Caroline Rowland

Publications

Displaying 1 - 4 of 4
  • Abbot-Smith, K., Chang, F., Rowland, C. F., Ferguson, H., & Pine, J. (2017). Do two and three year old children use an incremental first-NP-as-agent bias to process active transitive and passive sentences?: A permutation analysis. PLoS One, 12(10): e0186129. doi:10.1371/journal.pone.0186129.

    Abstract

    We used eye-tracking to investigate if and when children show an incremental bias to assume that the first noun phrase in a sentence is the agent (first-NP-as-agent bias) while processing the meaning of English active and passive transitive sentences. We also investigated whether children can override this bias to successfully distinguish active from passive sentences, after processing the remainder of the sentence frame. For this second question we used eye-tracking (Study 1) and forced-choice pointing (Study 2). For both studies, we used a paradigm in which participants simultaneously saw two novel actions with reversed agent-patient relations while listening to active and passive sentences. We compared English-speaking 25-month-olds and 41-month-olds in between-subjects sentence structure conditions (Active Transitive Condition vs. Passive Condition). A permutation analysis found that both age groups showed a bias to incrementally map the first noun in a sentence onto an agent role. Regarding the second question, 25-month-olds showed some evidence of distinguishing the two structures in the eye-tracking study. However, the 25-month-olds did not distinguish active from passive sentences in the forced choice pointing task. In contrast, the 41-month-old children did reanalyse their initial first-NP-as-agent bias to the extent that they clearly distinguished between active and passive sentences both in the eye-tracking data and in the pointing task. The results are discussed in relation to the development of syntactic (re)parsing.

    Additional information

    Data available from OSF
  • Jones, G., & Rowland, C. F. (2017). Diversity not quantity in caregiver speech: Using computational modeling to isolate the effects of the quantity and the diversity of the input on vocabulary growth. Cognitive Psychology, 98, 1-21. doi:10.1016/j.cogpsych.2017.07.002.

    Abstract

    Children who hear large amounts of diverse speech learn language more quickly than children who do not. However, high correlations between the amount and the diversity of the input in speech samples makes it difficult to isolate the influence of each. We overcame this problem by controlling the input to a computational model so that amount of exposure to linguistic input (quantity) and the quality of that input (lexical diversity) were independently manipulated. Sublexical, lexical, and multi-word knowledge were charted across development (Study 1), showing that while input quantity may be important early in learning, lexical diversity is ultimately more crucial, a prediction confirmed against children’s data (Study 2). The model trained on a lexically diverse input also performed better on nonword repetition and sentence recall tests (Study 3) and was quicker to learn new words over time (Study 4). A language input that is rich in lexical diversity outperforms equivalent richness in quantity for learned sublexical and lexical knowledge, for well-established language tests, and for acquiring words that have never been encountered before.
  • Monaghan, P., & Rowland, C. F. (2017). Combining language corpora with experimental and computational approaches for language acquisition research. Language Learning, 67(S1), 14-39. doi:10.1111/lang.12221.

    Abstract

    Historically, first language acquisition research was a painstaking process of observation, requiring the laborious hand coding of children's linguistic productions, followed by the generation of abstract theoretical proposals for how the developmental process unfolds. Recently, the ability to collect large-scale corpora of children's language exposure has revolutionized the field. New techniques enable more precise measurements of children's actual language input, and these corpora constrain computational and cognitive theories of language development, which can then generate predictions about learning behavior. We describe several instances where corpus, computational, and experimental work have been productively combined to uncover the first language acquisition process and the richness of multimodal properties of the environment, highlighting how these methods can be extended to address related issues in second language research. Finally, we outline some of the difficulties that can be encountered when applying multimethod approaches and show how these difficulties can be obviated
  • Rowland, C. F., & Monaghan, P. (2017). Developmental psycholinguistics teaches us that we need multi-method, not single-method, approaches to the study of linguistic representation. Commentary on Branigan and Pickering "An experimental approach to linguistic representation". Behavioral and Brain Sciences, 40: e308. doi:10.1017/S0140525X17000565.

    Abstract

    In developmental psycholinguistics, we have, for many years,
    been generating and testing theories that propose both descriptions of
    adult representations and explanations of how those representations
    develop. We have learnt that restricting ourselves to any one
    methodology yields only incomplete data about the nature of linguistic
    representations. We argue that we need a multi-method approach to the
    study of representation.

Share this page