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  • Ramscar Lab 10:13 on 03.20.2021 Permalink  

    New Paper

    Language learning as uncertainty reduction: The role of prediction error in linguistic generalization and item-learning

    with Maša Vujovića and Elizabeth Wonnacott

    https://www.sciencedirect.com/science/article/pii/S0749596X21000140?dgcid=coauthor

    Abstract

    Discriminative theories frame language learning as a process of reducing uncertainty about the meaning of an utterance by discriminating informative from uninformative cues via the mechanisms of prediction error and cue competition. Previous work showed that discriminative learning is affected by the order in which information is presented during language learning. Specifically, learning suffixes, where complex stems precede affixes, promotes better generalization than prefixing, which tends to promote better item-learning instead. We explored this in two large-scale web-based artificial language learning experiments with adult learners (total N = 434), as well as two computational simulations implementing a discriminative learning model. While we did not find an overall benefit of suffixing over prefixing in generalization, consistent with our theoretical and computational predictions, we found that participants in the prefix condition were unable to discriminate between frequent, but uninformative cues and low-frequency, informative cues. This resulted in them being more likely to show incorrect overgeneralization of that feature for low frequency test items than participants in the suffix condition. We did not find a benefit of prefixing in item learning (although there was overall better item-learning of low type-frequency items), which we discuss in terms of the methodological limitations of our empirical paradigm. Taken together, these results underline the crucial role prediction error plays in learning linguistic generalization, and have implications for how generalization interacts with item-learning.

     
  • Ramscar Lab 23:49 on 03.07.2021 Permalink  

    New Paper

    Simulating the Acquisition of Verb Inflection in Typically Developing Children and Children With Developmental Language Disorder in English and Spanish

    With Daniel Freudenthal, Laurence B. Leonard and Julian M. Pine

    https://onlinelibrary.wiley.com/doi/10.1111/cogs.12945

    Abstract

    Children with developmental language disorder (DLD) have significant deficits in language ability that cannot be attributed to neurological damage, hearing impairment, or intellectual disability. The symptoms displayed by children with DLD differ across languages. In English, DLD is often marked by severe difficulties acquiring verb inflection. Such difficulties are less apparent in languages with rich verb morphology like Spanish and Italian. Here we show how these differential profiles can be understood in terms of an interaction between properties of the input language, and the child’s ability to learn predictive relations between linguistic elements that are separated within a sentence. We apply a simple associative learning model to sequential English and Spanish stimuli and show how the model’s ability to associate cues occurring earlier in time with later outcomes affects the acquisition of verb inflection in English more than in Spanish. We relate this to the high frequency of the English bare form (which acts as a default) and the English process of question formation, which means that (unlike in Spanish) bare forms frequently occur in third‐person singular contexts. Finally, we hypothesize that the pro‐drop nature of Spanish makes it easier to associate person and number cues with the verb inflection than in English. Since the factors that conspire to make English verb inflection particularly challenging for learners with weak sequential learning abilities are much reduced or absent in Spanish, this provides an explanation for why learning Spanish verb inflection is relatively unaffected in children with DLD.

     
  • Ramscar Lab 02:17 on 01.14.2021 Permalink  

    New Paper

    Representing absence of evidence: why algorithms and representations matter in models of language and cognition

    With Franziska Bröker (http://franziskabroeker.com)

    https://www.tandfonline.com/doi/full/10.1080/23273798.2020.1862257

    Abstract

    Theories of language and cognition develop iteratively from ideas, experiments and models. The abstract nature of “cognitive processes” means that computational models play a critical role in this, yet bridging the gaps between models, data, and interpretations is challenging. While the how and why computations are performed is often the primary research focus, the conclusions drawn from models can be compromised by the representations chosen for them. To illustrate this point, we revisit a set of empirical studies of language acquisition that appear to support different models of learning from implicit negative evidence. We examine the degree to which these conclusions were influenced by the representations chosen and show how a plausible single mechanism account of the data can be formulated for representations that faithfully capture the task design. The need for input representations to be incorporated into model conceptualisations, evaluations, and comparisons is discussed.

     
  • Ramscar Lab 04:52 on 01.07.2021 Permalink  

    New Paper

    Articulatory Variability is Reduced by Repetition and Predictability

    With Fabian Tomaschek, Denis Arnold, Konstantin Sering, Benjamin Tucker and, Jacolien van Rij (http://jacolienvanrij.com)

    https://journals.sagepub.com/doi/abs/10.1177/0023830920948552

    Abstract

    Repeating the movements associated with activities such as drawing or sports typically leads to improvements in kinematic behavior: these movements become faster, smoother, and exhibit less variation. Likewise, practice has also been shown to lead to faster and smoother movement trajectories in speech articulation. However, little is known about its effect on articulatory variability. To address this, we investigate the extent to which repetition and predictability influence the articulation of the frequent German word “sie” [zi] (they). We find that articulatory variability is proportional to speaking rate and the duration of [zi], and that overall variability decreases as [zi] is repeated during the experiment. Lower variability is also observed as the conditional probability of [zi] increases, and the greatest reduction in variability occurs during the execution of the vocalic target of [i]. These results indicate that practice can produce observable differences in the articulation of even the most common gestures used in speech.

     
  • Ramscar Lab 06:26 on 11.09.2020 Permalink
    Tags: ,   

    New Paper

    Order Matters! Influences of Linear Order on Linguistic Category Learning

    With Dorothée Hoppe (https://dorohoppe.github.io), Jacolien van Rij (http://jacolienvanrij.com) and Petra Hendriks

    https://onlinelibrary.wiley.com/doi/10.1111/cogs.12910

    Abstract

    Linguistic category learning has been shown to be highly sensitive to linear order, and depending on the task, differentially sensitive to the information provided by preceding category markers (premarkers, e.g., gendered articles) or succeeding category markers (postmarkers, e.g., gendered suffixes). Given that numerous systems for marking grammatical categories exist in natural languages, it follows that a better understanding of these findings can shed light on the factors underlying this diversity. In two discriminative learning simulations and an artificial language learning experiment, we identify two factors that modulate linear order effects in linguistic category learning: category structure and the level of abstraction in a category hierarchy. Regarding category structure, we find that postmarking brings an advantage for learning category diagnostic stimulus dimensions, an effect not present when categories are non‐confusable. Regarding levels of abstraction, we find that premarking of super‐ordinate categories (e.g., noun class) facilitates learning of subordinate categories (e.g., nouns). We present detailed simulations using a plausible candidate mechanism for the observed effects, along with a comprehensive analysis of linear order effects within an expectation‐based account of learning. Our findings indicate that linguistic category learning is differentially guided by pre‐ and postmarking, and that the influence of each is modulated by the specific characteristics of a given category system.

     
  • Ramscar Lab 04:17 on 06.03.2020 Permalink
    Tags: ,   

    New Paper

    Quantifying the speech-gesture relation with massive multimodal datasets: Informativity in time expressions

    with Cristóbal Pagán Cánovas, Javier Valenzuel, Daniel Alcaraz Carrión and Inés Olza,

    Abstract

    The development of large-scale corpora has led to a quantum leap in our understanding of speech in recent years. By contrast, the analysis of massive datasets has so far had a limited impact on the study of gesture and other visual communicative behaviors. We utilized the UCLA-Red Hen Lab multi-billion-word repository of video recordings, all of them showing communicative behavior that was not elicited in a lab, to quantify speech-gesture co-occurrence frequency for a subset of linguistic expressions in American English. First, we objectively establish a systematic relationship in the high degree of co-occurrence between gesture and speech in our subset of expressions, which consists of temporal phrases. Second, we show that there is a systematic alignment between the informativity of co-speech gestures and that of the verbal expressions with which they co-occur. By exposing deep, systematic relations between the modalities of gesture and speech, our results pave the way for the data-driven integration of multimodal behavior into our understanding of human communication.

    https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0233892

     
  • Ramscar Lab 16:29 on 01.10.2016 Permalink
    Tags: , , Decision Making, ,   

    Exploratory Decision-Making as a Function of Lifelong Experience, Not Cognitive Decline 

    Our latest aging study has just been published in the Journal of Experimental Psychology: General. The paper reports work done with Nate Blanco, Kirsten Smayda and Todd Maddox at the University of Texas, Austin, Bradley Love (University College London) and Ross Otto (New York University). It helps illustrate just how important computational models are to our understanding of changes in behavior across the adult lifespan, showing why it makes no sense to talk about processing declines in the absence of models of what is being processed, and how.

    Here’s the abstract:

    Older adults perform worse than younger adults in some complex decision-making scenarios, which is commonly attributed to age-related declines in striatal and frontostriatal processing. Recently, this popular account has been challenged by work that considered how older adults’ performance may differ as a function of greater knowledge and experience, and by work showing that, in some cases, older adults outperform younger adults in complex decision-making tasks. In light of this controversy, we examined the performance of older and younger adults in an exploratory choice task that is amenable to model-based analyses and ostensibly not reliant on prior knowledge. Exploration is a critical aspect of decision-making poorly understood across the life span. Across 2 experiments, we addressed (a) how older and younger adults differ in exploratory choice and (b) to what extent observed differences reflect processing capacity declines. Model-based analyses suggested that the strategies used by the 2 groups were qualitatively different, resulting in relatively worse performance for older adults in 1 decision- making environment but equal performance in another. Little evidence was found that differences in processing capacity drove performance differences. Rather the results suggested that older adults’ performance might result from applying a strategy that may have been shaped by their wealth of real-word decision-making experience. While this strategy is likely to be effective in the real world, it is ill suited to some decision environments. These results underscore the importance of taking into account effects of experience in aging studies, even for tasks that do not obviously tap past experiences.

     
  • Ramscar Lab 00:41 on 07.12.2015 Permalink  

    CogSci Symposium: Generative and Discriminative Models in Cognitive Science 

    Brad Love and I are organizing a symposium at this year’s Cognitive Science Conference, with guest speakers Matt Jones and Tom Griffiths. Come check it out!

    One popular distinction in machine learning is between discriminative and generative models (Ng & Jordan, 2001). Given the cross fertilization between research in human and machine learning, the time is ripe to ask whether the mind is a generative or discriminative learning device. This symposium tackles this question from a variety of perspectives. The aim is to explore the explanatory value of these two basic views of learning, which cut across existing distinctions in cognitive science (e.g., connectionist vs. Bayesian approaches).

     
  • Ramscar Lab 05:57 on 05.24.2015 Permalink  

    New Series by Language Science Press 

    Together with Jim Blevins and Petar Milin, I will be co-editing a new series, Morphological Investigations, published through Language Science Press. We are inviting cross-disciplinary contributions, ranging from detailed descriptive studies through to quantitative analyses, simulations and models of learning and use. For more information, please consult the open call for submissions.

     
  • Ramscar Lab 00:02 on 06.11.2014 Permalink  

    Ageing Research Featured on BBC Radio4 

    The team’s ageing research features today on BBC Radio4’s Frontiers, a programme “exploring new ideas in science and meeting the scientists and researchers responsible for them”. The episode can be streamed here.

    Geoff Watts investigates the latest thinking about our brain power in old age.

    He meets researchers who argue that society has overly negative views of the mental abilities of the elderly – a dismal and fatalistic outlook which is not backed up by recent discoveries and theories.

    Geoff talks to Professor Lorraine Tyler who leads a large study in Cambridge (CamCAN) which is comparing cognition and brain structure and function in 700 people aged between 18 and 88 years old.

    He also meets scientists and participants involved in an unique study of cognition and ageing at the University of Edinburgh. It has traced hundreds of people who were given a nationwide intelligence test as children in 1932 and 1947. Since the year 2000, the study has been retesting their intelligence and mental agility in their 70s to 90s. The Lothian Birth Cohort study is revealing what we all might do in life to keep our minds fast and sharp well into old age.

    One new and controversial idea holds that cognitive decline is in fact a myth. A team in Germany, led by Michael Ramscar, argues that older people perform less well in intelligence and memory tests because they know so much more than younger subjects and not because their brains are deteriorating. Simply put, their larger stores of accumulated knowledge slow their performance. Their brains take longer to retrieve the answers from their richer memory stores.

     
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