Dynamates: Dynamic auditory predictions in human and non-human primates

Imagine yourself navigating through busy traffic, with cars, bikes and pedestrians moving around you in all directions. In this and many other situations, one of the crucial aspects for survival is accurately determining when and where external events are appearing, and whether and how things move. To allow both fast and accurate responses to external events, the brain continuously generates predictions about the future. For example, your brain predicts where a car will be by the time you want to cross the road. Yet, humans are not the only species for whom it is beneficial to predict the location and timing of sounds. Other primates might share these abilities, as they also need to navigate dense forest areas. To which degree recent evolutionary pressures have shaped human abilities in these contexts is unclear today. Furthermore, ambiguities in the sensory environment, due to noise or uncertainty about the source properties, require the brain to generate several co-existing predictions and make decisions among them. To date, most of the existing knowledge about perceptual decision making comes from studying visual tasks. However, very little is known regarding this process in the auditory modality, which serves at least as important functions for survival and social behaviour. The YIRG Dynamates aims to address those current key challenges in auditory cognition, by empirically testing and modelling the sensory prediction mechanisms in comparison across evolutionarily closely related species and in realistic but highly controllable virtual acoustic environments. Importantly, in humans, these mechanisms will also be investigated on a physiological level by means of high-density electroencephalography (EEG) allowing to examine whether the resulting model complies with biological restrictions on neural computation. Hence, this project rests on a strong collaboration between cognitive neuroscience, cognitive biology, and computational modelling. To this end, Dynamates comprises an interdisciplinary research team consisting of experts in the fields of computational neuroscience and psychoacoustics (Robert Baumgartner), human EEG and sensory processing (Ulrich Pomper), and comparative cognition between animal species (Michelle Spierings). Dynamates will conduct the first systematic comparative study on dynamic auditory predictions in space and time by both human and non-human primates. The findings of this project will broaden our understanding of the underlying neural mechanisms in humans, which may help future efforts to improve treatments for individuals with impairments or pathological biases in decision making. Moreover, the resulting computational model may stimulate further research by testable predictions of decision making across species as well as in more complex economic and social contexts, and it may be directly applied to improve artificial intelligence and virtual reality systems. 1

How do we adapt our perception when the world around us suddenly changes? Our brain constantly makes predictions about what will happen next, based on prior experience. When incoming sensory information deviates from these expectations, we experience a prediction error-but interpreting such errors is difficult in a noisy and changing environment. The interdisciplinary Young Independent Researcher Group DYNAMATES investigated how humans and marmoset monkeys update their beliefs about the world when faced with uncertainty, focusing on the auditory domain, where this process is less understood than in vision. In a series of behavioral and neurophysiological experiments, the researchers asked participants to interpret noisy sound sequences that occasionally changed their underlying rules-for example, by suddenly changing direction or tempo. They combined computational modeling with electroencephalography (measuring neural activity) and pupil size recordings to uncover how the brain flexibly balances prior expectations and new sensory evidence. Across studies, the team found clear signs of so called Bayesian inference in perception. Participants integrated prior knowledge with sensory input to improve accuracy when the environment was stable, but they reduced their reliance on prior information once unexpected changes occurred. Their behavior closely matched predictions of simplified Bayesian models, in which the brain continuously weighs the probability that an apparent error reflects either random noise or a true change in the environment. Neurophysiological data revealed distinct brain activity patterns linked to two key factors: the uncertainty of prior beliefs and the level of surprisal-how unexpected a new event is. These signals predicted momentary variations in localization accuracy, response confidence, and perceptual bias. Similarly, changes in pupil size-an indicator of arousal-reflected the degree of belief updating and information gain, supporting a link between perceptual adaptation and the brain's noradrenaline system. Taken together, the results of DYNAMATES show that the brain applies flexible, probabilistic strategies to interpret the sensory world. Even at the level of basic perception, we behave as adaptive observers who balance stability and change: relying on prior experience when it is useful, but rapidly adjusting when it no longer applies. This work provides a computational and neural foundation for understanding how we maintain perceptual stability in a dynamic and unpredictable environment.