In language learning, learners engage with their environment, incorporating cues from different sources. However, in lab‐based experiments, using artificial languages, many of the cues and features that are part of real‐world language learning are stripped away. In three experiments, we investigated the role of positive, negative, and mixed feedback on the gradual learning of language‐like statistical regularities within an active guessing game paradigm. In Experiment 1, participants received deterministic feedback (100%), whereas probabilistic feedback (i.e., 75% or 50%) was introduced in (...) Experiment 2. Finally, Experiment 3 explored the impact of mixed probabilistic feedback (33% positive, 33% negative, 33% no feedback). The results showed that cross‐situational learning of words was observed without feedback, but participants were able to learn structural regularities of the miniature language only when feedback was provided. Interestingly, the presence of positive feedback was particularly helpful for the learner, promoting more in‐depth learning of the artificial language. (shrink)

In their editors’ introduction, Rebuschat and Monaghan provide the background to the special issue. They outline the rationale for bringing together, in a single volume, leading researchers from two distinct, yet related research strands, implicit learning and statistical learning. The editors then introduce the new contributions solicited for this special issue and provide their perspective on the agenda setting that results from combining these two approaches.

Recent laboratory experiments have shown that both infant and adult learners can acquire word‐referent mappings using cross‐situational statistics. The vast majority of the work on this topic has used unfamiliar objects presented on neutral backgrounds as the visual contexts for word learning. However, these laboratory contexts are much different than the real‐world contexts in which learning occurs. Thus, the feasibility of generalizing cross‐situational learning beyond the laboratory is in question. Adapting the Human Simulation Paradigm, we conducted a series of experiments (...) examining cross‐situational learning from children's egocentric videos captured during naturalistic play. Focusing on individually ambiguous naming moments that naturally occur during toy play, we asked how statistical learning unfolds in real time through accumulating cross‐situational statistics in naturalistic contexts. We found that even when learning situations were individually ambiguous, learners’ performance gradually improved over time. This improvement was driven in part by learners’ use of partial knowledge acquired from previous learning situations, even when they had not yet discovered correct word‐object mappings. These results suggest that word learning is a continuous process by means of real‐time information integration. (shrink)

Objects in the world usually have names at different hierarchical levels (e.g., beagle, dog, animal). This research investigates adults' ability to use cross‐situational statistics to simultaneously learn object labels at individual and category levels. The results revealed that adults were able to use co‐occurrence information to learn hierarchical labels in contexts where the labels for individual objects and labels for categories were presented in completely separated blocks, in interleaved blocks, or mixed in the same trial. Temporal presentation schedules significantly affected (...) the learning of individual object labels, but not the learning of category labels. Learners' subsequent generalization of category labels indicated sensitivity to the structure of statistical input. (shrink)