Similar neural correlates for language and sequential learning: Evidence from event-related brain potentials

Abstract

We used event-related potentials (ERPs) to investigate the time course and distribution of brain activity while adults performed (1) a sequential learning task involving complex structured sequences and (2) a language processing task. The same positive ERP deflection, the P600 effect, typically linked to difficult or ungrammatical syntactic processing, was found for structural incongruencies in both sequential learning as well as natural language and with similar topographical distributions. Additionally, a left anterior negativity (LAN) was observed for language but not for sequential learning. These results are interpreted as an indication that the P600 provides an index of violations and the cost of integration of expectations for upcoming material when processing complex sequential structure. We conclude that the same neural mechanisms may be recruited for both syntactic processing of linguistic stimuli and sequential learning of structured sequence patterns more generally.

Keywords:

• Event-related potentials (ERPs)
• Sequential learning
• Implicit learning
• Language processing
• Prediction
• P600

ACKNOWLEDGEMENTS

This research was supported by Human Frontiers Science Program grant RGP0177/2001-B to MHC, by NIDCD grant R03DC9485 to CMC, and by NICHD grant 5R03HD051671-02 to LO. We are grateful for the helpful comments from Stewart McCauley and two anonymous reviewers.

Notes

¹Findings relating to sequential learning are variously published under different headings such as “statistical learning,” “artificial language learning,” or “artificial grammar learning,” largely for historical reasons. However, as we see these studies as relating to the same underlying implicit learning mechanisms (Conway & Christiansen, 2006; Perruchet & Pacton, 2006), we prefer the term “sequential learning” as it highlights the sequential nature of the stimuli and its potential relevance to language processing.

²We additionally analyzed the data re-referenced to average reference and obtained qualitatively similar results.

³In contrast to our results, Friederici et al. (2002) found a reliable P600 effect in the 700–900 msec interval for an artificial language learning task using similar stimuli as here. We see at least two factors that may contribute to this discrepancy: (1) The participants in Friederici et al.'s study spent many hours during the learning phase of this study compared to the 30 min of exposure that our participants received; (2) Friederici et al. used a more language-like learning situation in which participants were playing a computerised board game in pairs using utterances from the artificial language with explicit feedback on incorrect language use, whereas our participants only received passive exposure to the sequences and associated visual referents. Thus, the participants in the Friederici et al. study not only received more than 10 times the exposure compared to our participants, but they were also actively trained and received feedback on their use of the language. Together, these factors likely explain why we obtained a weaker P600 effect in our study.

⁴More recently, Mueller et al. (2005) did report a broadly distributed negativity for word category violations in a miniature-Japanese learning task but this was observed even in untrained control participants, perhaps suggesting that the negativity in the trained participants may be related to other nonsyntactic factors.