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)

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)

This paper uses the Process Dissociation Procedure to explore whether people can acquire unconscious knowledge in the serial reaction time task [Destrebecqz, A., & Cleeremans, A. . Can sequence learning be implicit? New evidence with the Process Dissociation Procedure. Psychonomic Bulletin & Review, 8, 343–350; Wilkinson, L., & Shanks, D. R. . Intentional control and implicit sequence learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 354–369]. Experiment 1 showed that people generated legal sequences above baseline levels under exclusion (...) instructions. Reward moved exclusion performance towards baseline, indicating that the extent of motivation in the test phase influenced the expression of unconscious knowledge. Experiments 2 and 3 revealed that even with reward, adding noise to the sequences or shortening training led to above-baseline exclusion performance, suggesting that task difficulty and the amount of training also affected the expression of unconscious knowledge. The results help resolve some current debates about the role of conscious awareness in sequence learning. (shrink)

An experiment explored the acquisition of conscious and unconscious knowledge of semantic prosody in a second language under incidental and intentional learning conditions. Semantic prosody is the conotational coloring of the semantics of a word, largely uncaptured by dictionary definitions. Contrary to some claims in the literature, we revealed that both conscious and unconscious knowledge were involved in the acquisition of semantic prosody. Intentional learning resulted in similar unconscious but more conscious knowledge than incidental learning. The results are discussed in (...) terms of second language learning and the nature of unconscious knowledge. (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)

referred to as implicit learning (Reber, 1989). Implicit learning contrasts with explicit learning (exhibited for.

While the study of implicit learning is nothing new, the field as a whole has come to embody — over the last decade or so — ongoing questioning about three of the most fundamental debates in the cognitive sciences: The nature of consciousness, the nature of mental representation (in particular the difficult issue of abstraction), and the role of experience in shaping the cognitive system. Our main goal in this chapter is to offer a framework that attempts to integrate current (...) thinking about these three issues in a way that specifically links consciousness with adaptation and learning. Our assumptions about this relationship are rooted in further assumptions about the nature of processing and of representation in cognitive systems. When considered together, we believe that these assumptions offer a new perspective on the relationships between conscious and unconscious processing and on the function of consciousness in cognitive systems. (shrink)

We explore three methods for measuring the conscious status of knowledge using the artificial grammar learning paradigm. We show wagering is no more sensitive to conscious knowledge than simple verbal confidence reports but is affected by risk aversion. When people wager rather than give verbal confidence they are less ready to indicate high confidence. We introduce a “no-loss gambling” method which is insensitive to risk aversion. We show that when people are just as ready to bet on a genuine random (...) process as their own classification decisions, their classifications are still above baseline, indicating knowledge participants are not aware of having. Our results have methodological implications for any study investigating whether people are aware of knowing. (shrink)

Running head: Implicit sequence learning ABSTRACT Can we learn without awareness? Although this issue has been extensively explored through studies of implicit learning, there is currently no agreement about the extent to which knowledge can be acquired and projected onto performance in an unconscious way. The controversy, like that surrounding implicit memory, seems to be at least in part attributable to unquestioned acceptance of the unrealistic assumption that tasks are process-pure, that is, that a given task exclusively involves either implicit (...) or explicit knowledge. (shrink)

This paper considers two subjective measures of the existence of unconscious mental states - the guessing criterion, and the zero correlation criterion - and considers the assumptions underlying their application in experimental paradigms. Using higher order thought theory the impact of different types of biases on the zero correlation and guessing criteria are considered. It is argued that subjective measures of consciousness can be biased in various specified ways, some of which involve the relation between first order states and second (...) order thoughts, and hence are not errors in measurement of the conscious status of mental states; but other sorts of biases are measurement errors, involving the relation between higher order thoughts and their expression. Nonetheless, it is argued this type of bias does not preclude subjective measures - both the guessing criterion and the zero correlation criterion - as being amongst the most appropriate and useful tools for measuring the conscious status of mental states. (shrink)