Humans, like many other species, build models of themselves and their environment, to understand the past and predict the future. These models are constantly evaluated and updated based on new, surprising input. Surprise (i.e. the distance between one’s prior and current beliefs) appears an essential ingredient in various cognitive faculties such as perception, learning, motivation and action and it strongly drives brain activity in both sub-cortical and cortical networks underlying goal-directed behavior. Yet, we currently lack a good understanding of the form and function of surprise signals in the brain.
The overall aim of this research proposal is to elucidate how surprise is computed within sensory circuits and how it influences information seeking behavior. To achieve this, I will: 1) test the theoretical proposition that surprise signals emerge from the discrepancy between prediction signals and input signals that are represented in distinct layers of the neocortex, using ultra-high field neuroimaging in human volunteers; 2) investigate how sensory surprise signals may be communicated to downstream areas to update the brain’s attentional sampling policies; and 3) investigate the relationship between sensory surprise and the explicit drive for information that we call curiosity.
This proposal bridges several levels of analysis (from laminar circuit models that calculate sensory surprise to systems neuroscience and computational models of behavior) and several cognitive domains (perception, attention, motivation and curiosity). This multi-scale and multi-method investigation of surprise signals is critical for a more complete and integrated understanding of what may be one of the most important drivers of cognition and behavior.
Call for proposal
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