Learning in natural environments is often characterized by a degree of inconsistency from an input. These inconsistencies occur, for example, when learning from more than one source, or when the presence of environmental noise distorts incoming information; as a result, the task faced by the learner becomes ambiguous. In this study, we investigate how learners handle such situations. We focus on the setting where a learner receives and processes a sequence of utterances to master associations between objects and their labels, (...) where the source is inconsistent by design: It uses both “correct” and “incorrect” object‐label pairings. We hypothesize that depending on the order of presentation, the result of the learning may be different. To this end, we consider two types of symbolic learning procedures: the Object‐Label (OL) and the Label‐Object (LO) process. In the OL process, the learner is first exposed to the object, and then the label. In the LO process, this order is reversed. We perform experiments with human subjects, and also construct a computational model that is based on a nonlinear stochastic reinforcement learning algorithm. It is observed experimentally that OL learners are generally better at processing inconsistent input compared to LO learners. We show that the patterns observed in the learning experiments can be reproduced in the simulations if the model includes (a) an ability to regularize the input (and also to do the opposite, i.e., undermatch) and (b) an ability to take account of implicit negative evidence (i.e., interactions among different objects/labels). The model suggests that while both types of learners utilize implicit negative evidence in a similar way, there is a difference in regularization patterns: OL learners regularize the input, whereas LO learners undermatch. As a result, OL learners are able to form a more consistent system of image‐utterance associations, despite the ambiguous learning task. (shrink)

Individuals shift their language to converge with interlocutors. Recent work has suggested that convergence can target not only observed but also expected linguistic behavior, cued by social information. However, it remains uncertain how expectations and observed behavior interact, particularly when they contradict each other. We investigated this using a cooperative map task experiment, in which pairs of participants communicated online by typing messages to each other in a miniature “alien” language that exhibited variation between alien species. The overall task comprised (...) three phases, in each of which participants were told that they would be paired with a different partner. One member of the pair was given explicit linguistic expectations in each phase, while the software controlled whether or not observed behavior from their partner would be consistent or inconsistent with these expectations. The other participant was given no such expectations, allowing us to control for the role of expectation. Participants converged to both observed and expected linguistic behavior, and convergence was boosted when observation and expectation were aligned. When expected and observed behavior were misaligned, participants updated their expectations, though convergence levels did not drop. Furthermore, participants generalized what they learned about one partner to apparent novel partners of the same alien species. We also discuss individual variation in convergence patterns and the lack of a relationship between linguistic convergence and success at the map task. Findings are consistent with observations outside the laboratory that language users converge toward expected linguistic behavior. They also have broader implications for understanding linguistic accommodation and the influence of social information on linguistic processing and production. (shrink)

How do speakers learn the social meaning of different linguistic variants, and what factors influence how likely a particular social–linguistic association is to be learned? It has been argued that the social meaning of more salient variants should be learned faster, and that learners' pre‐existing experience of a variant will influence its salience. In this paper, we report two artificial‐language‐learning experiments investigating this. Each experiment involved two language‐learning stages followed by a test. The first stage introduced the artificial language and (...) trained participants in it, while the second stage added a simple social context using images of cartoon aliens. The first learning stage was intended to establish participants' experience with the artificial language in general and with the distribution of linguistic variants in particular. The second stage, in which linguistic stimuli were accompanied by images of particular aliens, was intended to simulate the acquisition of linguistic variants in a social context. In our first experiment, we manipulated whether a particular linguistic variant, associated with one species of alien in the second learning phase, had been encountered in the first learning phase. In the second experiment, we manipulated whether the variant had been encountered in the same grammatical context. In both cases we predicted that the unexpectedness of a new variant or a new grammatical context for an old variant would increase the variant's salience and facilitate the learning of its social meaning. This is what we found, although in the second experiment, the effect was driven by better learners. Our results suggest that unexpectedness increases the salience of variants and makes their social distribution easier to learn, deepening our understanding of the role of individual language experience in the acquisition of sociolinguistic meaning. (shrink)