Language learning, language use and the evolution of linguistic variation

Smith, Kenny; Perfors, Amy; Fehér, Olga; Samara, Anna; Swoboda, Kate; Wonnacott, Elizabeth

doi:10.1098/rstb.2016.0051

Cited by 66 publications

(82 citation statements)

References 31 publications

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“…Similar effects of mixing are reported in Smith et al. () in a computational model and human experiment exploring the transmission of unpredictably variable linguistic systems: While individual learners tend to condition linguistic variants on aspects of the linguistic context, there is substantial inter‐individual variation in which variants are conditioned on which contexts; consequently, as we see here, mixing the output of multiple such individuals masks this individual‐level simplification of the language.…”

Section: Experiments 2: Mixed Linguistic Input Data and The Spread Of supporting

confidence: 85%

“…In the same vein, in their experimental study Smith et al. () manipulated the presence or absence of speaker identity information while mixing input from multiple speakers, and found that it made no difference to the mixing effect, again suggesting that learners can at least sometimes fail to exploit social cues which would otherwise allow them to account for variability in their input. Providing information on speaker identity therefore does not guarantee that participants will be able to use it to deal with variability in their input, and the requirement to track speaker identity may in itself add to the burden on learners.…”

Section: Experiments 2: Mixed Linguistic Input Data and The Spread Of mentioning

confidence: 97%

“…Smith et al. () provide models showing that, at least for the case of variation‐learning, such speaker‐based conditioning can in some circumstances attenuate the effects of mixing; for instance, if an individual identifies with a single input source (say based on shared social characteristics) and then bases his or her own output on that individual, effects of mixing are entirely removed.…”

Section: Experiments 2: Mixed Linguistic Input Data and The Spread Of mentioning

confidence: 99%

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Adult Learning and Language Simplification

2018

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Languages spoken in larger populations are relatively simple. A possible explanation for this is that languages with a greater number of speakers tend to also be those with higher proportions of non‐native speakers, who may simplify language during learning. We assess this explanation for the negative correlation between population size and linguistic complexity in three experiments, using artificial language learning techniques to investigate both the simplifications made by individual adult learners and the potential for such simplifications to influence group‐level language characteristics. In Experiment 1, we show that individual adult learners trained on a morphologically complex miniature language simplify its morphology. In Experiment 2, we explore how these simplifications may then propagate through subsequent learning. We use the languages produced by the participants of Experiment 1 as the input for a second set of learners, manipulating (a) the proportion of their input which is simplified and (b) the number of speakers they receive their input from. We find, contrary to expectations, that mixing the input from multiple speakers nullifies the simplifications introduced by individuals in Experiment 1; simplifications at the individual level do not result in simplification of the population's language. In Experiment 3, we focus on language use as a mechanism for simplification, exploring the consequences of the interaction between individuals differing in their linguistic competence (as native and non‐native speakers might). We find that speakers who acquire a more complex language than their partner simplify their language during interaction. We ultimately conclude that adult learning can result in languages spoken by more people having simpler morphology, but that idiosyncratic simplifications by non‐natives do not offer a complete explanation in themselves; accommodation—by comparatively competent non‐natives to less competent speakers, or by native speakers to non‐natives—may be a key linking mechanism.

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Section: Experiments 2: Mixed Linguistic Input Data and The Spread Of supporting

confidence: 85%

Section: Experiments 2: Mixed Linguistic Input Data and The Spread Of mentioning

confidence: 97%

Section: Experiments 2: Mixed Linguistic Input Data and The Spread Of mentioning

confidence: 99%

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Adult Learning and Language Simplification

2018

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“…However, both experimental tasks and computational models have suggested specific ways in which constraints on statistical learning might have influenced the structure of natural languages (e.g., Christiansen & Chater 2008, Saffran 2001b, Smith et al 2017). The general idea is that language structures that are more learnable, particularly by infants and young children, should be more prevalent in the languages of the world than structures that are more difficult to learn.…”

Section: Why Are We Statistical Learners?mentioning

confidence: 99%

Infant Statistical Learning

Saffran

Kirkham

2018

Annu. Rev. Psychol.

334

211

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Perception involves making sense of a dynamic, multimodal environment. In the absence of mechanisms capable of exploiting the statistical patterns in the natural world, infants would face an insurmountable computational problem. Infant statistical learning mechanisms facilitate the detection of structure. These abilities allow the infant to compute across elements in their environmental input, extracting patterns for further processing and subsequent learning. In this selective review, we summarize findings that show that statistical learning is both a broad and flexible mechanism (supporting learning from different modalities across many different content areas) and input specific (shifting computations depending on the type of input and goal of learning). We suggest that statistical learning not only provides a framework for studying language development and object knowledge in constrained laboratory settings, but also allows researchers to tackle real-world problems, such as multilingualism, the role of ever-changing learning environments, and differential developmental trajectories.

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“…Smith et al 2017) We have tested this hypothesis in a diachronic corpus study of highly ambiguous final clusters that arose through system-wide vowel deletion in unstressed syllables at the beginning of the Middle English period. In terms of data, it is based on the ECCE-database (ecce.univie.ac.at), which contains about 370.000 tokens of word forms ending in consonant clusters and covers the period from the middle of the 12 th to the middle of the 18 th century.…”

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confidence: 99%