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, between learning that takes place with versus without concurrent awareness, and between learning that involves the encoding of instances 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 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. (shrink)

Since the inception of the Minimalist Program, Noam Chomsky has increasingly pushed the boundaries of a minimalist conception of grammar, one that pares the mechanisms specific to language down to the bare bones. In addition to the structure‐building operation of Merge, grammar involves some mechanism for feature interaction. Chomsky has explored several different varieties of a mechanism AGREE responsible for these kinds of feature interactions, for example feature matching, feature valuation, and feature movement, as discussed in this chapter. The Minimalist (...) Program leads to the question of whether Merge could be sufficient for narrow syntax. Actual implementations of even simple grammatical descriptions of real natural languages always appear to require more than Merge, but this chapter explores some of the ways in which various different mechanisms might be reduced to Merge, in the spirit of the Minimalist Program. (shrink)

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 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. (shrink)

We present a processing model that integrates same important psychological claims about the human sentence‐parsing mechanism: namely, that processing is influenced by limitations an working memory and by various syntactic preferences. The model uses time‐constraint information to resolve conflicting preferences in a psychologically plausible way. The starting paint far this proposal is the Sausage Machine model (Fodor & Frazier, 1980: Frazier & Fodor, 1978). From there, we attempt to overcome the original model's dependence an ad hoc aspects of its grammar, (...) and its omission of verb‐frame preferences. We also add mechanisms far lexical disambiguation and semantic processing in parallel with syntactic processing. (shrink)

We present a processing model that integrates same important psychological claims about the human sentence‐parsing mechanism: namely, that processing is influenced by limitations an working memory and by various syntactic preferences. The model uses time‐constraint information to resolve conflicting preferences in a psychologically plausible way. The starting paint far this proposal is the Sausage Machine model (Fodor & Frazier, 1980: Frazier & Fodor, 1978). From there, we attempt to overcome the original model's dependence an ad hoc aspects of its grammar, (...) and its omission of verb‐frame preferences. We also add mechanisms far lexical disambiguation and semantic processing in parallel with syntactic processing. (shrink)

Arguments from the Logical Problem of Language Acquisition suggest that since linguistic experience provides few negative data that would falsify overgeneral grammatical hypotheses, innate knowledge of the principles of Universal Grammar must constrain learners hypothesis formulation. Although this argument indicates a need for domain-specific constraints, it does not support their innateness. Learning from mostly positive data proceeds unproblematically in virtually all domains. Since not every domain can plausibly be accorded its own special faculty, the probative value of the argument in (...) the linguistic case is dubious. In ignoring the holistic and probablistic nature of theory construction, the argument underestimates the extent to which positive data can supply negative evidence and hence overestimates the intractability of language learning in the absence of a dedicated faculty. While nativism about language remains compelling, the alleged Logical Problem contributes nothing to its plausibility and the emphasis on the Problem in the recent acquisition literature has been a mistake. (shrink)