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  • Unknown's avatar

    Ramscar Lab 08:23 on 01.15.2026 Permalink  

    New Paper

    Sequence structure in children’s speech reveals non-linear development of relations between word categories

    With: Maja Linke

    https://www.nature.com/articles/s44271-025-00380-w

    Abstract

    Why do children learn some words earlier than others? Can children’s speech patterns reveal how their evolving models of language determine what they learn? This study presents a systemic analysis of children’s speech using low-dimensional embeddings to examine how the contextual knowledge reflected in their utterances reorganizes as linguistic experience increases. We analyzed age-stratified samples from the CHILDES database (18–36 months: n = 1,693,641 tokens; 3–6 years: n = 1,750,007; 5–12 years: n = 1,721,828) and adult speech from the SUBS2VEC subtitle corpus (n = 1,742,885). Our results suggest that the order and position of words in sequences produced by children from different age groups reflect changes in the way they represent categories of words. Rather than being ungrammatical, children’s utterances appear to be structured by temporary grammars that optimize the distribution of information in sequences. The results point to shifts in how words are organized in semantic space, reflecting the gradual alignment of lexical categories during learning; this restructuring appears to draw on functionally ambiguous (multipurpose) categories in English. These findings are somewhat counterintuitive, as they suggest that not knowing the exact meaning of words can facilitate both learning and communication.

     
  • Unknown's avatar

    Ramscar Lab 02:19 on 06.20.2024 Permalink  

    New Paper

    The Effects of Linear Order in Category Learning: Some
    Replications of Ramscar et al. (2010) and Their
    Implications for Replicating Training Studies

    With: Eva Viviani and Elizabeth Wonnacott

    https://onlinelibrary.wiley.com/doi/pdf/10.1111/cogs.13445

    Abstract

    Ramscar, Yarlett, Dye, Denny, and Thorpe (2010) showed how, consistent with the predictions of
    error-driven learning models, the order in which stimuli are presented in training can affect category
    learning. Specifically, learners exposed to artificial language input where objects preceded their labels
    learned the discriminating features of categories better than learners exposed to input where labels preceded objects. We sought to replicate this finding in two online experiments employing the same tests
    used originally: A four pictures test (match a label to one of four pictures) and a four labels test (match
    a picture to one of four labels). In our study, only findings from the four pictures test were consistent
    with the original result. Additionally, the effect sizes observed were smaller, and participants overgeneralized high-frequency category labels more than in the original study. We suggest that although
    Ramscar, Yarlett, Dye, Denny, and Thorpe (2010) feature-label order predictions were derived from
    error-driven learning, they failed to consider that this mechanism also predicts that performance in
    any training paradigm must inevitably be influenced by participant prior experience. We consider our
    findings in light of these factors, and discuss implications for the generalizability and replication of
    training studies.

     
  • Unknown's avatar

    Ramscar Lab 01:23 on 05.02.2024 Permalink  

    New Paper

    The Keys to the Future? An Examination of Statistical Versus Discriminative Accounts of Serial Pattern Learning

    With: Fabian Tomaschek and Jessie S. Nixon

    https://onlinelibrary.wiley.com/doi/full/10.1111/cogs.13404

    Abstract

    Sequence learning is fundamental to a wide range of cognitive functions. Explaining how sequences—and the relations between the elements they comprise—are learned is a fundamental challenge to cognitive science. However, although hundreds of articles addressing this question are published each year, the actual learning mechanisms involved in the learning of sequences are rarely investigated. We present three experiments that seek to examine these mechanisms during a typing task. Experiments 1 and 2 tested learning during typing single letters on each trial. Experiment 3 tested for “chunking” of these letters into “words.” The results of these experiments were used to examine the mechanisms that could best account for them, with a focus on two particular proposals: statistical transitional probability learning and discriminative error-driven learning. Experiments 1 and 2 showed that error-driven learning was a better predictor of response latencies than either n-gram frequencies or transitional probabilities. No evidence for chunking was found in Experiment 3, probably due to interspersing visual cues with the motor response. In addition, learning occurred across a greater distance in Experiment 1 than Experiment 2, suggesting that the greater predictability that comes with increased structure leads to greater learnability. These results shed new light on the mechanism responsible for sequence learning. Despite the widely held assumption that transitional probability learning is essential to this process, the present results suggest instead that the sequences are learned through a process of discriminative learning, involving prediction and feedback from prediction error.

     
  • Unknown's avatar

    Ramscar Lab 03:57 on 06.05.2023 Permalink  

    New Paper

    Variability and abstraction in evaluative conditioning: Consequences for the generalization of likes and dislikes

    With: Kathrin Reichmann, Mandy Hütter and Barbara Kaup

    https://www.sciencedirect.com/science/article/abs/pii/S0022103123000355?CMX_ID=&SIS_ID=&dgcid=STMJ_AUTH_SERV_PUBLISHED&utm_acid=100098200&utm_campaign=STMJ_AUTH_SERV_PUBLISHED&utm_in=DM368176&utm_medium=email&utm_source=AC_

    Abstract

    The present work examines whether the variability of attitude objects at attitude acquisition increases the generalization of likes and dislikes. In particular, variability might enhance the discriminative learning of cues, resulting in attitudes towards abstract entities rather than concrete instances. Using evaluative conditioning as an experimental paradigm to study attitude acquisition, we manipulated the variability of conditioned stimuli (CSs) that were paired with unconditioned stimuli (USs) of negative or positive valence. CSs resembled Chinese characters that could be grouped into categories by one common component. In the invariable condition, one item per category served as CSs. In the variable condition, multiple items per category were used as CSs. We measured participants’ evaluations of the CSs and novel Chinese characters (generalization stimuli), and included a recognition memory task and evaluative measures of CS components. As compared to the invariable condition, the learning condition that introduced variability among CSs facilitated generalization towards novel stimuli, diminished recognition memory performance, and produced evaluative ratings of CS components that were more extreme for common components. The findings suggest the formation of attitudes towards abstract cues rather than concrete instances in the variable relative to the invariable condition, and propose that high variability facilitates the generalization of likes and dislikes. We discuss mechanistic explanations as well as practical implications with regard to the formation of prejudice and intergroup biases.

     
  • Unknown's avatar

    Ramscar Lab 03:26 on 04.02.2022 Permalink  

    New Paper

    Psycholinguistics and Aging

    Oxford Research Encyclopedia of Linguistics. https://oxfordre.com/linguistics/view/10.1093/acrefore/9780199384655.001.0001/acrefore-9780199384655-e-374.

    Summary

    Healthy aging is associated with many cognitive, linguistic, and behavioral changes. For example, adults’ reaction times slow on many tasks as they grow older, while their memories, appear to fade, especially for apparently basic linguistic information such as other people’s names. These changes have traditionally been thought to reflect declines in the processing power of human minds and brains as they age. However, from the perspective of the information-processing paradigm that dominates the study of mind, the question of whether cognitive processing capacities actually decline across the life span can only be scientifically answered in relation to functional models of the information processes that are presumed to be involved in cognition.

    Consider, for example, the problem of recalling someone’s name. We are usually reminded of the names of friends on a regular basis, and this makes us good at remembering them. However, as we move through life, we inevitably learn more names. Sometimes we hear these new names only once. As we learn each new name, the average exposure we will have had to any individual name we know is likely to decline, while the number of different names we know is likely to increase. This in turn is likely to make the task of recalling a particular name more complex. One consequence of this is as follows: If Mary can only recall names with 95% accuracy at age 60—when she knows 900 names—does she necessarily have a worse memory than she did at age 16, when she could recall any of only 90 names with 98% accuracy? Answering the question of whether Mary’s memory for names has actually declined (or improved even) will require some form of quantification of Mary’s knowledge of names at any given point in her life and the definition of a quantitative model that predicts expected recall performance for a given amount of name knowledge, as well as an empirical measure of the accuracy of the model across a wide range of circumstances.

    Until the early 21st century, the study of cognition and aging was dominated by approaches that failed to meet these requirements. Researchers simply established that Mary’s name recall was less accurate at a later age than it was at an earlier one, and took this as evidence that Mary’s memory processes had declined in some significant way. However, as computational approaches to studying cognitive—and especially psycholinguistic—processes and processing became more widespread, a number of matters related to the development of processing across the life span began to become apparent: First, the complexity involved in establishing whether or not Mary’s name recall did indeed become less accurate with age began to be better understood. Second, when the impact of learning on processing was controlled for, it became apparent that at least some processes showed no signs of decline at all in healthy aging. Third, the degree to which the environment—both in terms of its structure, and its susceptibility to change—further complicates our understanding of life-span cognitive performance also began to be better comprehended. These new findings not only promise to change our understanding of healthy cognitive aging, but also seem likely to alter our conceptions of cognition and language themselves.

    Click to access Ramscar-Psycholinguistics%20and%20Aging-2022.pdf

     
  • Unknown's avatar

    Ramscar Lab 01:51 on 01.17.2022 Permalink  

    New Paper

    An exploration of error-driven learning in simple two-layer networks from a discriminative learning perspective

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

    https://rdcu.be/cE1Qj

    Abstract

    Error-driven learning algorithms, which iteratively adjust expectations based on prediction error, are the basis for a vast array of computational models in the brain and cognitive sciences that often differ widely in their precise form and application: they range from simple models in psychology and cybernetics to current complex deep learning models dominating discussions in machine learning and artificial intelligence. However, despite the ubiquity of this mechanism, detailed analyses of its basic workings uninfluenced by existing theories or specific research goals are rare in the literature. To address this, we present an exposition of error-driven learning – focusing on its simplest form for clarity – and relate this to the historical development of error-driven learning models in the cognitive sciences. Although historically error-driven models have been thought of as associative, such that learning is thought to combine preexisting elemental representations, our analysis will highlight the discriminative nature of learning in these models and the implications of this for the way how learning is conceptualized. We complement our theoretical introduction to error-driven learning with a practical guide to the application of simple error-driven learning models in which we discuss a number of example simulations, that are also presented in detail in an accompanying tutorial.

     
  • Unknown's avatar

    Ramscar Lab 05:34 on 01.11.2022 Permalink  

    New Paper

    A discriminative account of the learning, representation and processing of inflection systems

    https://tinyurl.com/yv72f2ne

    Abstract

    What kind of knowledge accounts for linguistic productivity? How is it acquired? For years, debate on these questions has focused on a seemingly obscure domain: inflectional morphology. On one side, theorists inspired by Rumelhart & McClelland’s classic error-driven learning model have sought to show how all morphological forms are the products of a single memory-based process, whereas the opposing theories have claimed that irregular forms are processed by qualitatively different mechanisms to rule-governed regulars. This review argues that while the main ideas put forward by Rumelhart & McClelland – that inflectional patterns are learned, and rule-like behavior emerges from the distribution of forms – appear to be correct, the theory embodied in their model (and those following it) is incompatible with the discriminative nature of learning itself. An examination of the constraints error-driven learning mechanisms impose on theories of morphological processing – along with language learning and human communication itself – is presented.

     
  • Unknown's avatar

    Ramscar Lab 01:18 on 09.29.2021 Permalink  

    New Paper

    How children learn to communicate discriminatively

    https://www.cambridge.org/core/journals/journal-of-child-language/article/how-children-learn-to-communicate-discriminatively/25796886D9D5A892B661DAA39A77DA2C#

    Abstract

    How do children learn to communicate, and what do they learn? Traditionally, most theories have taken an associative, compositional approach to these questions, supposing children acquire an inventory of form-meaning associations, and procedures for composing / decomposing them; into / from messages in production and comprehension. This paper presents an alternative account of human communication and its acquisition based on the systematic, discriminative approach embodied in psychological and computational models of learning, and formally described by communication theory. It describes how discriminative learning theory offers an alternative perspective on the way that systems of semantic cues are conditioned onto communicative codes, while information theory provides a very different view of the nature of the codes themselves. It shows how the distributional properties of languages satisfy the communicative requirements described in information theory, enabling language learners to align their expectations despite the vastly different levels of experience among language users, and to master communication systems far more abstract than linguistic intuitions traditionally assume. Topics reviewed include morphological development, the acquisition of verb argument structures, and the functions of linguistic systems that have proven to be stumbling blocks for compositional theories: grammatical gender and personal names.

     
  • Unknown's avatar

    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.

     
  • Unknown's avatar

    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.

     
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