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,

How are people able to think about things they have never seen or touched? We demonstrate that abstract knowledge can be built analogically from more experience-based knowledge. People's understanding of the abstract domain of time, for example, is so intimately dependent on the more experience-based domain of space that when people make an air journey or wait in a lunch line, they also unwittingly (and dramatically) change their thinking about time. Further, our results suggest that it is not sensorimotor spatial experience per se that influences people's thinking about time, but rather people's representations of and thinking about their spatial experience.

As adults age, their performance on many psychometric tests changes systematically, a finding that is widely taken to reveal that cognitive information-processing capacities decline across adulthood. Contrary to this, we suggest that older adults' changing performance reflects memory search demands, which escalate as experience grows. A series of simulations show how the performance patterns observed across adulthood emerge naturally in learning models as they acquire knowledge. The simulations correctly identify greater variation in the cognitive performance of older adults, and successfully predict that older adults will show greater sensitivity to fine-grained differences in the properties of test stimuli than younger adults. Our results indicate that older adults' performance on cognitive tests reflects the predictable consequences of learning on informationprocessing, and not cognitive decline. We consider the implications of this for our scientific and cultural understanding of aging.

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.

Why do adult language learners typically fail to acquire second languages with native proficiency? Does prior linguistic experience influence the size of the "units" adults attend to in learning, and if so, how does this influence what gets learned? Here, we examine these questions in relation to grammatical gender, which adult learners almost invariably struggle to master. We present a model of learning that predicts that exposure to smaller units (such as nouns) before exposure to larger linguistic units (such as sentences) can critically impair learning about predictive relations between units: such as that between a noun and its article. This prediction is then confirmed by a study of adult participants learning grammatical gender in an artificial language. Adults learned both nouns and their articles better when they were first heard nouns used in context with their articles prior to hearing the nouns individually, compared with learners who first heard the nouns in isolation, prior to hearing them used in context. In the light of these results, we discuss the role gender appears to play in language, the importance of meaning in artificial grammar learning, and the implications of this work for the structure of L2-training.