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Building Categories in Interaction: Linguistic resources at work
Edited by Caterina Mauri, Ilaria Fiorentini and Eugenio Goria
[Studies in Language Companion Series 220] 2021
► pp. 111–154

Chapter 5
Are new words predictable?

A pilot study on the origin of neologies by means of natural selection

| Ludwig-Maximilians University Munich
Research on neologisms has so far been dominated by retrospective investigations (cf. e.g. Schmid 2008; Kerremans & Prokić 2018). By contrast, the present study is probably the first to directly tap into speakers’ linguistic competence by eliciting names for unfamiliar entities. Building on early assumptions by Darwin (1859, 1871) and others an evolutionary, fitness-based approach is developed to predict und thus explain the selections made by groups of German speakers of successful neologisms from self-generated pools. Four main factors are identified as crucial: quantity (brevity), quality (truthfulness), relation (distinctiveness) and manner (attractiveness). In order to test the viability of the theory an experiment is conducted and replicated. The results provide preliminary support for the assumptions made.
Keywords: language change, prediction, experimental linguistics, lexicon, neologisms, Darwinian evolution, natural selection, fitness
Article outline
  • 1.Introduction
    • 1.1Predictive language theories and their moving target
    • 1.2Language change and neology
    • 1.3Language evolution: From Darwin via Haeckel, Schleicher and Müller back to Darwin
    • 1.4The incredible career of a neologism and Engelbart’s Question
    • 1.5Naming and categorization: Clarifications and sample case observations
  • 2.Investigating form and fate of neologisms: An experimental approach
    • 2.1Creating demand for names of unknown objects: Meet the Fribbles
    • 2.2Preparatory data collection: Five objects, five name pools and an initial hypothesis
    • 2.3Venturing a first hypothesis
  • 3.Pretest: Deriving preference hypotheses from estimated natural selection
    • 3.1Pretest
    • 3.2Pretest results: Five pools resulting from artificial and estimated natural selection
    • 3.3Developing a selection predictor: Factors of fitness ranking
      • 1.Quantity
      • 2.Quality
      • 3.Relation
      • 4.Manner
    • 3.4Assigning degrees of fitness: The coding algorithm
      • 1.Quantity
      • 2.Quality
      • 3.Relation
      • 4.Manner
  • 4.Experiment: Testing the fitness rank predictor with forced natural selection
    • 4.1Experiment
    • 4.2Comparing observed and predicted rankings
      • 4.2.1Object 1 experiment ranking
      • 4.2.2Object 2 experiment ranking
      • 4.2.3Object 3 experiment ranking
      • 4.2.4Object 4 experiment ranking
      • 4.2.5Object 5 experiment ranking
      • 4.2.6Overview of experiment rankings
    • 4.3Discussion of the experiment results
      • 4.3.1Limits of ecological validity
      • 4.3.2Other possible constraints
      • 4.3.3Tackling the replicability issue
  • 5.Replication: Retesting the fitness rank predictor
    • 5.1Replication procedure
    • 5.2Comparing observed and predicted replication rankings
      • 5.2.1Object 1 replication ranking
      • 5.2.2Object 2 replication ranking
      • 5.2.3Object 3 replication ranking
      • 5.2.4Object 4 replication ranking
      • 5.2.5Object 5 replication ranking
      • 5.2.6Overview of replication rankings
    • 5.3Discussion of the replication
      • 5.3.1Group dynamics
      • 5.3.2Optimizing fitness categories?
  • 6.Overall analysis
    • 6.1Ways of estimating correlation strength of incomplete rankings with ties
    • 6.2Statistical overview of experiment and replication results
  • 7.Summary and outlook
  • Acknowledgements
  • Notes
  • References
Published online: 6 December 2021
DOI logo
https://doi.org/10.1075/slcs.220.05zae
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