Symbols enable people to organize and communicate about the world. However, the ways in which symbolic knowledge is learned and then represented in the mind are poorly understood. We present a formal analysis of symbolic learning-in particular, word learning-in terms of prediction and cue competition, and we consider two possible ways in which symbols might be learned: by learning to predict a label from the features of objects and events in the world, and by learning to predict features from a label. This analysis predicts significant differences in symbolic learning depending on the sequencing of objects and labels. We report a computational simulation and two human experiments that confirm these differences, revealing the existence of Feature-Label-Ordering effects in learning. Discrimination learning is facilitated when objects predict labels, but not when labels predict objects. Our results and analysis suggest that the semantic categories people use to understand and communicate about the world can only be learned if labels are predicted from objects. We discuss the implications of this for our understanding of the nature of language and symbolic thought, and in particular, for theories of reference.Keywords: Language; Learning; Representation; Concepts; Computational modeling; Prediction Symbolic thought and symbolic communication are defining human characteristics. Yet despite the benefits symbols bring in allowing us to organize, communicate about, manipulate, and master the world, our understanding of symbols and symbolic knowledge is poor. Centuries of pondering the nature of symbolic representation, in terms of concepts and categories and words and their meanings, has yielded more puzzles than answers (Murphy, 2002; Wittgenstein, 1953). Our impoverished understanding of symbolic learning, and especially how words and their meanings are learned, represented, and used, contrasts starkly with the progress made in other areas, where computational models of learning processes Correspondence should be sent to Michael Ramscar,

As children learn their mother tongues, they make systematic errors. For example, English-speaking children regularly say mouses rather than mice . Because children’s errors are not explicitly corrected, it has been argued that children could never learn to make the transition to adult language based on the evidence available to them, and thus that learning even simple aspects of grammar is logically impossible without recourse to innate, language-specific constraints. Here, we examine the role children’s expectations play in language learning and present a model of plural noun learning that generates a surprising prediction: at a given point in learning, exposure to regular plurals (e.g. rats ) can decrease children’s tendency to overregularize irregular plurals (e.g. mouses ). Intriguingly, the model predicts that the same exposure should have the opposite effect earlier in learning. Consistent with this, we show that testing memory for items with regular plural labels contributes to a decrease in irregular plural overregularization in six-year-olds, but to an increase in four-year-olds. Our model and results suggest that children’s overregularization errors both arise and resolve themselves as a consequence of the distribution of error in the linguistic environment, and that far from presenting a logical puzzle for learning, they are inevitable consequences of it.

In a series of analyses over mega datasets, Jones, Johns, and Recchia (Canadian Journal of Experimental Psychology, 66(2), [115][116][117][118][119][120][121][122][123][124] 2012) and Johns et al. (Journal of the Acoustical Society of America, 132:2, EL74-EL80, 2012) found that a measure of contextual diversity that takes into account the semantic variability of a word's contexts provided a better fit to both visual and spoken word recognition data than traditional measures, such as word frequency or raw context counts. This measure was empirically validated with an artificial language experiment (Jones et al.). The present study extends the empirical results with a unique natural language learning paradigm, which allows for an examination of the semantic representations that are acquired as semantic diversity is varied. Subjects were incidentally exposed to novel words as they rated short selections from articles, books, and newspapers. When novel words were encountered across distinct discourse contexts, subjects were both faster and more accurate at recognizing them than when they were seen in redundant contexts. However, learning across redundant contexts promoted the development of more stable semantic representations. These findings are predicted by a distributional learning model trained on the same materials as our subjects.

The question of how children learn the meanings of words has long puzzled philosophers and psychologists. As Quine famously pointed out, simply hearing a word in context reveals next to nothing about its meaning. How then do children learn to understand and use words correctly? Here, we show how learning theory can offer an elegant solution to this seemingly intractable puzzle in language acquisition. From it, we derived formal predictions about word learning in situations of Quinean ambiguity, and subsequently tested our predictions on toddlers, undergraduates, and developmental psychologists. The toddlers' performance was consistent both with our predictions and with the workings of implicit mechanisms that can facilitate the learning of meaningful lexical systems. Adults adopted a markedly different and likely suboptimal strategy. These results suggest one explanation for why early word learning can appear baffling: Adult intuitions may be a poor source of insight into how children learn.

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Recently, a new crowd-sourced language metric has been introduced, entitled word prevalence, which estimates the proportion of the population that knows a given word. This measure has been shown to account for unique variance in large sets of lexical performance. This article aims to build on the work of Brysbaert et al. and Keuleers et al. by introducing new corpus-based metrics that estimate how likely a word is to be an active member of the natural language environment, and hence known by a larger subset of the general population. This metric is derived from an analysis of a newly collected corpus of over 25,000 fiction and non-fiction books and will be shown that it is capable of accounting for significantly more variance than past corpus-based measures.

Cognitive control, the ability to align our actions with goals or context, is largely absent in children under four. How then are preschoolers able to tailor their behavior to best match the situation? Learning may provide an alternative route to context-sensitive responding. This study investigated this hypothesis in the Dimensional Change Card Sort (DCCS), a classic test of cognitive control that most under-fours fail. A training intervention based on learning theoretic principles proved highly effective: Three-year-olds who learned about DCCS rules and game contexts in a card-labeling task, subsequently transferred this knowledge to sorting in the DCCS, passing at more than 3 times the rate of controls (N = 47). This surprising finding reveals much about the nature of the developing mind.

Although number words are common in everyday speech, learning their meanings is an arduous, drawn-out process for most children, and the source of this delay has long been the subject of inquiry. Children begin by identifying the few small numerosities that can be named without counting, and this has prompted further debate over whether there is a specific, capacity-limited system for representing these small sets, or whether smaller and larger sets are both represented by the same system. Here we present a formal, computational analysis of number learning that offers a possible solution to both puzzles. This analysis indicates that once the environment and the representational demands of the task of learning to identify sets are taken into consideration, a continuous system for learning, representing and discriminating set-sizes can give rise to effective discontinuities in processing. At the same time, our simulations illustrate how typical prenominal linguistic constructions (“there are three balls”) structure information in a way that is largely unhelpful for discrimination learning, while suggesting that postnominal constructions (“balls, there are three”) will facilitate such learning. A training-experiment with three-year olds confirms these predictions, demonstrating that rapid, significant gains in numerical understanding and competence are possible given appropriately structured postnominal input. Our simulations and results reveal how discrimination learning tunes children's systems for representing small sets, and how its capacity-limits result naturally out of a mixture of the learning environment and the increasingly complex task of discriminating and representing ever-larger number sets. They also explain why children benefit so little from the training that parents and educators usually provide. Given the efficacy of our intervention, the ease with which it can be implemented, and the large body of research showing how early numerical ability predicts later educational outcomes, this simple discovery may have far-reaching consequences.