Actively Learning Nouns Across Ambiguous Situations

Actively Learning Nouns Across Ambiguous Situations George Kachergis, Chen Yu, and Richard M. Shiffrin {gkacherg, chenyu, shiffrin}@indiana.edu Department of Psychological & Brain Science / Cognitive Science Program Bloomington, IN 47405 USA Abstract When tested on each word and given four trained objects to choose from, participants chose the correct object for half of the 18 words, on average (Yu & Smith, 2007). However, in the real world even infant learners are not passive observers, merely watching the world go by. As learners shift their attention, their eyes, head and hands move, changing the objects in their view. If caregivers notice these attention shifts, they may be more likely to name objects that are currently being attended to. Thus, learners may in essence be able to increase the likelihood an object is named by shifting their attention to include this object. This is a form of active learning, a concept studied extensively in machine learning (cf. Settles, 2009), in which a learner can query an information source for the labels of particular data points. In this study, we introduce active cross-situational word learning, in which learners choose which four objects they would like to see named on each successive trial. Thus, learners control when to repeat pairs, when to stop experiencing pairs they feel they know, and when to attempt to learn more pairs. This gives us a glimpse of their preferred strategies. For example, participants may choose to repeat a single pair from the previous trial, and leverage working memory to quickly learn that the repeated word refers to the repeated object, while ignoring the other three word-object pairs on the trial. Equivalently, a learner may prefer to repeat three pairs from the previous trial, and quickly learn the novel pairing that was not present. Kachergis, Yu, & Shiffrin (2009a) manipulated this sort of temporal contiguity in a passive cross-situational study and found not only that repeated pairs are learned more easily, but so are unrepeated pairs in conditions with some repeats. This suggests that simple inference supported by working memory is not the only learning mechanism at work. In fact, investigating active learning can reveal what information and mechanisms a learner has at their disposal, and characterizing the observed strategies—and their performance—will motivate learning models. For example, our recent associative model of cross-situational learning assumes that learners have access to both their familiarity and their uncertainty about the word-object pairings present on a given trial, and that attention competes for uncertain stimuli and for already-strong pairings (Kachergis, Yu, & Shiffrin, 2012). This model matches adult behavior in passive cross-situational experiments investigating mutual exclusivity, a bias to find 1-to-1 word-object mappings that is present even in 2.5-year-olds (Markman & Wachtel, 1988). If active learners have access to their knowledge of pairing strength and stimulus uncertainty, these cues can be combined to produce a few active learning strategies. One Previous research shows that people can use the co- occurrence of words and objects in ambiguous situations (i.e., containing multiple words and objects) to learn word meanings during a brief passive training period (Yu & Smith, 2007). However, learners in the world are not completely passive, but can affect how their environment is structured by moving their heads, eyes, and even objects. These actions can indicate attention to a language teacher, who may then be more likely to name the attended objects. Using a novel active learning paradigm in which learners choose which four objects they would like to see named on each successive trial, this study asks whether active learning is superior to passive learning in a cross-situational word learning context. Finding that learners perform better in active learning, we investigate the strategies that were most successful, discuss the implications, and model the results. Keywords: active learning; statistical learning; cross- situational learning; temporal contiguity; language acquisition Introduction Human infants learn words quite quickly despite many challenges facing them, including uncertainty and ambiguity in the language environment. Recent research has studied how learners may acquire word meanings from regularities in the co-occurrence of words and referents (e.g., objects). Such cross-situational statistical word learning relies on two assumptions: 1) that spoken words are often relevant to the visible environment, and 2) that learners can to some extent remember the co-occurrence of multiple words and objects in a scene. Thus, as words and their intended referents are observed in different situations over time, learners can apprehend the correct word-object mappings. Relying only on the regularity of the linguistic environment and basic memory and attention processes, this may be an important method of learning nouns for infants, and even adult travelers. In adult cross-situational learning studies (e.g., Yu & Smith 2007), participants are asked to learn the meaning of alien words by watching a series of training trials. On each trial learners see an array of unfamiliar objects (e.g., four sculptures) and hear pseudowords (e.g., stigson, bosa). The meaning of each pseudoword is ambiguous on a given trial, because although each word refers to a single onscreen object, the intended referent is not indicated. In a typical learning scenario, participants attempt to learn 18 word- object pairings from 27 trials, with four words and four objects given per trial. In this design, each word-referent pair is presented six times over the five-minute training period. Learning a correct word-object pairing requires some form of accumulation of word-object co-occurrences.