, sensory processing, visual cortex, visual learning, Adolescent, Cues, Eye Movements, Female, Humans, Image Processing, Computer-Assisted, Learning, Magnetic Resonance Imaging, Male, Orientation, Oxygen, Photic Stimulation, Visual Cortex, Visual Pathways, Young Adult

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J Neurophysiol

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American Physiological Society
Wellcome Trust (095183/Z/10/Z)
Leverhulme Trust (RF-2011-378)
European Commission (290011)
This work was supported by a Wellcome Trust Senior Research Fellowship to AEW (095183/Z/10/Z) and grants to ZK from the Biotechnology and Biological Sciences Research Council [H012508], a Leverhulme Trust Research Fellowship (RF-2011-378) and the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme FP7/2007-2013/ under REA grant agreement no. PITN-GA-2011-290011.