A Blueprint for the Hard Problem of Consciousness

A Blueprint for the Hard Problem of Consciousness

A Blueprint for the Hard Problem of Consciousness addresses the fundamental mechanism that allows physical events to transcend into subjective experiences, termed the Hard Problem of Consciousness. ...
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Attention, Neuronal Oscillations and Predictive Coding

Pp. 113-131 (19)

Paulo J. Negro

Abstract

Theories of consciousness share the idea of consciousness realization in neuronal states. Conscious experiences take place in embodied agents that generate an internal model of the external environment and of themselves. Artificial intelligence researchers have focused their efforts on the creation of a multi-level consciousnessbased architecture that follows these precepts. Attention mechanisms can serve as control models on silicon environments with limited resources for information processing. Conversely, attention can emerge when information-processing modules cooperate to solve a given problem. However, computationally responding to a topic does not equal conscious processing. Top-down attention and consciousness are conflated, but not the same. Phenomenal consciousness seems possible with activation of local networks, but it is fully realized only after global ignition. Conscious experiences have their own abstraction level. However, they depend on the dynamic cross coupling of neuronal oscillations across processing levels. Predictive Coding ties efferent copies of corollary discharges to the experience of agency. A system based on monitoring action-based signals may underlie both ownership and awareness. An efferent copy is an extra copy of a given command from a control region. It informs local processors of intentions, allowing these downstream processors to timely match intention with the actual results of motor commands. This voluntary motor model applies to abstract experiences. A generative model depends on actions. It maps hidden causes to sensory consequences of these actions. Perception corresponds to the inverse mapping from sensations to hidden causes. A subject may be inferred from inverse mapping of the hidden causes that underlie seen or unseen globally integrated actions. The subject represents the narrative or scenario that predicts sequences of events. However, caution should be applied against strict localizationism in the interpretation of this model.

Keywords:

Access Consciousness, Adaptive Resonance Theory, Artificial Intelligence, Attentional Models, Being the Subject as Inference, Bottom-Up Awareness, Consciousness-Based Architecture, Corollary Discharge, Cross- Frequency Coupling, Emotional Tagging, Feelings of Knowing, Generative Model, Global Workspace, Labeled-Line Limitations, Localizationism, Machine Consciousness, Neuronal Oscillations Summary, Phenomenal Consciousness, Primary Visual Cortex, Predictive Coding, Self-Modeling, Systems 1 and 2, Top- Down Amplification, Variable Q.

Affiliation:

Assistant Clinical Professor of Psychiatry and Behavioral Sciences at the George Washington School of Medicine and Health Sciences.