MAR 15, 2018 10:30 AM PDT

Investigating Consciousness with Closed-Loop Neural Reinforcement

Presented at: Neuroscience 2018
Speaker
  • Researcher, ATR Institute International
    Biography
      Aurelio received his MSc degree from the Life Sciences and Technologies faculty at EPFL in Switzerland (2012), while spending one year at the Max-Planck Institute of Psychiatry in Munich working on mice models of Alzheimer's disease and depression. He then moved to Japan, where he obtained his PhD (2016) under the joint supervision of Mitsuo Kawato at ATR (Kyoto, Japan) and Hakwan Lau at UCLA (CA, USA). Since then he has worked on confidence and awareness using a combination of beahvioral modeling, machine learning and multivariate aproaches to functional magnetic resonance imaging. Throughout, he has collaborated with Kaoru Amano at the Center for Information and Neural Networks (Osaka, Japan). He is now a researcher at ATR working with Mitsuo Kawato, with ongoing collaborations with Hakwan Lau's Lab at UCLA and Hong Kong University, and with Benedetto de Martino at UCL/Wellcome Trust (London, UK).

    Abstract

    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.


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