Eye-movements can help disentangle mechanisms underlying disfluency

To reveal the underlying cause of disfluency, several authors related the pattern of disfluencies to difficulties at specific levels of production, using a Network Task. Given that disfluencies are multifactorial, we combined this paradigm with eye-tracking to disentangle disfluency related to word preparation difficulties from others (e.g. stalling strategies). We manipulated lexical and grammatical selection difficulty. In Experiment 1, lines connecting the pictures varied in length, which led participants to use a strategy and inspect other areas than the upcoming picture when pictures were preceded by long lines. Experiment 2 only used short lines. In both experiments, lexical selection difficulty promoted self-corrections, pauses and longer fixation latency prior to naming. Multivariate Pattern Analyses demonstrated that disfluency and eye-movement data patterns can predict lexical selection difficulty. Eye-tracking could provide complementary information about network tasks, by disentangling disfluencies related to picture naming from disfluencies related to self-monitoring or stalling strategies.