Balancing the Word
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We conclude with a discussion of how the logic of the arguments presented in this paper can be extended to a broader range of cues to grammatical function assignment, beyond the interaction between case marking and constituent order. Conditioned variability in our experiment increases communicative success without a concomitant increase in production effort. In a language that conditions case marking on constituent order, not all sentential objects need to be marked but sentence meaning can be reliably inferred when case markers are not present.
While the same increase in communicative success would be gained by conditioning case marking on either SOV or OSV order, learners in our experiment consistently used case more often in OSV sentences. What can account for this preference? Even though OSV and SOV constituent orders occurred equally frequently in the input and in participants' productions, OSV constituent order is typologically rare and is uncommon in English. Finally, the preference to use more case in OSV sentences might reflect a bias to maximize the number of linguistic dependencies processed at each point in time cf.
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Because our input languages only have object case marking, case would provide the earliest point of disambiguation in OSV sentences and allow for correct parsing commitments early on. Thus, it is possible that learners preferentially restructured the input language in such a way as to put more informative cues earlier in the sentence.
The three explanations outlined above are mutually compatible, and future work is necessary to assess their validity. Our results add to the growing body of research investigating the circumstances under which learners are likely to deviate from the input they receive. Adult learners in our experiment did not veridically reproduce the inefficient variation in the input, but tended to consistently deviate from the input toward more efficient linguistic systems. These studies, however, differ from present work in several important respects.
First, a large body of work suggests that learners are not likely to introduce innovations into perfectly consistent languages traditionally used to study the relationship between language learning and language structure , at least not within the short amount of time spent in the laboratory e. Unlike these studies, our input languages incorporate variability, somewhat reminiscent of the situation of a pidgin, to make learning biases manifest within the short period of time available in an experiment.
Studies using the paradigm similar to ours, in which learners are exposed to languages where several grammatical structures are available to express the same meaning, have produced somewhat mixed results with regard to adult learners' behavior. While young learners readily regularized the inconsistent input they received and used one of the determiners in almost all of their productions, adults mostly reproduced the unpredictable variation present in the input.
This bias against deviating from the input can, however, be overcome in adult learners if input languages are fairly complex e. Crucially, the miniature artificial languages used in our experiment were different from those used in most previous work in that the design of our experiment provides a functional motivation to deviate from the input. Our findings raise questions about the precise nature of the mechanism underlying learners' preferences to efficiently trade off cues to grammatical function assignment.
Particularly relevant to the work presented here is a recent study by Kurumada and Jaeger They found that speakers of Japanese were more likely to produce object case marking when sentence properties such as animacy of the object or plausibility of grammatical function assignment were likely to bias the listener away from the intended interpretation.
It is also possible that the patterns observed in our experiments originate in learning that is not specific to production: Learners may have misinterpreted some of the sentences they were exposed to, altering the characteristics of the input from which they learned. One possibility is that learners might have misinterpreted some of the sentences during initial perception cf.
Guy, ; Ohala, For example, in Van Everbroeck's simulations, the model was trained to determine grammatical function of each sequentially presented word in a sentence. For flexible constituent order languages, however, the model could not reliably distinguish between constituents in the absence of other cues to grammatical function assignment e. Adding case marking dramatically improved network performance. How likely is such an account to explain our results? On the other hand, two properties of our experimental design make an account that grounds misinterpretations in perception somewhat implausible.
First, unlike the connectionist simulations described above, the meaning of the training sentences was always illustrated by the accompanying video in our experiment, thereby unambiguously conveying the intended grammatical function assignment. Recall that sentence descriptions were played as the action unfolded, thus making it less probable that learners have formed incorrect interpretations of sentence meaning based on the novel descriptions alone.
Second, there was no time pressure. This makes it somewhat unlikely that misinterpretations during perception were sufficiently frequent to create the observed effect.
This interference is increased if the items that need to be retrieved or encoded are in some way similar to each other e. Increasing syntactic or semantic similarity between the target and preceding or following material poses additional difficulty during retrieval as manifested in longer reading times and increased comprehension errors.
Since all visual referents in our experiment were human and male, it is likely that participants experienced this type of interference and thus misassigned grammatical roles in a certain number of trials. Such misassignments could have introduced changes into the input distribution when it was consolidated in memory. The hypothesis that the outcomes observed in our experiment arise outside of the production system whether in perception or memory is broadly compatible with functional theories of efficient communication.
It complements work that has focused on the organization of human production system as the locus of biases for efficient communication e. While the main focus of this study is on the interaction between constituent order and case marking, the arguments presented here can be extended beyond these particular cues to grammatical function assignment.
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If an existing cue to grammatical function assignment is highly informative, other cues would be redundant and thus could be omitted; if existing cues do not contain enough information to successfully communicate who is doing what to whom in a sentence, additional cues should be recruited to maintain successful communication. Our account also makes explicit predictions for languages that go against the correlation between constituent order flexibility and the presence of case, such as some highly flexible constituent order languages that have no case marking e.
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Such languages are expected to have cues other than case marking to encode grammatical function assignment. Note that while we argue against excessive unnecessary redundancy in linguistic systems, some amount of redundancy is expected under our account for a related discussion, see also Piantadosi, Tily, and Gibson a. Human communication takes place in the presence of noise.
Thus, listeners need to infer the speaker's intended message from multiple probabilistic cues, each of which can be corrupted during its transmission via the noisy channel with a certain probability. For example, case markers and constituent order might not be successfully perceived every time—they can be lost or misheard. This has consequences for when case marking would be expected in a language. Even when constituent order is highly informative, case marking will often further increase robust information transmission especially in those cases when the message is less expected.
This might explain why fixed constituent order languages sometimes have case and agreement systems Siewierska, or why many languages with optional case marking e.
Our results also contribute to a growing body of work demonstrating the potential of using miniature artificial language learning to study the relationship between learning biases and language structures. We thank T. Stanley and S. Krishnan for their help with data collection and data coding; C. Anzalone, G. Smith, E. Williams, and B.
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Chu for their help with data coding; C. Donatelli, I. Minkina and A. Wood for their help with video stimuli; L. Emberson, P. Reeder, and J. Blevins for valuable feedback on the manuscript. Volume 41 , Issue 2. The full text of this article hosted at iucr. If you do not receive an email within 10 minutes, your email address may not be registered, and you may need to create a new Wiley Online Library account. If the address matches an existing account you will receive an email with instructions to retrieve your username.
Cognitive Science Volume 41, Issue 2. Regular Article Free Access. Maryia Fedzechkina Corresponding Author E-mail address: mfedze sas. Elissa L. Tools Request permission Export citation Add to favorites Track citation. Share Give access Share full text access. Share full text access. Please review our Terms and Conditions of Use and check box below to share full-text version of article. Abstract Across languages of the world, some grammatical patterns have been argued to be more common than expected by chance. Nouns Verbs Case Marker glim geed kah flugit kleidum spad zamper bliffen shen norg melnawg.
Figure 1 Open in figure viewer PowerPoint. Illustration of form to grammatical function mappings in two extreme scenarios completely fixed and completely free constituent order languages in the experiment. Pictures are still images of sample videos with their descriptions in the miniature language not shown to participants. Arrows indicate form to meaning mappings in the two languages.
Figure 2 Open in figure viewer PowerPoint. Figure 3 Open in figure viewer PowerPoint. Constituent order use by language condition. The dashed lines indicate the input proportions different across conditions. Figure 4 Open in figure viewer PowerPoint. Case marker use by language condition. The dashed line represents the input proportion equal across conditions. Figure 5 Open in figure viewer PowerPoint. Case marker use by sentence constituent order flexible constituent order language. The dashed line represents the input proportion equal across constituent orders. Figure 6 Open in figure viewer PowerPoint.
Constituent order preferences of individual learners on the final third day of training in the fixed top panel and flexible bottom panel constituent order languages. The dashed lines indicate the input proportion. Figure 7 Open in figure viewer PowerPoint.
Case marker preferences of individual learners on the final third day of training in the fixed top panel and flexible bottom panel constituent order languages. Figure 8 Open in figure viewer PowerPoint. Diamonds with red borders represent input languages.