Rebecca Frost

Publications

Displaying 1 - 16 of 16
  • Stärk, K., Kidd, E., & Frost, R. L. A. (2021). Word segmentation cues in German child-directed speech: A corpus analysis. Language and Speech. Advance online publication. doi:10.1177/0023830920979016.

    Abstract

    To acquire language, infants must learn to segment words from running speech. A significant body of experimental research shows that infants use multiple cues to do so; however, little research has comprehensively examined the distribution of such cues in naturalistic speech. We conducted a comprehensive corpus analysis of German child-directed speech (CDS) using data from the Child Language Data Exchange System (CHILDES) database, investigating the availability of word stress, transitional probabilities (TPs), and lexical and sublexical frequencies as potential cues for word segmentation. Seven hours of data (~15,000 words) were coded, representing around an average day of speech to infants. The analysis revealed that for 97% of words, primary stress was carried by the initial syllable, implicating stress as a reliable cue to word onset in German CDS. Word identity was also marked by TPs between syllables, which were higher within than between words, and higher for backwards than forwards transitions. Words followed a Zipfian-like frequency distribution, and over two-thirds of words (78%) were monosyllabic. Of the 50 most frequent words, 82% were function words, which accounted for 47% of word tokens in the entire corpus. Finally, 15% of all utterances comprised single words. These results give rich novel insights into the availability of segmentation cues in German CDS, and support the possibility that infants draw on multiple converging cues to segment their input. The data, which we make openly available to the research community, will help guide future experimental investigations on this topic.

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  • Frost, R. L. A., Jessop, A., Durrant, S., Peter, M. S., Bidgood, A., Pine, J. M., Rowland, C. F., & Monaghan, P. (2020). Non-adjacent dependency learning in infancy, and its link to language development. Cognitive Psychology, 120: 101291. doi:10.1016/j.cogpsych.2020.101291.

    Abstract

    To acquire language, infants must learn how to identify words and linguistic structure in speech. Statistical learning has been suggested to assist both of these tasks. However, infants’ capacity to use statistics to discover words and structure together remains unclear. Further, it is not yet known how infants’ statistical learning ability relates to their language development. We trained 17-month-old infants on an artificial language comprising non-adjacent dependencies, and examined their looking times on tasks assessing sensitivity to words and structure using an eye-tracked head-turn-preference paradigm. We measured infants’ vocabulary size using a Communicative Development Inventory (CDI) concurrently and at 19, 21, 24, 25, 27, and 30 months to relate performance to language development. Infants could segment the words from speech, demonstrated by a significant difference in looking times to words versus part-words. Infants’ segmentation performance was significantly related to their vocabulary size (receptive and expressive) both currently, and over time (receptive until 24 months, expressive until 30 months), but was not related to the rate of vocabulary growth. The data also suggest infants may have developed sensitivity to generalised structure, indicating similar statistical learning mechanisms may contribute to the discovery of words and structure in speech, but this was not related to vocabulary size.

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    Supplementary data
  • Frost, R., & Monaghan, P. (2020). Insights from studying statistical learning. In C. F. Rowland, A. L. Theakston, B. Ambridge, & K. E. Twomey (Eds.), Current Perspectives on Child Language Acquisition: How children use their environment to learn (pp. 65-89). Amsterdam: John Benjamins. doi:10.1075/tilar.27.03fro.

    Abstract

    Acquiring language is notoriously complex, yet for the majority of children this feat is accomplished with remarkable ease. Usage-based accounts of language acquisition suggest that this success can be largely attributed to the wealth of experience with language that children accumulate over the course of language acquisition. One field of research that is heavily underpinned by this principle of experience is statistical learning, which posits that learners can perform powerful computations over the distribution of information in a given input, which can help them to discern precisely how that input is structured, and how it operates. A growing body of work brings this notion to bear in the field of language acquisition, due to a developing understanding of the richness of the statistical information contained in speech. In this chapter we discuss the role that statistical learning plays in language acquisition, emphasising the importance of both the distribution of information within language, and the situation in which language is being learnt. First, we address the types of statistical learning that apply to a range of language learning tasks, asking whether the statistical processes purported to support language learning are the same or distinct across different tasks in language acquisition. Second, we expand the perspective on what counts as environmental input, by determining how statistical learning operates over the situated learning environment, and not just sequences of sounds in utterances. Finally, we address the role of variability in children’s input, and examine how statistical learning can accommodate (and perhaps even exploit) this during language acquisition.
  • Frost, R. L. A., Dunn, K., Christiansen, M. H., Gómez, R. L., & Monaghan, P. (2020). Exploring the "anchor word" effect in infants: Segmentation and categorisation of speech with and without high frequency words. PLoS One, 15(12): e0243436. doi:10.1371/journal.pone.0243436.

    Abstract

    High frequency words play a key role in language acquisition, with recent work suggesting they may serve both speech segmentation and lexical categorisation. However, it is not yet known whether infants can detect novel high frequency words in continuous speech, nor whether they can use them to help learning for segmentation and categorisation at the same time. For instance, when hearing “you eat the biscuit”, can children use the high-frequency words “you” and “the” to segment out “eat” and “biscuit”, and determine their respective lexical categories? We tested this in two experiments. In Experiment 1, we familiarised 12-month-old infants with continuous artificial speech comprising repetitions of target words, which were preceded by high-frequency marker words that distinguished the targets into two distributional categories. In Experiment 2, we repeated the task using the same language but with additional phonological cues to word and category structure. In both studies, we measured learning with head-turn preference tests of segmentation and categorisation, and compared performance against a control group that heard the artificial speech without the marker words (i.e., just the targets). There was no evidence that high frequency words helped either speech segmentation or grammatical categorisation. However, segmentation was seen to improve when the distributional information was supplemented with phonological cues (Experiment 2). In both experiments, exploratory analysis indicated that infants’ looking behaviour was related to their linguistic maturity (indexed by infants’ vocabulary scores) with infants with high versus low vocabulary scores displaying novelty and familiarity preferences, respectively. We propose that high-frequency words must reach a critical threshold of familiarity before they can be of significant benefit to learning.

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    data
  • The ManyBabies Consortium (2020). Quantifying sources of variability in infancy research using the infant-directed speech preference. Advances in Methods and Practices in Psychological Science, 30(1), 24-52. doi:10.1177/2515245919900809.

    Abstract

    Psychological scientists have become increasingly concerned with issues related to methodology and replicability, and infancy researchers in particular face specific challenges related to replicability: For example, high-powered studies are difficult to conduct, testing conditions vary across labs, and different labs have access to different infant populations. Addressing these concerns, we report on a large-scale, multisite study aimed at (a) assessing the overall replicability of a single theoretically important phenomenon and (b) examining methodological, cultural, and developmental moderators. We focus on infants’ preference for infant-directed speech (IDS) over adult-directed speech (ADS). Stimuli of mothers speaking to their infants and to an adult in North American English were created using seminaturalistic laboratory-based audio recordings. Infants’ relative preference for IDS and ADS was assessed across 67 laboratories in North America, Europe, Australia, and Asia using the three common methods for measuring infants’ discrimination (head-turn preference, central fixation, and eye tracking). The overall meta-analytic effect size (Cohen’s d) was 0.35, 95% confidence interval = [0.29, 0.42], which was reliably above zero but smaller than the meta-analytic mean computed from previous literature (0.67). The IDS preference was significantly stronger in older children, in those children for whom the stimuli matched their native language and dialect, and in data from labs using the head-turn preference procedure. Together, these findings replicate the IDS preference but suggest that its magnitude is modulated by development, native-language experience, and testing procedure.

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    Open Practices Disclosure Open Data OSF
  • Frost, R. L. A., Monaghan, P., & Christiansen, M. H. (2019). Mark my words: High frequency marker words impact early stages of language learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 45(10), 1883-1898. doi:10.1037/xlm0000683.

    Abstract

    High frequency words have been suggested to benefit both speech segmentation and grammatical categorization of the words around them. Despite utilizing similar information, these tasks are usually investigated separately in studies examining learning. We determined whether including high frequency words in continuous speech could support categorization when words are being segmented for the first time. We familiarized learners with continuous artificial speech comprising repetitions of target words, which were preceded by high-frequency marker words. Crucially, marker words distinguished targets into 2 distributionally defined categories. We measured learning with segmentation and categorization tests and compared performance against a control group that heard the artificial speech without these marker words (i.e., just the targets, with no cues for categorization). Participants segmented the target words from speech in both conditions, but critically when the marker words were present, they influenced acquisition of word-referent mappings in a subsequent transfer task, with participants demonstrating better early learning for mappings that were consistent (rather than inconsistent) with the distributional categories. We propose that high-frequency words may assist early grammatical categorization, while speech segmentation is still being learned.

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    Supplemental Material
  • Frost, R. L. A., Isbilen, E. S., Christiansen, M. H., & Monaghan, P. (2019). Testing the limits of non-adjacent dependency learning: Statistical segmentation and generalisation across domains. In A. K. Goel, C. M. Seifert, & C. Freksa (Eds.), Proceedings of the 41st Annual Meeting of the Cognitive Science Society (CogSci 2019) (pp. 1787-1793). Montreal, QB: Cognitive Science Society.

    Abstract

    Achieving linguistic proficiency requires identifying words from speech, and discovering the constraints that govern the way those words are used. In a recent study of non-adjacent dependency learning, Frost and Monaghan (2016) demonstrated that learners may perform these tasks together, using similar statistical processes - contrary to prior suggestions. However, in their study, non-adjacent dependencies were marked by phonological cues (plosive-continuant-plosive structure), which may have influenced learning. Here, we test the necessity of these cues by comparing learning across three conditions; fixed phonology, which contains these cues, varied phonology, which omits them, and shapes, which uses visual shape sequences to assess the generality of statistical processing for these tasks. Participants segmented the sequences and generalized the structure in both auditory conditions, but learning was best when phonological cues were present. Learning was around chance on both tasks for the visual shapes group, indicating statistical processing may critically differ across domains.
  • Isbilen, E., Frost, R. L. A., Monaghan, P., & Christiansen, M. (2018). Bridging artificial and natural language learning: Comparing processing- and reflection-based measures of learning. In C. Kalish, M. Rau, J. Zhu, & T. T. Rogers (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (CogSci 2018) (pp. 1856-1861). Austin, TX: Cognitive Science Society.

    Abstract

    A common assumption in the cognitive sciences is that artificial and natural language learning rely on shared mechanisms. However, attempts to bridge the two have yielded ambiguous results. We suggest that an empirical disconnect between the computations employed during learning and the methods employed at test may explain these mixed results. Further, we propose statistically-based chunking as a potential computational link between artificial and natural language learning. We compare the acquisition of non-adjacent dependencies to that of natural language structure using two types of tasks: reflection-based 2AFC measures, and processing-based recall measures, the latter being more computationally analogous to the processes used during language acquisition. Our results demonstrate that task-type significantly influences the correlations observed between artificial and natural language acquisition, with reflection-based and processing-based measures correlating within – but not across – task-type. These findings have fundamental implications for artificial-to-natural language comparisons, both methodologically and theoretically.
  • Frost, R. L. A., Monaghan, P., & Tatsumi, T. (2017). Domain-general mechanisms for speech segmentation: The role of duration information in language learning. Journal of Experimental Psychology: Human Perception and Performance, 43(3), 466-476. doi:10.1037/xhp0000325.

    Abstract

    Speech segmentation is supported by multiple sources of information that may either inform language processing specifically, or serve learning more broadly. The Iambic/Trochaic Law (ITL), where increased duration indicates the end of a group and increased emphasis indicates the beginning of a group, has been proposed as a domain-general mechanism that also applies to language. However, language background has been suggested to modulate use of the ITL, meaning that these perceptual grouping preferences may instead be a consequence of language exposure. To distinguish between these accounts, we exposed native-English and native-Japanese listeners to sequences of speech (Experiment 1) and nonspeech stimuli (Experiment 2), and examined segmentation using a 2AFC task. Duration was manipulated over 3 conditions: sequences contained either an initial-item duration increase, or a final-item duration increase, or items of uniform duration. In Experiment 1, language background did not affect the use of duration as a cue for segmenting speech in a structured artificial language. In Experiment 2, the same results were found for grouping structured sequences of visual shapes. The results are consistent with proposals that duration information draws upon a domain-general mechanism that can apply to the special case of language acquisition
  • Frost, R. L. A., & Monaghan, P. (2017). Sleep-driven computations in speech processing. PLoS One, 12(1): e0169538. doi:10.1371/journal.pone.0169538.

    Abstract

    Acquiring language requires segmenting speech into individual words, and abstracting over those words to discover grammatical structure. However, these tasks can be conflicting—on the one hand requiring memorisation of precise sequences that occur in speech, and on the other requiring a flexible reconstruction of these sequences to determine the grammar. Here, we examine whether speech segmentation and generalisation of grammar can occur simultaneously—with the conflicting requirements for these tasks being over-come by sleep-related consolidation. After exposure to an artificial language comprising words containing non-adjacent dependencies, participants underwent periods of consolidation involving either sleep or wake. Participants who slept before testing demonstrated a sustained boost to word learning and a short-term improvement to grammatical generalisation of the non-adjacencies, with improvements after sleep outweighing gains seen after an equal period of wake. Thus, we propose that sleep may facilitate processing for these conflicting tasks in language acquisition, but with enhanced benefits for speech segmentation.

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    Data available
  • Monaghan, P., Brand, J., Frost, R. L. A., & Taylor, G. (2017). Multiple variable cues in the environment promote accurate and robust word learning. In G. Gunzelman, A. Howes, T. Tenbrink, & E. Davelaar (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society (CogSci 2017) (pp. 817-822). Retrieved from https://mindmodeling.org/cogsci2017/papers/0164/index.html.

    Abstract

    Learning how words refer to aspects of the environment is a complex task, but one that is supported by numerous cues within the environment which constrain the possibilities for matching words to their intended referents. In this paper we tested the predictions of a computational model of multiple cue integration for word learning, that predicted variation in the presence of cues provides an optimal learning situation. In a cross-situational learning task with adult participants, we varied the reliability of presence of distributional, prosodic, and gestural cues. We found that the best learning occurred when cues were often present, but not always. The effect of variability increased the salience of individual cues for the learner, but resulted in robust learning that was not vulnerable to individual cues’ presence or absence. Thus, variability of multiple cues in the language-learning environment provided the optimal circumstances for word learning.
  • Frost, R. L. A., & Monaghan, P. (2016). Simultaneous segmentation and generalisation of non-adjacent dependencies from continuous speech. Cognition, 147, 70-74. doi:10.1016/j.cognition.2015.11.010.

    Abstract

    Language learning requires mastering multiple tasks, including segmenting speech to identify words, and learning the syntactic role of these words within sentences. A key question in language acquisition research is the extent to which these tasks are sequential or successive, and consequently whether they may be driven by distinct or similar computations. We explored a classic artificial language learning paradigm, where the language structure is defined in terms of non-adjacent dependencies. We show that participants are able to use the same statistical information at the same time to segment continuous speech to both identify words and to generalise over the structure, when the generalisations were over novel speech that the participants had not previously experienced. We suggest that, in the absence of evidence to the contrary, the most economical explanation for the effects is that speech segmentation and grammatical generalisation are dependent on similar statistical processing mechanisms.
  • Frost, R. L. A., Monaghan, P., & Christiansen, M. H. (2016). Using Statistics to Learn Words and Grammatical Categories: How High Frequency Words Assist Language Acquisition. In A. Papafragou, D. Mirman, & J. Trueswell (Eds.), Proceedings of the 38th Annual Meeting of the Cognitive Science Society (CogSci 2016) (pp. 81-86). Austin, Tx: Cognitive Science Society. Retrieved from https://mindmodeling.org/cogsci2016/papers/0027/index.html.

    Abstract

    Recent studies suggest that high-frequency words may benefit speech segmentation (Bortfeld, Morgan, Golinkoff, & Rathbun, 2005) and grammatical categorisation (Monaghan, Christiansen, & Chater, 2007). To date, these tasks have been examined separately, but not together. We familiarised adults with continuous speech comprising repetitions of target words, and compared learning to a language in which targets appeared alongside high-frequency marker words. Marker words reliably preceded targets, and distinguished them into two otherwise unidentifiable categories. Participants completed a 2AFC segmentation test, and a similarity judgement categorisation test. We tested transfer to a word-picture mapping task, where words from each category were used either consistently or inconsistently to label actions/objects. Participants segmented the speech successfully, but only demonstrated effective categorisation when speech contained high-frequency marker words. The advantage of marker words extended to the early stages of the transfer task. Findings indicate the same high-frequency words may assist speech segmentation and grammatical categorisation.
  • Frost, R. (2014). Learning grammatical structures with and without sleep. PhD Thesis, Lancaster University, Lancaster.
  • Gaskell, M. G., Warker, J., Lindsay, S., Frost, R. L. A., Guest, J., Snowdon, R., & Stackhouse, A. (2014). Sleep Underpins the Plasticity of Language Production. Psychological Science, 25(7), 1457-1465. doi:10.1177/0956797614535937.

    Abstract

    The constraints that govern acceptable phoneme combinations in speech perception and production have considerable plasticity. We addressed whether sleep influences the acquisition of new constraints and their integration into the speech-production system. Participants repeated sequences of syllables in which two phonemes were artificially restricted to syllable onset or syllable coda, depending on the vowel in that sequence. After 48 sequences, participants either had a 90-min nap or remained awake. Participants then repeated 96 sequences so implicit constraint learning could be examined, and then were tested for constraint generalization in a forced-choice task. The sleep group, but not the wake group, produced speech errors at test that were consistent with restrictions on the placement of phonemes in training. Furthermore, only the sleep group generalized their learning to new materials. Polysomnography data showed that implicit constraint learning was associated with slow-wave sleep. These results show that sleep facilitates the integration of new linguistic knowledge with existing production constraints. These data have relevance for systems-consolidation models of sleep.

    Additional information

    https://osf.io/zqg9y/
  • Frost, R. L. A., Gaskell, G., Warker, J., Guest, J., Snowdon, R., & Stackhouse, A. (2012). Sleep Facilitates Acquisition of Implicit Phonotactic Constraints in Speech Production. Journal of sleep research, 21(s1), 249-249. doi:10.1111/j.1365-2869.2012.01044.x.

    Abstract

    Sleep plays an important role in neural reorganisation which underpins memory consolidation. The gradual replacement of hippocampal binding of new memories with intracortical connections helps to link new memories to existing knowledge. This process appears to be faster for memories which fit more easily into existing schemas. Here we seek to investigate whether this more rapid consolidation of schema-conformant information is facilitated by sleep, and the neural basis of this process.

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