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)

This is my first publication of the distinction between phenomenal consciousness and access consciousness, though not using quite those terms. It ends with this: "The upshot is this: If Searle is using the access sense of "consciousness," his argument doesn't get to first base. If, as is more likely, he intends the what-it-is-like sense, his argument depends on assumptions about issues that the cognitivist is bound to regard as deeply unsettled empirical questions." Searle replies: "He refers to what he calls (...) an "access sense of consciousness." On my account there is no such sense.". (shrink)

What, in essence, characterizes the mind? According to Searle, the potential to be conscious provides the only definitive criterion. Thus, conscious states are unquestionably "mental"; "shallow unconscious" states are also "mental" by virtue of their capacity to be conscious (at least in principle); but there are no "deep unconscious mental states" - i.e. those rules and procedures without access to consciousness, inferred by cognitive science to characterize the operations of the unconscious mind are not mental at all. Indeed, according to (...) Searle, they have no ontological status - they are simply ways of describing some interesting facets of purely physiological phenomena. (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)

Cognitive science typically postulates unconscious mental phenomena, computational or otherwise, to explain cognitive capacities. The mental phenomena in question are supposed to be inaccessible in principle to consciousness. I try to show that this is a mistake, because all unconscious intentionality must be accessible in principle to consciousness; we have no notion of intrinsic intentionality except in terms of its accessibility to consciousness. I call this claim the The argument for it proceeds in six steps. The essential point is that (...) intrinsic intentionality has aspectual shape: Our mental representations represent the world under specific aspects, and these aspectual features are essential to a mental state's being the state that it is. (shrink)

This paper considers different subjective measures of conscious and unconscious knowledge in a concept formation paradigm. In particular, free verbal reports are compared with two subjective measures, the zero-correlation and the guessing criteria, based on trial-by-trial confidence ratings (a type of on-line verbal report). Despite the fact that free verbal reports are frequently dismissed as being insensitive measures of conscious knowledge, a considerable bulk of research on implicit learning has traditionally relied on this measure of consciousness, because it is widely (...) regarded as almost self-evident that the content of any conscious state that is intentional and conceptual can be expressed verbally. However, we found that the most recently developed subjective measures based on trial-by-trial confidence ratings provided a more sensitive measure of conscious and unconscious knowledge than free verbal reports. In a complementary way, the qualitative pattern of the free report and the confidence measures were similar, providing further evidence for the validity of the latter. (shrink)

It is argued that the cognitive revolution provided general support for the view that associative learning requires cognitive processing, but only limited support for the view that it requires conscious processing. The point is illustrated by two studies of associative learning that played an important role in the development of the cognitive revolution, but which are surprisingly neglected by Mitchell et al. in the target article.