Cross-situational word learning, like any statistical learning problem, involves tracking the regularities in the environment. However, the information that learners pick up from these regularities is dependent on their learning mechanism. This article investigates the role of one type of mechanism in statistical word learning: competition. Competitive mechanisms would allow learners to find the signal in noisy input and would help to explain the speed with which learners succeed in statistical learning tasks. Because cross-situational word learning provides information at multiple (...) scales—both within and across trials/situations—learners could implement competition at either or both of these scales. A series of four experiments demonstrate that cross-situational learning involves competition at both levels of scale, and that these mechanisms interact to support rapid learning. The impact of both of these mechanisms is considered from the perspective of a process-level understanding of cross-situational learning. (shrink)

Topics in Cognitive Science, Volume 14, Issue 2, Page 388-399, April 2022.

For both adults and children, learning from one's mistakes (error-based learning) has been shown to be advantageous over avoiding errors altogether (errorless learning) in pedagogical settings. However, it remains unclear whether this advantage carries over to nonpedagogical settings in children, who mostly learn language in such settings. Using irregular plurals (e.g., “mice”) as a test case, we conducted a corpus analysis (N = 227) and two preregistered experiments (N = 56, N = 99), to investigate the potency of error-based learning (...) as a mechanism for language acquisition in 3- and 4-year-old children. The results of the corpus analysis showed that incidental feedback after errors, in the form of caregivers’ reformulations of children's errors, was relatively infrequent, had modest informational value, and was rarely used by children to correct their errors immediately. The following two experiments contrasted error-based learning with errorless learning, where the correct utterance was modeled for the child before a potential error was committed. The results showed that error-based learning was not always effective, and when it was, it was certainly not superior to errorless learning. Collectively, these findings question the extension of the benefits of error-based learning from pedagogical to nonpedagogical settings and define constraints under which one mechanism may be more beneficial to learning than the other. (shrink)

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)