Ricomprendo: an assessment test for fine-graded alterations of syntactic processing.

INTRODUCTION

RiComprendo was designed as a comprehensive tool to evaluate comprehension of various syntactic structures with a sentence-to-picture matching-task. The study sets normative data and investigates the influence of demographic characteristics and sentence types.

METHODS

Two-hundred-ten right-handed healthy Italian native speakers were included. RiComprendo was administered via E-Prime, collecting both accuracy and response time (RTs) data. Adjusted scores for age and education and equivalent scores for each sentence type (i.e., simple active sentences, passives, peripheral and center-embedded subject and object relatives, and coordination) were computed. The effect of sentence type, age and education on accuracy and RTs was investigated through generalized linear mixed models. Item difficulty was tested with an Item Response Theory (IRT) model.

RESULTS

Normative data and a spreadsheet for scores automatic computation are provided. The mixed models identified a significant impact of sentence type both on accuracy and RTs. Simple sentences presented high accuracy, while the worst performance was found with center-embedded object relatives. Higher education was significantly associated with better accuracy, while older age was associated with longer RTs and with a marginally negative effect on accuracy.

DISCUSSION

Sentence comprehension is influenced by structural complexity and demographic characteristics (age and education). Providing normative data for different sentence types, RiComprendo enables the evaluation of effects of specific syntactic properties (i.e., canonicity, number of clauses, filler-gap dependencies, intervention, center-embedding).

CONCLUSION

RiComprendo could serve as a valuable tool for testing the comprehension of complex sentences in research and clinical settings, informing in-depth functional assessment and tailored intervention.