Barker M, Rehrig G, Ferreira F. Speakers prioritise affordance-based object semantics in scene descriptions.
Lang Cogn Neurosci 2023;
38:1045-1067. [PMID:
37841974 PMCID:
PMC10572038 DOI:
10.1080/23273798.2023.2190136]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 02/18/2023] [Indexed: 10/17/2023]
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 offer theoretical support for the role of object semantics in scene viewing and description.
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