ABSTRACT: In this paper, we discuss the results of two experiments, one off-line (acceptability judgment) and the other on-line (eye-tracking), targeting Object Cleft (OC) constructions. In both experiments, we used the same materials presenting a manipulation on person features: second person plural pronouns and plural definite determiners alternate in introducing a full NP ("it was [DP1 the/you [NP bankers]]i that [DP2 the/you [NP lawyers]] have avoided _i at the party") in a language, Italian, with overt person (and number) subject-verb agreement. As results, we first observed that the advantage of the bare pronominal forms reported in previous experiments (Gordon et al., 2001; Warren and Gibson, 2005, a.o.) is lost when the full NP (the "lexical restriction" in Belletti and Rizzi, 2013) is present. Second, an advantage for the mismatch condition, Art 1 -Pro 2, in which the focalized subject is introduced by the determiner and the OC subject by the pronoun, as opposed to the matching Pro 1 -Pro 2 condition, is observed, both off-line (higher acceptability and accuracy in answering comprehension questions after eyetracking) and on-line (e.g., smaller number of regressions from the subject region); third, we found a relevant difference between acceptability and accuracy in comprehension questions: despite similar numerical patterns in both off-line measures, the difference across conditions in accuracy is mostly not significant, while it is significant in acceptability. Moreover, while the matching condition Pro 1 -Pro 2 is perceived as nearly ungrammatical (far below the mean acceptability across-conditions), the accuracy in comprehension is still high (close to 80%). To account for these facts, we compare different formal competence and processing models that predict difficulties in OC constructions: similarity-based (Gordon et al., 2001, a.o.), memory load (Gibson, 1998), and intervention-based (Friedmann et al., 2009) accounts are compared to processing oriented ACT-R-based predictions (Lewis and Vasishth, 2005) and to top-down Minimalist derivations (Chesi, 2015). We conclude that most of these approaches fail in making predictions able to reconcile the competence and the performance perspective in a coherent way to the exception of the top-down model that is able to predict correctly both the on-line and the off-line main effects obtained.
