A Bootstrapping Model of Frequency and Context Effects in Word Learning

Abstract

Prior research has shown that people can learn many nouns (i.e., word-object mappings) from a short series of ambiguous situations containing multiple words and objects. For successful cross-situational learning, people must approximately track which words and referents co-occur most frequently. This study investigates the effects of allowing some word-referent pairs to appear more frequently than others, as is true in real-world learning environments. Surprisingly, high-frequency pairs are not always learned better, but can also boost learning of other pairs. Using a recent associative model (Kachergis, Yu, & Shiffrin, 2012), we explain how mixing pairs of different frequencies can bootstrap late learning of the low-frequency pairs based on early learning of higher frequency pairs. We also manipulate contextual diversity, the number of pairs a given pair appears with across training, since it is naturalistically confounded with frequency. The associative model has competing familiarity and uncertainty biases, and their interaction is able to capture the individual and combined effects of frequency and contextual diversity on human learning. Two other recent word-learning models do not account for the behavioral findings.

Real-world visual statistics and infants' first-learned object names

“…Considerable evidence shows that adults and a whole variety of computational models are very good at cross-situational word-referent learning even in contexts of high uncertainty (e.g. [7,9,[19][20][21][22]). Several studies have also shown that 1-yearold infants can aggregate word-referent co-occurrence data in simple laboratory experiments with minimal visual clutter and short temporal lags between repeated naming events (e.g.…”

Cross‐Situational Learning of Phonologically Overlapping Words Across Degrees of Ambiguity

“…To understand how learners determine word-referent mappings, researchers have sought to identify the information used to resolve referential ambiguity, revealing that learners are able to track co-occurring words and referents across moments in time. This behavior is often termed cross-situational word learning (XSWL) or statistical word learning (Benitez, Yurovsky, & Smith, 2016;Blythe, Smith, & Smith, 2016;Fitneva & Christiansen, 2011;Kachergis, Yu, & Shiffrin, 2016;Vlach & DeBrock, 2017;Vlach & Sandhofer, 2014;Yurovsky & Frank, 2015). In a typical XSWL task, participants are presented with a series of ambiguous learning trials that consist of several words and several objects within a single trial.…”

Grounding statistical learning in context: The effects of learning and retrieval contexts on cross-situational word learning

“…One possible pathway that contextual information may influence learning is through memory processes (e.g., Kachergis, Yu, & Shiffrin, in press). Some studies show that it is easier to recall lists of words from the same semantic category than lists of unrelated words (e.g., Poirier & Saint-Aubin, 1995).…”