In everyday life, we experience an ever-changing environment. To deal with these dynamic changes and adjust our behaviour accordingly, a key function of our brain is to predict future states of the world. Predictive processes play a role in all our senses. In the auditory domain, the ability to form predictions of what we will hear next is fundamental when dealing with the everyday soundscape, as it helps us in segregating different sources in complex auditory scenes, to deal with incomplete information, and to make sense of sounds in noisy scenarios. In addition, aberrant predictive processing is hypothesized to underlie phantom sound perception such as in tinnitus or auditory hallucinations.
To support predictive processes, our brains are thought to infer causes of sensory stimuli by building internal generative models. In the auditory system, these computations are supported by information travelling from the sensory periphery to the cortex - extracting complex information from the acoustic content of sounds - as well as by extensive feedback processing which are hypothesised t convey the current best prediction of the sensory input supported by our internal generative model. The mismatch between the predicted and actual input (i.e. the prediction error) is used to update the generative model and travels from lower processing stages to higher brain areas.
So far, limitations in coverage and spatial resolution of non-invasive imaging methods have prohibited grounding contextual sound processing onto the fundamental computational units of the human brain, resulting in an incomplete understanding of its biological underpinnings. PrAud proposes to use state of the art technology - such as Ultra-High field functional Magnetic Resonance Imaging (UHF-fMRI) - to investigate how small subcortical structures and layers of cortex are involved in predictive hearing and in combination with magnetoencephalography, derive a neurobiological model of contextual sound processing at high spatial and temporal resolution.
