Reference Production as Search: The Impact of Domain Size on the Production of Distinguishing Descriptions

Albert Gatt, Emiel Krahmer, Kees Van Deemter, Roger P. G. van Gompel

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


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 fndings in visual search; however, unlike models of reference production, visual search models also predict that search can become very ecient 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.
Original languageEnglish
Pages (from-to)1457-1492
Number of pages36
JournalCognitive Science
Issue numberS6
Early online date6 Jun 2016
Publication statusPublished - May 2017

Bibliographical note

The authors thank the anonymous reviewers of this manuscript for many insightful comments and discussion. AG and EK received financial support from The Netherlands Organization for Scientific Research, via a Vici Grant (NWO Grant 27770007), which is gratefully acknowledged. KvD was supported by the RefNet project (EPSRC award EP/J019615/1). Thanks are also due to Ms. Manon Yassa and Ms. Kristel Bartels for their help in conducting the experiments and transcription of the data.


  • reference
  • language production
  • visual search
  • salience
  • computational models


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