In consciousness studies, a longstanding controversy concerns whether activity in the prefrontal cortical (PFC) region of the brain is necessary to evoke conscious experiences. Similarly, there is contrasting evidence on whether subjective confidence directly reflects sensory evidence or may depend on a late-stage estimation, related to consciousness but dissociable from sensory processes. As of yet, in humans, experimental tools have lacked the power to resolve these issues convincingly.
We overcome this difficulty by capitalizing on the recently developed method of decoded neurofeedback (DecNef), where the occurrence of distinct neural events (e.g., spatial activation patterns) is selectively rewarded. This closed-loop training thus has the power of reinforcing purely content-specific processes that typically lie below consciousness.
In a series of recent studies, we employed DecNef to directly reinforce neural activation patterns in areas related to representations at different levels of complexity, from simpler (e.g. orientation in visual cortex) to more composite (e.g. confidence in PFC). In all cases, the manipulations resulted in clear behavioral or physiological changes. Nevertheless, during the training sessions, participants were never conscious about the content of these localized recurring activation patterns.
This raises a very interesting point: it is likely that consciousness requires more than just a local representation, however well defined. As already proposed, consciousness may rely on concomitant activations across frontoparietal networks. Frontal cortices could thus play a crucial role in bringing content to consciousness, by virtue of being also implicated in higher order representations. Approaches combining machine learning techniques with brain imaging and closed-loop training such as DecNef may offer a strong paradigm to further explore and understand consciousness and its real neural basis.