Neurocognitive basis of implicit learning of sequential structure and its relation to language processing

Abstract

The ability to learn and exploit environmental regularities is important for many aspects of skill learning, of which language may be a prime example. Much of such learning proceeds in an implicit fashion, that is, it occurs unintentionally and automatically and results in knowledge that is difficult to verbalize explicitly. An important research goal is to ascertain the underlying neurocognitive mechanisms of implicit learning abilities and understand its contribution to perception, language, and cognition more generally. In this article, we review recent work that investigates the extent to which implicit learning of sequential structure is mediated by stimulus-specific versus domain-general learning mechanisms. Although much of previous implicit learning research has emphasized its domain-general aspect, here we highlight behavioral work suggesting a modality-specific locus. Even so, our data also reveal that individual variability in implicit sequence learning skill correlates with performance on a task requiring sensitivity to the sequential context of spoken language, suggesting that implicit sequence learning to some extent is domain-general. Taking into consideration this behavioral work, in conjunction with recent imaging studies, we argue that implicit sequence learning and language processing are both complex, dynamic processes that partially share the same underlying neurocognitive mechanisms, specifically those that rely on the encoding and representation of phonological sequences.

Figure 1

An example of an artificial grammar used to generate sequences in implicit learning experiments. To generate a sequence, the experimenter follows the paths of the grammar and notes the sequence of numbers that are encountered. For instance, the sequence 3-4-2-4-1 is grammatical with respect to this grammar, whereas the sequence 4-1-3-2-2 is not. The numbers 1-4 are then mapped onto stimulus elements needed for the experiment in question, allowing for the generation of a set of structured patterns occurring in virtually any stimulus modality or dimension as needed (e.g., tones, nonsense syllables, visual patterns, etc.).

Figure 2

Vibrotactile devices attached to the fingers of a participant’s hand. This setup was used by Conway & Christiansen (2005) to assess tactile implicit learning.

Figure 3

Implicit learning results for tactile, visual, and auditory sequences. Figure adapted from Conway & Christiansen (2005).

Figure 4

Implicit learning results comparing performance for dual-grammar (black bars) versus single-grammar (white bars) conditions. Figure drawn from data presented in Conway & Christiansen (2006).

Figure 5

Performance on a visual color sequence learning task plotted against performance on a spoken language perception task under degraded listening conditions for high predictability (HP), low predictability (LP), and anomalous (AN) sentences. Figures drawn from data presented in Conway et al. (2007).

Figure 6

Performance on a visual non-color sequence learning task for subjects who used a verbal coding strategy, plotted against performance on a spoken language perception task under degraded listening conditions for high predictability (HP), low predictability (LP), and anomalous (AN) sentences. Figures drawn from data presented in Conway et al. (2007).

Figure 7

Performance on a visual non-color sequence learning task for subjects who did not use a verbal coding strategy, plotted against performance on a spoken language perception task under degraded listening conditions for high predictability (HP), low predictability (LP), and anomalous (AN) sentences. Figures drawn from data presented in Conway et al. (2007).