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)

In recent decades it has become increasingly clear that a substantial amount of cognitive work goes on independent of consciousness. The research has been carried out largely under two rubrics, implicit learning and implicit memory. The former has been concerned primarily with the acquisition of knowledge independent of awareness and the latter with the manner in which memories not readily available to conscious recall or recognition play a role in behavior; collectively these operations comprise the essential functions of the cognitive (...) unconscious. This paper reviews the recent history of work on these issues, identifies some of the problems confronting researchers, and introduces a theoretical framework based on principles of evolutionary biology within which to view them. The argument is that the cognitive unconscious, despite its apparent sophistication, is of considerable evolutionary antiquity and antedates conscious cognitive systems by a considerable amount of time. Various entailments of this evolutionary perspective are explored including such issues as phylogeny and ontogeny of implicit processes, the robustness of the implicit functions as displayed by the capacity to resist disruptions from psychological and neurological disorders, the relationship between implicit cognition and intelligence, and individual differences in implicit cognitive abilities. (shrink)

I examine the phenomenon of implicit learning, the process by which knowledge about the rule-governed complexities of the stimulus environment is acquired independently of conscious attempts to do so. Our research with the two seemingly disparate experimental paradigms of synthetic grammar learning and probability learning, is reviewed and integrated with other approaches to the general problem of unconscious cognition. The conclusions reached are as follows: Implicit learning produces a tacit knowledge base that is abstract and representative of the structure of (...) the environment; such knowledge is optimally acquired independently of conscious efforts to learn; and it can be used implicitly to solve problems and make accurate decisions about novel stimulus circumstances. Various epistemological issues and related problems such as intuition, neuroclinical disorders of learning and memory, and the relationship of evolutionary processes to cognitive science are also discussed. (shrink)

A modified version of the mere exposure effect paradigm was utilized in an implicit artificial grammar learning task in an attempt to develop a procedure that would be more sensitive in assesing nonconscious learning processes than the methods currently utilized within the field of implicit learning. Subjects were presented with stimuli generated from a finite-state artificial grammar and then had to either decide if novel items conformed to the rule structure of the grammar or rate the degree to which they (...) liked novel items. Because the latter of the task was more indirect of the two procedures, subjects′ availability to discriminate between well-formed and ill-formed items on this liking task was taken as a more sensitive piece of evidence of implicit learning, compared to performance on the rule conformity task. A subsequent test of subjects′ explicit knowledge of the rules of the grammar showed that subjects do have some conscious knowledge of the artificial grammar, but subjects making liking decisions exhibited significantly less conscious rule knowledge than subjects initially making rule-conformity judgements. The findings are discussed in relation to synergistic relationships among implicit perception, implicit memory, and implicit learning proceses. (shrink)

Nissen and Bullemer's serial reaction time task has proven to be a useful model task for exploring implicit sequence learning. Neuropsychological research indicates that SRT learning may depend on the integrity of the basal ganglia, but not on medial temporal and diencephalic structures that are crucial for explicit learning. Recent neuroimaging research demonstrates that motor cortical areas , prefrontal, and parietal cortex also may be involved. This paper reviews this neuropsychological and neuroimaging research, but finds it lacking specific links between (...) structure and function. In order to promote better functional hypotheses, the second part of the paper examines the function of these brain areas from a broader perspective. Neuroimaging and neuropsychological research with human subjects, as well as neurophysiological and lesion research with animals, suggests a number component operations that these brain mechanisms may contribute to learning in the SRT task. (shrink)

The thesis of this article is that implicit memory does not exist. Implicit memory.