A psychoengineering paradigm for the neurocognitive mechanisms of biofeedback and neurofeedback
Introduction
When children go to school to learn how to read and write, they receive guidance and feedback from their teachers. Through hard work and receptivity to instruction, their cognitive skills will adapt and they will eventually acquire reading and writing skills. This adaptation is crucial to human development and central to the acquisition of what makes us human; tutored interaction plays a key role in culture acquisition. Biofeedback provides a subject with a similar type of training, but instead of acquiring knowledge, the subject acquires self-regulation mechanisms in order to control affective, biological, and/or cognitive skills. Such psychophysiological self-regulation could theoretically extend to the functioning of both the autonomic and the central nervous systems (Prinzel et al., 2001). Common modalities of biofeedback include respiratory, cardiovascular, neuromuscular, skin conductance and temperature, and central nervous system (Khazan, 2013).
Biofeedback can be explicit or implicit information (Dekker and Champion, 2007; Kuikkaniemi et al., 2010, Nacke et al., 2011). In the explicit model, feedback is given to the controller so that the controller can act on the system. This is the most typical case of biofeedback or neurofeedback: the user observes a (generally visual or auditory, less frequently tactile) feedback signal, which is a direct correlate of the biosignal to regulate. For example, the user hears a sound with an amplitude directly proportional to his heart rate, providing him/her with an additional perception to help him/her regulate this biosignal. In implicit biofeedback, the signal is not explicitly presented to the subject, but instead changes some detail(s) of the experimental conditions. For example, a person using a videogame whose content (e.g., changing levels of difficulty or access to bonus items) evolves depending upon his heart rate is receiving implicit feedback; he/she does not know directly that his heart rate has dropped, but he/she experiences indirect effects of this physiological change. The user is not directly aware of his biosignal, but since it changes the behavior of the system he/she is observing, he/she gets implicit access to a correlate of that biosignal. Implicit feedback is used for subtle and indirect interactions (e.g., changing implicitly the game difficulty) rather than to provide information (Dekker and Champion, 2007; Kuikkaniemi et al., 2010). Such indirect biofeedbacks have an effect on motivational variables (Nacke et al., 2010), and are typically used in affective videogames (Gilleade and Dix, 2005). However, note that if the user of an implicit biofeedback starts learning how the system works and thereby gains control over it, implicit biofeedback becomes explicit (Kuikkaniemi et al., 2010).
Biofeedback is also one of the best approaches to the problem of neurophenomenology (Varela et al., 2001). Especially when applied to the brain (neurofeedback), it is a promising new scientific avenue to explore phenomenology and to investigate the self and consciousness (Bagdasaryan and Le Van Quyen, 2013), thereby attempting to solve the so-called hard problem of consciousness (Chalmers, 1995).
Finally, biofeedback holds a prominent position in the transhumanist agenda (Hansell and Grassie, 2011). Transhumanism is an international and intellectual movement that aims to enhance human intellectual, physical, and psychological capacities (Bostrom, 2006). The cybernetics perspective on biofeedback (Anliker, 1977) opens new perspectives about human enhancement, attracting the attention of a growing scientific community.
In order to clearly evaluate the clinical efficacy of biofeedback interventions, the Association for Applied Psychophysiology and Biofeedback and the Society for Neuronal Regulation developed guidelines with five levels of performance (Moss and Gunkelman, 2002): (1) not empirically supported, (2) possibly efficacious, (3) probably efficacious, (4) efficacious, (5) efficacious and specific. In order to reach level 4 and be considered efficacious, a treatment must be replicated in at least two independent studies, the data analysis must not be flawed, the outcome must be evaluated with precise inclusion criteria, and the experimental setting must involve randomized control trials. Level 5 is reached if the treatment satisfies level 4 conditions, and in addition is statistically superior to credible sham therapy, pill, or alternative bona fide treatment in at least two studies. In a review of 41 treatments, urinary incontinence in females was the only biofeedback treatment found to be efficacious and specific (Yucha and Montgomery, 2008). In the same study, biofeedback was deemed efficacious for ten other conditions: anxiety, attention deficit hyperactivity disorder (ADHD), chronic pain, epilepsy, constipation, headache, hypertension, motion sickness, Raynaud’s disease, and temporomandibular disorder. Note that the survey criteria did not require double-blind investigations; consequently, some of the treatments ranked at level 4 may still be biased by placebo effects. In other words, despite several well-conducted studies exist, the effectiveness of biofeedback has not been fully demonstrated yet, due to insufficient evidences. We hope future biofeedback studies will reach higher standards, so they can meet with level 5 condition with double-blind protocols.
Previous studies attempted to describe the cognitive adaptation mechanisms supporting neuro and biofeedback (Sherlin et al., 2011, Bagdasaryan and Le Van Quyen, 2013, Gevensleben et al., 2014, Ros et al., 2014, Micoulaud-Franchi et al., 2015). We believe that the missing keystone to design effective and efficient approaches is a clear and comprehensive model synthesizing the existing medical, neurological, psychological and engineering perspectives. Considering that information processing is impacted by biofeedback, one would expect to see a model—or at least an explanation—of how these processes will adapt. Due to disciplinary barriers, even though these cognitive adaptation processes have been described in the scientific literature, a general model has never been proposed. In the interest of removing those barriers, we will review existing models of biofeedback from biomedical, psychological, brain science, and bioengineering perspectives. We will then synthesize those views and present a general model of the cognitive adaptation mechanisms underlying biofeedback. As was stated by Georges Box, all models are essentially wrong, but some are useful (Box and Draper, 1987). We will prove the usefulness of this model by providing guidelines for proper development of efficient biofeedback and neurofeedback protocols and the means to control key parameters for successful feedback learning.
Section snippets
Biomedical perspective
Psychophysiological self-regulation, also commonly termed biofeedback (biological feedback), can be investigated from a biomedical perspective. In this section we will review the existing models of biofeedback mechanisms from the perspective of biomedical interventions, where the aim is to improve biological variables impaired by dysfunctions (e.g., blood pressure, tension, heart rate variability, etc.). The variable of interest is fed back to the subject as a biosignal that he/she then
Operant conditioning: the reward problem
As we mentioned in Section 2.2, the mechanisms of biofeedback have traditionally been theorized using a behavioral approach inspired by Skinner’s theories of OC (Skinner, 1938, Sherlin et al., 2011) and reinforcement learning (RL). The OC paradigm states that when a behavior has consequences (either rewards or punishments), it will be reinforced or repressed. In the case of biofeedback, the behavior is the regulation of an underlying biological variable, and the reinforcement signal is the
Neural correlates of schemata formation
Straightforward links can be established between schemata theory and functional neuroanatomy (Johnson and Grafton, 2003, Cannon et al., 2008). Schemata correspond closely to biological networks of neurons usually termed “neural assemblies.” A neural assembly is a small set of interconnected neurons that can persist without external stimulus, connected by learning and supported by synchronous firing behavior (Huyck and Passmore, 2013). The “information overlap to abstract” (iOtA) model of Lewis
Process control models of feedback learning
In engineering, process control is a discipline that aims to maintain the output of a process in a certain desired state (Murrill, 2000; Bennett, 1993; Levine, 2010). For example, a thermostat on a heater can turn the heater on or off by comparing the temperature measured by a sensor to a reference temperature. Once the target temperature is reached, the difference between the room temperature and the target temperature is zero, so the thermostat stops the heater. Process control can work in an
The missing keystone: toward a psychoengineering model
In the previous sections, we have explored the various existing models of biofeedback: biomedical, psychological, neuroscience and bioengineering perspectives. We could argue in favor of any of these four perspectives, as each one answers a set of critical questions. However, we believe that a blended model would best describe the mechanisms of biofeedback and produce useful experimental paradigms. This model should represent the perspective of biofeedback itself and bridge the gaps among the
Conclusion
The learning mechanisms involved in biofeedback should be thoroughly investigated, as the existing literature is largely insufficient to understand biofeedback and explain how it works. We conclude thereafter with five directions that ought to be pursued to better investigate these mechanisms and to improve biofeedback and neurofeedback protocols. These guidelines are representative of the existing literature and should not be seen as established laws but rather as future research directions.
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