ABSTRACT
This work investigates the linearisation strategies used by speakers when describing real-world scenes to better understand production plans for multi-utterance sequences. In this study, 30 participants described real-world scenes aloud. To investigate which semantic features of scenes predict order of mention, we quantified three features (meaning, graspability, and interactability) using two techniques (whole-object ratings and feature map values). We found that object-level semantic features, namely those affordance-based, predicted order of mention in a scene description task. Our findings provide the first evidence for an object-related semantic feature that guides linguistic ordering decisions and offers theoretical support for the role of object semantics in scene viewing and description.
Acknowledgements
We thank our research assistants, Lauren Alimento, Casey Felton, and Susannah Rudd, for their excellent work.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
All study materials including data, stimuli, and analysis code are available at: https://osf.io/ey3wd/. This study was not preregistered.
Notes
1 Refer to Henderson et al. (Citation2018) for analyses pertaining to visual attention.