Consciousness remains a formidable challenge. Different theories of consciousness have proposed different mechanisms to account for phenomenal experience. Here, appealing to Global Workspace Theory, Higher-Order Theories, Social Theories, and Predictive Processing, we introduce a novel framework -the Self-Organizing Metarerpresentational Account (SOMA), in which consciousness is viewed as something that the brain learns to do. The brain continuously and unconsciously learns to redescribe its own activity to itself, so developing systems of metarepresentations that qualify target first-order representations. Experiences only occur in experiencers that have learned to know they possess certain first-order states and that have learned to care more about certain states than about others. Thus, consciousness is the brain's (unconscious, embodied, enactive, non-conceptual) theory about itself.

Highlights This article discusses the role of awareness in neurofeedback (NF) learning. Most implicit NF tasks include explicit goals. Explicit processes are central in skill acquisition and goal-directed behavior. Measures of awareness are suggested to go beyond verbal exit questionnaires. Considering awareness is vital for understanding the learning mechanism of NF.

Can people categorize complex visual scenes unconsciously? The possibility of unconscious perception remains controversial. Here, we addressed this question using psychophysical methods applied to unmasked visual stimuli presented for extremely short durations (in the μsec range) by means of a custom-built modern tachistoscope. Our experiment was composed of two phases. In the first phase, natural or urban scenes were either absent or present (for varying durations) on the tachistoscope screen, and participants were simply asked to evaluate their subjective perception using a 3-points scale (absence of stimulus, stimulus detection or stimulus identification). Participants' responses were tracked by means of two staircases. The first psychometric function aimed at defining participants' proportion of subjective detection responses (i.e., not having seen anything vs. having seen something without being able to identify it), while the second staircase tracked the proportion of subjective identification rates (i.e., being unaware of the stimulus' category vs. being aware of it). In the second phase, the same participants performed an objective categorization task in which they had to decide, on each trial, whether the image was a natural vs. an urban scene. A third staircase was used in this phase so as to build a psychometric curve reflecting the objective categorization performance of each participant. In this second phase, participants also rated their subjective perception of each stimulus on every trial, exactly as in the first phase of the experiment. Our main result is that objective categorization performance, here assumed to reflect the contribution of both conscious and unconscious trials, cannot be explained based exclusively on conscious trials. This clearly suggests that the categorization of complex visual scenes is possible even when participants report being unable to consciously perceive the contents of the stimulus.

Neurofeedback allows humans to self-regulate neural activity in specific brain regions and is considered a promising tool for psychiatric interventions. Recently, methods have been developed to use neurofeedback implicitly, prompting a theoretical debate on the role of awareness in neurofeedback learning. We offer a critical review of the role of awareness in neurofeedback learning, with a special focus on recently developed neurofeedback paradigms. We detail differences in instructions and propose a fine-grained categorization of tasks based on the degree of involvement of explicit and implicit processes. Finally, we review the methods used to measure awareness in neurofeedback and propose new candidate measures. We conclude that explicit processes cannot be eschewed in most current implicit tasks that have explicit goals, and suggest ways in which awareness could be better measured in the future. Investigating awareness during learning will help understand the learning mechanisms underlying neurofeedback learning and will help shape future tasks.