Interference between sentence processing and probabilistic implicit sequence learning

Abstract

Background: During sentence processing we decode the sequential combination of words, phrases or sentences according to previously learned rules. The computational mechanisms and neural correlates of these rules are still much debated. Other key issue is whether sentence processing solely relies on language-specific mechanisms or is it also governed by domain-general principles.

Methodology/principal findings: In the present study, we investigated the relationship between sentence processing and implicit sequence learning in a dual-task paradigm in which the primary task was a non-linguistic task (Alternating Serial Reaction Time Task for measuring probabilistic implicit sequence learning), while the secondary task were a sentence comprehension task relying on syntactic processing. We used two control conditions: a non-linguistic one (math condition) and a linguistic task (word processing task). Here we show that the sentence processing interfered with the probabilistic implicit sequence learning task, while the other two tasks did not produce a similar effect.

Conclusions/significance: Our findings suggest that operations during sentence processing utilize resources underlying non-domain-specific probabilistic procedural learning. Furthermore, it provides a bridge between two competitive frameworks of language processing. It appears that procedural and statistical models of language are not mutually exclusive, particularly for sentence processing. These results show that the implicit procedural system is engaged in sentence processing, but on a mechanism level, language might still be based on statistical computations.

Figure 1. Schematic design of the experiment.

The presentation order of the conditions was counterbalanced between subjects. In the ASRT task blocks 1, 8 and 15 were single task (ST) blocks without parallel task, whereas in other blocks (2–7; 9–14) our subjects had to perform one of the three parallel tasks as well (DT condition).

Figure 2

A) Mean RTs of sequence-specific learning (difference between high and low frequency triplets) in probe blocks of the ASRT task for all dual task conditions. There was significant sequence-specific learning in the Word and Math condition, but no learning in the Sentence condition. B) Error rates in parallel task during dual task. There were significantly more errors in the Math condition than in the other two conditions. C) Mean RTs in dual task blocks of the ASRT for all dual task conditions. The Math condition was the most difficult: the RTs differed significantly from the Word and Sentence conditions, while the latter two did not differ significantly from each other. D) Mean accuracy (ACC) in dual task blocks of the ASRT for all dual task conditions. The Math condition was the most difficult: participants were less accurate in the Math condition than in the Sentence condition, while the Word-Math and Word-Sentence conditions did not differ significantly from each other. Error bars indicate standard errors of the mean (SEM).