Sensory entrainment: Neurobiological
underpinnings for accident
prevention.
Over the course of the last 30 years, a large body of experimental and
theoretical literature has suggested that perception is a discrete
phenomenon \citep{VanRullen_2003} (Marti., 2017; VanRullen., 2016; VanRullen and Koch, 2003;
Asplund et al., 2014; Thibault., et al 2016; Varela et al., 1981; Seth
et al., 2008; Sergent & Dehaene., 2004; Friston., 2005; Friston,
Daunizeau, Kilner, & Kiebel, 2010). This is, human perception is based
on discrete neural processing cycles rather than a continuous stream of
accumulating information. Such processing cycles have been often
associated with brain rhythms; short-lived voltage variations observable
at the level of microcircuits and large-scale neural networks emerging
from the apparently stochastic behavior of individual neurons (Buzsáki,
2006). Such “coordinated” brain activity is frequently associated
with rhythmic fluctuations in the excitation–inhibition cycle of local
neuronal populations, modulating the global organization of our nervous
system (Herter, 1967; Hirsh & Serrick, 1961; Varela et al., 1981; Le
van quyen & Bragin 2007; Le van quyen 2011; VanRullen et al., 2011;
Varela et al., 2001) and generating variability in sensory and
perceptual processing (Varela et al., 1981). Thus, to form a coherent
representation of the world around us, the brain must dynamically
organize the different sensory features composing our global perception
(Varela et al., 2001; Hasson et al., 2008; Le van queyen, 2001;
Bagdasaryan & Le van queyen, 2013). The nervous system’s ability to
continuously couple with dynamic visual inputs depends on the
synchronous neural entrainment to the rhythm of incoming stimuli (Spaak
et al., 2012; Spaak et al., 2014). Improved perceptual processing and
increased behavioral performance can be reached through “optimal”
brain rhythm coupling mechanisms (Ronconi & Melcher, 2017; Arnal &
Kleinschmidt, 2017; Henry & Obleser 2012) and in this manner, taking
advantage of the entrainment property of endogenous neural oscillations
involving sensory processing reorganization. However, practical applications of these new and interesting findings are lacking. Such applications could facilitate the development of novel ecological tools supporting human
sensory adaptation in everyday complex environments.
Within this context, automobile driving can be understood as a complex
task requiring sensory information processing, visual perception
integration, and decision making under conditions of high perceptual
uncertainty. In natural driving conditions, a busy road with incoming
traffic is a common scenario. Eye fixations will concentrate on the road
ahead, while periodically fixating towards the rear-view mirror
sets (Nunes & Recarte, 2002). Moreover, covert attention will be
deployed towards the incoming traffic and potential pedestrians;
shifting between unattended visual fields. Thus, driver performance
studies are a well-suited model for studying neural mechanisms allowing
the identification and processing of diverse dynamic stimuli.
The fields of Cognitive Psychology and -more recently- Cognitive
Neuroscience has produced a variety of research tools composed by
experimental and computational techniques. These methodological tools
allow the identification and modulation of oscillatory brain activity;
having direct impact on sensory, perceptual, and behavioral processes
(Wutz, Weiz, Braun & Melcher, 2014; Thut, Schyns & Gross, 2011). Likewise, a growing
number of studies provide key insights about psychophysiological factors and their impact on driving performance (Klinestiver, 1980; Mehler, Reimer,
Couhling & Dusek, 2009; Thiffault & Bergeron, 2003; Lal & Craig,
2001). These studies focus on quantifying the impact on driving
performance of cellphone calls (Nunes and Recarte, 2002), texting while
driving (Caird et al., 2014), conversations with passengers (Drews et
al., 2008), spontaneous episodes of breakdown of attentional stream
(i.e. mind wandering) (He, Becic, Lee & McCarey, 2014; Yanko & Spalek,
2014; Thomson, Seli, Besner & Smilek, 2014; Henriquez, Chica, Billeke
& Bartolomeo, 2016 ), among others factors. Additionally, other human
demographical and historical factors such as age and driver accident
records are variables that also impact overall driving
performance (Horberry et al., 2006; Ball et al., 1993). Thus, growing
evidence indicate these distractions are associated with reduction of
reaction times, attentional impairments such as “tunnel vision”,
limiting peripheral vision, and -of course- increasing the overall risk
of accidents (Alexander et al., 2004; Robb et al., 2008; Petridou and
Moustaki, 2000). Although relevant evidence has been gathered about
built environment, human, and cognitive factors predicting automobile
accidents, a unifying neurobiological model providing clear empirical
hypothesis is still lacking.
In the present perspective article we will highlight the role of perceptual cycles and
sensory entrainment processes as an integrative and ecological
neurobiological model of driver’s cognitive and behavioral performance.
Our main hypothesis is that external modulation of visual sensory
processing of incoming stimuli, would improve the perception and
behavior (i.e: sensory-motor coupling) during automobile driving. If
true, our hypothesis suggests the possibility to set a driver’s sensory
system within a range of “optimal perceptual state”, allowing
processing external sensory demands successfully. Furthermore, we
suggest cognitive neuroscience should work towards establishing a
relationship between driving performance and external sensory
entrainment of neural oscillations. Establishing such a link would allow
the implementation of real-time information delivery systems working
alongside innovative probabilistic collision prediction applications
indicating collisions and road hazards (Basso et al., 2018). Providing
thus a starting point for the development of perceptually-driven interactive vial
infrastructure and other empirically-designed applications for car accident prevention.