The energy metabolic footprint of predictive processing in the human brain

Abstract

Our expectations about the world influence how we interpret visual information, improving the speed and accuracy of perception. However, the underlying neural activity requires energy which is strictly limited in the brain. While predictive processing is a prevalent framework to explain perception, it remains unclear whether it also serves energy-efficient processing. Here, we employed metabolic brain imaging to quantify oxygen consumption during visual perception under varying levels of input predictability and subjective uncertainty. For three days, we presented participants with object sequences that were either predictable or unpredictable, and assessed their performance and confidence in predicting follow-up objects from partial sequences. On the fourth day, we first tested for behavioral consequences of predictability. We found that subjects detected predictable objects quicker than unpredictable ones. We then quantified cortical oxygen consumption during passive viewing of predictable, unpredictable or surprising sequences. Despite highly similar sensory load, predictable visual input elicited reduced oxygen metabolism when subjects were confident, across both sensory and higher cognitive areas. Crucially, this summed up to cortical energy savings of up to 12%, or 118 mol oxygen per minute, given average brain size. In contrast, cost increases due to surprising input were restricted to a network of fronto-parietal areas. In summary, we found that predictive processing enhances behavioral performance and notably reduces signaling costs, moderated by subjective confidence. This suggests that examining energy efficiency alongside behavioral performance may uncover novel computational strategies of human cognition and behavior.