Modeling the distributional dynamics of attention and semantic interference in word production

In recent years, it has become clear that attention plays an important role in spoken word production. Some of this evidence comes from distributional analyses reaction time (RT) regular picture naming and picture-word interference. Yet we lack a mechanistic account how the properties RT distributions come to reflect attentional processes these may turn modulate amount conflict between lexical representations. Here, present computational according which lapses allow for existing build up unsupervised on subset trials, thus modulating shape resulting distribution. Our process model resolves discrepancies outcomes previous studies semantic Moreover, model's predictions were confirmed new experiment where participants' motivation remain attentive determined size locus interference naming. We conclude modeling importantly improves our understanding interplay provides framework interpreting data tasks.