Speakers’ perception of a visual scene influences the language they use to describe it—which objects they choose to mention and how they characterize the relationships between them. We show that visual complexity can either delay or facilitate description generation, depending on how much disambiguating information is required and how useful the scene's complexity can be in providing, for example, helpful landmarks. To do so, we measure speech onset times, eye gaze, and utterance content in a reference production experiment in which (...) the target object is either unique or non-unique in a visual scene of varying size and complexity. Speakers delay speech onset if the target object is non-unique and requires disambiguation, and we argue that this reflects the cost of deciding on a high-level strategy for describing it. The eye-tracking data demonstrate that these delays increase when speakers are able to conduct an extensive early visual search, implying that when speakers scan too little of the scene early on, they may decide to begin speaking before becoming aware that their description is underspecified. Speakers’ content choices reflect the visual makeup of the scene—the number of distractors present and the availability of useful landmarks. Our results highlight the complex role of visual perception in reference production, showing that speakers can make good use of complexity in ways that reflect their visual processing of the scene. (shrink)

When producing a description of a target referent in a visual context, speakers need to choose a set of properties that distinguish it from its distractors. Computational models of language production/generation usually model this as a search process and predict that the time taken will increase both with the number of distractors in a scene and with the number of properties required to distinguish the target. These predictions are reminiscent of classic findings in visual search; however, unlike models of reference (...) production, visual search models also predict that search can become very efficient under certain conditions, something that reference production models do not consider. This paper investigates the predictions of these models empirically. In two experiments, we show that the time taken to plan a referring expression—as reflected by speech onset latencies—is influenced by distractor set size and by the number of properties required, but this crucially depends on the discriminability of the properties under consideration. We discuss the implications for current models of reference production and recent work on the role of salience in visual search. (shrink)