A number of ways of taxonomizing human learning have been proposed. We examine the evidence for one such proposal, namely, that there exist independent explicit and implicit learning systems. This combines two further distinctions, (1) between learning that takes place with versus without concurrent awareness, and (2) between learning that involves the encoding of instances (or fragments) versus the induction of abstract rules or hypotheses. Implicit learning is assumed to involve unconscious rule learning. We examine the evidence for implicit learning derived from subliminal learning, conditioning, artificial grammar learning, instrumental learning, and reaction times in sequence learning. We conclude that unconscious learning has not been satisfactorily established in any of these areas. The assumption that learning in some of these tasks (e.g., artificial grammar learning) is predominantly based on rule abstraction is questionable. When subjects cannot report the “implicitly learned” rules that govern stimulus selection, this is often because their knowledge consists of instances or fragments of the training stimuli rather than rules. In contrast to the distinction between conscious and unconscious learning, the distinction between instance and rule learning is a sound and meaningful way of taxonomizing human learning. We discuss various computational models of these two forms of learning.
The high-flow nasal oxygen-delivery system improves oxygenation saturation, decreases the risk of desaturation during the procedure, and potentially, optimizes conditions for awake fibre-optic intubation. The soft nasal cannulae uniquely allow continuous oxygenation and simultaneous passage of the fibrescope and tracheal tube. The safety of the procedure may be increased, because any obstruction, hypoventilation, or periods of apnoea that may arise may be tolerated for longer, allowing more time to achieve ventilation in an optimally oxygenated patient.
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