The project has achieved most of its objectives and milestones for the period (considering the delay to the COVID-19 pandemic and the “Force Majeure”).
In work-package 1, we used a network task, during which participants describe the route taken by a marker through visually presented networks of objects while their eye-movements were monitored (Figure 1). We manipulated lexical selection difficulty and grammatical selection difficulty. To do so, we used pictures with a high name agreement and pictures with a low name agreement, that had a common gender or neuter gender. We replicated the finding that lexical selection difficulty (low name agreement) induces more pauses and self-corrections while neuter gender did not elicit more disfluency. Eye-tracking helped us demonstrate that not all disfluencies were related to this linguistic manipulation (e.g. some reflected a stalling strategy). Finally, by using statistical methods based on machine learning (multivariate pattern analyses), we found that all participants had a similar behavior when naming a picture with a low name agreement (both in terms of disfluency and eye-movements, Figure 1B). This work-package led to a publication in Language, Cognition and Neuroscience, as well as presentations to five international conferences.
In work-package 2, we asked whether delays in the earliest stages of picture naming elicit disfluency. To address this question, we used visual blurring which hinders visual identification of the items and thereby slows down selection of a lexical concept. We tested the effect of this manipulation on disfluency production and visual attention, similarly to Work-Package 1. Contrary to what was expected, blurriness did not lead to more disfluency on average and viewing times decreased with blurred pictures. However, multivariate pattern analyses revealed that a classifier could predict, from the pattern of disfluency, whether each participant was about to name blurred or control pictures. Impeding the conceptual generation of a message therefore affected the pattern of disfluencies of each participant individually, but this pattern was not consistent from one participant to another. This finding suggests that conceptual difficulty manifests itself differently from one participant to another. Correlations with cognitive performance provided insights into these inter-individual differences, suggesting an influence of inhibition and processing speed on disfluency production. For this study, a paper is currently under review. It also led to two proceedings and two presentations to international conferences (ExLing Society and DiSS workshop).
In work-package 3 (on-going), we investigate the cognitive mechanisms of semantic interference. To do so, we use a word-picture interference paradigm, in which participants have to name a picture with a superimposed word written on it (semantically related or not). Behavioral performance will then modelled using the drift diffusion model, an analytical approach that combines reaction time distributions for correct and incorrect task responses in order to estimate latent variables associated with task performance. This aims at uncovering the origin of such interference: at the lexico-semantic level or in the domain of executive control. In a second step, we will focus more specifically on the mechanisms underlying disfluency in such tasks: lexico-semantic, executive control, or attentional bias.
