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

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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.

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.

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.

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.

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.

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.

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.

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.

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