Rebecca Frost

Publications

Displaying 1 - 12 of 12
  • The ManyBabies Consortium (in press). Quantifying sources of variability in infancy research using the infant-directed speech preference. Advances in Methods and Practices in Psychological Science.
  • 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.

    Supplementary material

    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.

    Supplementary material

    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., & 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. 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. (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.

    Supplementary material

    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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