Implicit learning.

Abstract

Implicit learning is nonepisodic learning of complex information in an incidental manner, without awareness of what has been learned. Implicit learning experiments use 3 different stimulus structures (visual, sequence, and function) and 3 different dependent measures or response modalities (conceptual fluency, efficiency, and prediction and control). Implicit learning may require a certain minimal amount of attention and may depend on attentional and working memory mechanisms. The result of implicit learning is implicit knowledge in the form of abstract (but possibly instantiated) representations rather than verbatim or aggregate representations. Implicit learning shows biases and dissociations in learning different stimulus structures. The dependence of implicit learning on particular brain areas is discussed, some conclusions are drawn for modeling implicit learning, and the interaction of implicit and explicit learning is considered.

Implicit learning of tonality: A self-organizing approach.

“…This is thought to occur by hypothesis testing or rule instruction. Implicit learning is the acquisition of knowledge in an incidental manner without complete verbalizable knowledge of what is learned (Seger, 1994). Implicit learning is seen as a fundamental characteristic of the cognitive system, enabling the acquisition of highly complex information that may not be acquired in an explicit way (Reber, 1989).…”

Semantic contextual cuing and visual attention.

“…A tempting hypothesis is that some of the knowledge in scene schemas is acquired and used implicitly. Implicit learning refers to an adaptation process through which the behavior of an individual becomes sensitive to a structure in an incidental manner, in such a way that the individual is not able to verbally report or even consciously access the resulting knowledge (for reviews, Reber, 1989;Seger, 1994). Such knowledge could affect the unfolding of visuocognitive or visuomotor operations.…”

The Motivational and Metacognitive Control in CLARION

“…This is because distributed representational units in the hidden layer(s) of a backpropagation network are capable of accomplishing computations but are subsymbolic and generally not individually meaningful (Rumelhart et al 1986, Sun 1994. This characteristic of distributed representation, which renders the representational form less accessible, accords well with the relative inaccessibility of implicit knowledge (Reber 1989, Seger 1994, Cleeremans et al 1998. In contrast, explicit knowledge may be captured in computational modeling by symbolic or localist representation (Clark and Karmiloff-Smith 1993), in which each unit is more easily interpretable and has a clearer conceptual meaning.…”

“…Note also that social interaction is made possible by the (at least partially) innate ability of agents to reflect on, and to modify dynamically, their own behaviors (Tomasello 1999). The metacognitive self Each of these interacting subsystems consists of two levels of representation (i.e., a dual representational structure): Generally, in each subsystem, the top level encodes explicit knowledge and the bottom level encodes im-plicit knowledge; this distinction has been argued for earlier (see also Reber 1989, Seger 1994, and Cleeremans et al 1998. Let us consider the representational forms that need to be present for encoding these two different types of knowledge.…”