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Differentiating scalar implicature from exclusion inferences in language acquisition

Published online by Cambridge University Press:  10 April 2019

Jessica SULLIVAN*
Affiliation:
Skidmore College, Saratoga Springs, NY, USA University of California, San Diego, CA, USA
Kathryn DAVIDSON
Affiliation:
Harvard University, Cambridge, MA, USA University of California, San Diego, CA, USA
Shirlene WADE
Affiliation:
University of Rochester, Rochester, NY, USA University of California, Berkeley, CA, USA University of California, San Diego, CA, USA
David BARNER
Affiliation:
University of California, San Diego, CA, USA
*
*Corresponding author. Skidmore College, #101 Tisch Learning Center, 815 N. Broadway, Saratoga Springs, NY 12866, USA. E-mail: jsulliv1@skidmore.edu
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Abstract

During acquisition, children must learn both the meanings of words and how to interpret them in context. For example, children must learn the logical semantics of the scalar quantifier some and its pragmatically enriched meaning: ‘some but not all’. Some studies have shown that ‘scalar implicature’ – that some implies ‘some but not all’ – poses a challenge even to nine-year-olds, while others find success by age three. We asked whether reports of children's successes might be due to the computation of exclusion inferences (like contrast or mutual exclusivity) rather than scalar implicatures. We found that young children (N = 214; ages 4;0–7;11) sometimes compute symmetrical exclusion inferences rather than asymmetric scalar inferences. These data suggest that a stronger burden of evidence is required in studies of implicature; before concluding that children compute implicatures, researchers should first show that children exhibit sensitivity to asymmetric entailment in the task.

Information

Type
Articles
Copyright
Copyright © Cambridge University Press 2019 
Figure 0

Figure 1. Schematic of methods for Experiment 1 and Experiment 2. Left panel indicates the experimenter's request (e.g., Puppy's task), while the right panel demonstrates how the participant learned about Puppy's actions.

Figure 1

Table 1. Trial types, names, actions involved, and predictions for trials in Experiment 1

Figure 2

Figure 2. Mean rates of prize-giving for control trials; Good Job Control trials are on the top row and Bad Job Control trials are on the bottom two rows. Error bars are SEM.

Figure 3

Table 2. Percent giving Puppy a prize in Experiment 1 (Words Only), and Experiment 2 (Words and Pictures; Pictures Only).

Figure 4

Table 3. Percent including negation in their responses in Experiment 1 (Words Only), and Experiment 2 (Words and Pictures; Pictures Only).

Figure 5

Figure 3. Mean rates of prize-giving for Scalar, Im-‘blick’-ature, and Mixed-Scale trials; the left column shows implicatures, the right shows entailment; error bars are SEM.

Figure 6

Figure 4. Proportion giving prizes for Contrast Mismatch and Contextual Entailment trials; error bars are SEM.

Figure 7

Figure 5. Proportion giving prize (left) and using negation (right) for Scalar Entailment (black) and Scalar Implicature (gray) trials. These data are for children only, and are combined across Experiments 1 and 2. Error bars are SEM.

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