Although statistical learning and language have been assumed to be intertwined, this theoretical presupposition has rarely been tested empirically. The present study investigates the relationship between statistical learning and language using a within-subject design embedded in an individual-differences framework. Participants were administered separate statistical learning tasks involving adjacent and nonadjacent dependencies, along with a language comprehension task and a battery of other measures assessing verbal working memory, short-term memory, vocabulary, reading experience, cognitive motivation, and fluid intelligence. Strong interrelationships were found among statistical learning, verbal working memory, and language comprehension. However, when the effects of all other factors were controlled for, performance on the two statistical learning tasks was the only predictor for comprehending relevant types of natural language sentences.

Considerable individual differences in language ability exist among normally developing children and adults. Whereas past research have attributed such differences to variations in verbal working memory or experience with language, we test the hypothesis that individual differences in statistical learning may be associated with differential language performance. We employ a novel paradigm for studying statistical learning on-line, combining a serial-reaction time task with artificial grammar learning. This task offers insights into both the timecourse of and individual differences in statistical learning. Experiment 1 charts the micro-level trajectory for statistical learning of nonadjacent dependencies and provides an on-line index of individual differences therein. In Experiment 2, these differences are then shown to predict variations in participants’ on-line processing of long-distance dependencies involving center-embedded relative clauses. The findings suggest that individual differences in the ability to learn from experience through statistical learning may contribute to variations in linguistic performance.

Prediction-based processes appear to play an important role in language. Few studies, however, have sought to test the relationship within individuals between prediction learning and natural language processing. This paper builds upon existing statistical learning work using a novel paradigm for studying the on-line learning of predictive dependencies. Within this paradigm, a new ''prediction task'' is introduced that provides a sensitive index of individual differences for developing probabilistic sequential expectations. Across three interrelated experiments, the prediction task and results thereof are used to bridge knowledge of the empirical relation between statistical learning and language within the context of nonadjacency processing. We first chart the trajectory for learning nonadjacencies, documenting individual differences in prediction learning. Subsequent simple recurrent network simulations then closely capture human performance patterns in the new paradigm. Finally, individual differences in prediction performances are shown to strongly correlate with participants' sentence processing of complex, long-distance dependencies in natural language.

Many social interactions require humans to coordinate their behavior across a range of scales. However, aspects of intentional coordination remain puzzling from within several approaches in cognitive science. Sketching a new perspective, we propose that the complex behavioral patterns - or 'unwritten rules' - governing such coordination emerge from an ongoing process of 'virtual bargaining'. Social participants behave on the basis of what they would agree to do if they were explicitly to bargain, provided the agreement that would arise from such discussion is commonly known. Although intuitively simple, this interpretation has implications for understanding a broad spectrum of social, economic, and cultural phenomena (including joint action, team reasoning, communication, and language) that, we argue, depend fundamentally on the virtual bargains themselves.

In 2 separate self-paced reading experiments, Farmer, Christiansen, and Monaghan (2006) found that the degree to which a word's phonology is typical of other words in its lexical category influences online processing of nouns and verbs in predictive contexts. Staub, Grant, Clifton, and Rayner (2009) failed to find an effect of phonological typicality when they combined stimuli from the separate experiments into a single experiment. We replicated Staub et al.'s experiment and found that the combination of stimulus sets affects the predictiveness of the syntactic context; this reduces the phonological typicality effect as the experiment proceeds, although the phonological typicality effect was still evident early in the experiment. Although an ambiguous context may diminish sensitivity to the probabilistic relationship between the sound of a word and its lexical category, phonological typicality does influence online sentence processing during normal reading when the syntactic context is predictive of the lexical category of upcoming words.