- Top of page
- 1. Introduction
- 2. Methods
- 3. Results
- 4. Discussion
- 5. Conclusions
Recent research has demonstrated that word learners can determine word-referent mappings by tracking co-occurrences across multiple ambiguous naming events. The current study addresses the mechanisms underlying this capacity to learn words cross-situationally. This replication and extension of Yu and Smith (2007) investigates the factors influencing both successful cross-situational word learning and mis-mappings. Item analysis and error patterns revealed that the co-occurrence structure of the learning environment as well as the context of the testing environment jointly affected learning across observations. Learners also adopted an exclusion strategy, which contributed conjointly with statistical tracking to performance. Implications for our understanding of the processes underlying cross-situational word learning are discussed.
- Top of page
- 1. Introduction
- 2. Methods
- 3. Results
- 4. Discussion
- 5. Conclusions
The notion that children learn words at least in part by tracking the common contexts in which a given word is uttered is neither a novel nor an unintuitive idea. Although many language acquisition scholars (e.g., Carey, 1978; Gleitman, 1990; Pinker, 1989) have argued that cross-situational learning plays a pivotal role in children’s lexical acquisition, empirical demonstrations of this learning capacity are relatively scarce. Indeed, research on how children learn the meanings of words has focused predominantly on the mechanisms underlying children’s word learning within a single observation or naming event (so called fast mapping; for reviews see Bloom, 2000; Golinkoff et al., 2000).
Recently, however, a number of studies have revealed that infants (Smith & Yu, 2008), toddlers (Akhtar & Montague, 1999), and adults (Gillette, Gleitman, Gleitman, & Lederer, 1999; Yu & Smith, 2007) are capable of learning words across multiple observations. For example, in one recent study, Yu and Smith (2007) presented adult learners with a series of naming events in which they heard multiple words and saw multiple pictures. Although each word always occurred with its referent across naming events, other words and other pictures were also present during any given event, leading to referential ambiguity within each event. Importantly, words and pictures were repeated in various permutations across naming events, leading to more reliable co-occurrences between words and their referents than between words and distractor objects. Despite only viewing a handful of repetitions of each word-picture pairing, adult learners correctly identified which words co-occurred most reliably with which pictures at above chance rates (Yu & Smith, 2007).
Yu and Smith’s findings have more recently been extended to infant word learners (Smith & Yu, 2008; Yu & Smith, 2011; see also Vouloumanos & Werker, 2009), highlighting the fact that this learning capacity is readily available early in development and thus raising the possibility that it plays a role in children’s early word learning. Despite this recent evidence of cross-situational word learning across development, the processes and factors that make this form of learning possible are relatively unknown. The broad goal of the current study is to shed light on the mechanisms underlying cross-situational word learning.
The logic behind successful learning in Yu and Smith’s task, and cross-situational word learning more generally, is that within-trial or within-situation referential ambiguity requires learners to track multiple possible referents for each observed use of a word. Learners can then compare the set of possible referents across observations to arrive at the most probable referent for each word (Siskind, 1996; Yu & Smith, 2007). Recently, K. Smith and colleagues have proposed that a process other than computation of cross-situational statistics may also account for Yu and Smith’s findings (Smith, Smith, & Blythe, 2009). They argue that a learner who simply keeps track of the set of objects and words present during a single learning trial could successfully perform above chance in Yu and Smith’s task. This is possible because the testing regimen implemented by Yu and Smith (a four-alternative forced-choice task) constrains learners’ referent selections to four items (the target picture and three foil pictures). Importantly, the probability with which the three foil items also co-occurred with the target word during the single encoded learning trial was relatively low. Thus, during test, the learner could perform at above-chance rates by simply selecting at random from the available pictures that had also been presented during the single encoded learning trial for that particular word.
Smith et al.’s (2009) alternative account highlights the notion that a variety of mechanisms could potentially explain successful cross-situational word learning. In the current experiment, we investigate these mechanisms by replicating Yu and Smith’s original finding and examining the factors influencing successful learning as well as mis-mappings. We propose to accomplish this in the following ways. First, we examine the role of the statistical structure of the learning environment (as stressed by Yu and Smith) on the learning process. As described above, within the learning phase of Yu and Smith’s cross-situational word learning paradigm, learners view a series of ambiguous situations involving multiple words and multiple pictures. Thus, a cross-situational statistical learner creates associations not only between words and their correct referents but also between words and distractor pictures that are the referents of other words also presented during the trial (spurious correlations). As learning trials are constructed by randomly selecting four word-referent pairs for each trial, there is variability in the strength and number of possible spurious correlations formed for each word across trials. That is, during learning, some words may appear many times with few distractors, creating a small number of strong spurious correlations. In contrast, other words may appear few times with many distractors, creating many weak spurious correlations. In the current experiment, we ask to what extent these variations (i.e., differences in contextual diversity, Kachergis, Yu, & Shiffrin, 2009) affect learning.
Three recent findings suggest that the structure of the learning environment does have an influence on statistical word learning. First, in Yu and Smith’s original study, participants in a condition with a larger to-be-learned lexicon acquired more words than participants in a condition with a smaller lexicon to learn. Yu and Smith argued that this pattern reflects the fact that the smaller lexicon resulted in fewer competitors and thus stronger spurious correlations. Second, Kachergis et al. (2009) demonstrated that systematically manipulating the diversity in the learning contexts in which words appear impacts cross-situational statistical learning; within the same to-be-learned lexicon, words with many weak spurious correlations elicited higher learning rates during test than those with fewer, stronger spurious correlations. A third reason to suspect that spurious correlations may affect learning is that word learners appear to readily track multiple word-to-referent mappings (Vouloumanos, 2008; Vouloumanos & Werker, 2009). That is, adult word learners (and to some extent infant word learners, see Vouloumanos & Werker, 2009) appeared sensitive not only to high-frequency pairings but also to low-frequency pairings. Based on these findings, we predict that variability in the co-occurrence structure of the learning environment should affect which words are learned best.
A second goal of the current experiment is to examine whether the testing environment’s context influences performance independent of the structure of the learning environment. Test trials in this design involved presenting a target word with four possible referents (the target picture and three foils). As foils were randomly selected, some words were tested with foils that co-occurred often with the target word during learning while other words were tested with foils that co-occurred rarely or not at all with the target word during learning. We predicted that performance should vary as a function of the probability with which foils served as distractors during learning. Thus, in addition to exerting influence within the learning process, co-occurrence statistics should also have an effect during test. Of particular interest is the extent to which these effects are independent of one another, as this may disambiguate between the single-exposure learner and statistical learner accounts of cross-situational word learning. We suggest that although both cross-situational learning and single-trial accounts would predict an effect of testing environments on performance, only the cross-situational learning account would predict an independent effect of the learning environment’s statistical structure on performance.
A third goal of the current experiment is to examine influences of additional mechanisms beyond tracking of co-occurrence statistics on cross-situational word learning. One candidate mechanism that has previously been proposed to play a prominent role in cross-situational word learning (e.g., Siskind, 1996; Yu, 2008) is mutual exclusivity (Markman & Wachtel, 1988). Briefly, the mutual exclusivity constraint refers to a word learner’s default tendency to accept only one word for each object (Markman, 1992). This assumption may aid cross-situational word learning in several ways. Across the learning phase, mutual exclusivity simplifies the learning process by limiting the hypothesis space and guiding the learner away from entertaining many-to-one or one-to-many word-referent mappings. Mutual exclusivity may also contribute to performance as learners can use known word-referent mappings to rule out possible referents for unknown words, either during learning (Ichinco, Frank, & Saxe, 2009; Yurovsky & Yu, 2008) or during test (e.g., Diesendruck & Markson, 2001; Markman & Wachtel, 1988). In this study, we investigate participants’ use of this strategy in the cross-situational learning paradigm by examining the extent to which knowledge of (i.e., having successfully mapped labels for) the foils at test constrains referent selection for the target word.
A final goal of the current study is to provide some insight into the automatic and non-strategic nature of participants’ learning via cross-situational observations. Yu and Smith (2007) reported anecdotal evidence that participants in their task verbally reported learning very few words (see also Ichinco et al., 2009). This anecdotal evidence is reminiscent of previous investigations demonstrating that the learning of linguistic and non-linguistic statistical structures proceeds incidentally (e.g., Saffran, Newport, Aslin, Tunick, & Barrueco, 1997) and automatically (e.g., Turke-Browne, Junge, & Scholl, 2005). As a first step in assessing the automaticity of cross-situational word learning, we compared participants’ performance to their own explicit judgments of their performance during a post-experiment interview.