The article proposes a novel analysis of NPN constructions, exemplified by English expressions like back to back and year after year. An NPN is typically composed of two identical bare singular count nouns with a preposition between them. Previous research tends to treat NPNs as highly idiosyncratic. While acknowledging some idiosyncrasies, the present contribution shows that NPNs exhibit a considerable degree of regularity and compositionality. A widespread view that bare singulars normally do not function as arguments is shown to rest on weak foundations. As a consequence, the present approach is able to show that NPNs are, at the core, NPs with PP modifiers. Nominal NPNs have this basic structure, while adverbial NPNs involve an extra layer of semantics and are exocentric constructions. A distinction between nominal types and instances is employed to account for the semantics of bare singulars. NPNs exhibit two kinds of emergent meanings, leading to chain NPNs and twin NPNs. The different semantic structures of these NPN subtypes explain why some NPNs can have nominal in addition to adverbial functions. The data comes mostly from Norwegian. Details differ between languages, but central parts of the analyses can be assumed to hold for other languages as well.

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.