Linking the Bayesian brain, perceptual uncertainly, and cognitive prediction at the wheel

The nervous system’s ability to dynamically coordinate cognitive functions in a world of sensory uncertainty is one of the greatest evolutionarily-given tools \cite{Jensen_2012}. Although from experience, our perceptual world looks stable and determined, many factors indicate that coordination with incoming sensory information is less reliable than it appears \cite{Geisler_2002}. For example, the variability in the density of the receptors of the retina -as well as the structural limitations in neural organization and the variety and variability of information introduced in the early stages of sensory coding- mean that the nervous system must dynamically and effectively deal with the resulting uncertainty. Appropriate uncertainty management generates accurate perceptual representations of the world. Thus guiding decisions and actions in a more adaptive manner. Therefore, perception is a process of active unconscious and probabilistic inference \cite{Friston_2012,Moutoussis_2014,Moutoussis_2014a} (Barlow 1990 --> no lo encuentro).
On the one hand, Bayesian methods have proven successful in describing computational theoretical models for perception and sensory-motor control \cite{Buckley_2017}. On the other hand, psychophysics has provided a growing body of evidence on how perceptual calculations in human beings follow an optimal Bayesian pattern \cite{Buckley_2017}. This means that Bayesian coding approach reverse the notions classically established of perception as a largely bottom-up process of information accumulation or stimulus detection driven by the impact of sensory signals. Suggesting instead that both perceptual content and behavioral response is determined by top-down predictive signals. These signals would arise from multi-level generative internal models of environmental sources, which are continually modified by bottom-up prediction error signals. The latter ones, communicating reciprocal exchanges of information between predicted and actual signals across the different sensory and perceptive hierarchical levels, ultimately impacting on behavioral execution. Thus, evidence indicates the processes by which the human brain represents incoming sensory information, could be understood in a probabilistic way [CITE EVIDENCE]. Thus, the Bayesian coding hypothesis, suggests the human brain could be a natural probability distribution generator (Friston., 2009, 2010). This theoretical and experimental approach has fundamental implications for studying the brain in everyday environments, particularly in the way we understand and describe the neural calculations and natural dynamics of neural representations. However, direct applications in daily life on this hypothesis are almost non-existent. Determining how and which are the requirements of the driver’s brain to coordinate, the dynamics and natural neuronal interactions that are useful for the effectiveness of information coding in environments of high sensory uncertainty, is an important objective not only for the development of neuroergonomics as a field, but also a major advance in the field of applications in complex social contexts.
In consequence, considering the Bayesian brain framework (Friston, 2009, 2010 & 2011), if we provide relevant information to optimize a probability calculation (e.g. the prediction of motor behavior on a highway) our brain would use this information and modify itself from its interaction with incoming stimuli. Thus drawing a probabilistic prediction, with higher success rate of what could happen within the given context. In real high-uncertainty road driving conditions, the drivers’ brain would consider the inputs from the environment and compare them with their previous experiences, in order to regulate and modify their behavior according to perceived risk \cite{wilde1998risk}. In turn, optimization of brain functioning -by integrating the most relevant variables and trying to zero out any errors in its predictive model- can occur in a moment-by-moment basis. Furthermore, optimization involves updating the current model to reevaluate sensory inputs. Selective sampling of new incoming sensory inputs will thus generate adaptive responses reducing uncertainty and facilitating behavioral performance \cite{Friston_2015}. Hence the nervous system is prepared to make active and probabilistic inferences of what might happen in our nearby sensory world. Furthermore, it can update such models with new predictive information, increasing the degree of accuracy. Environmental Psychology can therefore use this framework in order to develop systems that can facilitate such an optimization process. For example, using information derived from state-of-the-art predictive model of accidents in order to provide online non intrusive data about driving conditions \cite{basso2018real}. Given the fact that in real driving scenarios signals and stimuli are in constant competition, transmitting information to drivers is not an easily achievable goal.