Interactions between statistical aggregation and hypothesis testing mechanisms during word learning

Interactions between statistical aggregation and hypothesis testing mechanisms during word learning Alexa R. Romberg and Chen Yu {aromberg, chenyu}@indiana.edu Department of Psychological and Brain Sciences, 1101 E. 10th Street Bloomington, IN 47405 USA Abstract Adults, children and infants are all able to infer likely word meanings based on the relative frequency with which labels and referents appear together (e.g., Smith & Yu, 2007; Yu & Smith, 2008). However, the extent to which learners rely on aggregation of co-occurrence statistics vs. test specific hypotheses to infer mappings is currently a matter of significant uncertainty (Smith & Yu, 2012), exacerbated by the different experimental methods used to test learning mechanisms. Real world word learning is likely to involve a combination of statistical aggregation and active hypothesis testing. The current experiment investigates how these two learning mechanisms interact during word learning by having participants respond to a subset of items during a cross- situational word learning task. We find that hypothesis-testing is most effective when informed by statistical information and that the process of hypothesis-testing draws attention away from the remaining set of items. Keywords: word learning; statistical learning; language acquisition; cross-situational learning; learning mechanisms Introduction In the past several years evidence has accumulated that infants, children and adults are able to aggregate information across multiple ambiguous contexts to disambiguate a word- referent mapping (e.g., Scott & Fisher, 2011; Smith & Yu, 2008; Suanda & Namy, 2012; Trueswell, Medina, Hafri & Gleitman, 2013; Yu & Smith, 2007). The learning mechanisms that learners use to do this cross-situational word learning are currently under debate, contrasting hypothesis testing (HT) with associative learning (AL; Koehne, Trueswell & Gleitman, 2013; Smith & Yu, 2012; Trueswell et al., 2013). Within associative theories, statistical computations emerge from the strengthening and weakening of associations as a function of co-occurrence reliability and competition among associations. Within hypothesis testing theories, conceptually coherent hypotheses are confirmed or disconfirmed through a variety of procedures. While both of these frameworks are consistent with learners inferring mappings between words and referents that consistently co- occur, they offer fundamentally different characterizations of what it means to be a statistical learner. Accordingly, two quite different experimental paradigms have been used to investigate cross-situational word learning. In the paradigm supporting hypothesis testing, each trial has a One:Many structure, providing participants with a single auditory label and several (often 5) objects to view (Koehne et al., 2013; Trueswell et al., 2013). Versions in which participants have responded with a selection of the word’s referent on each trial have produced similar results as versions in which they simply observe the trials (e.g., Trueswell et al., 2013). The findings from the HT paradigm demonstrate clearly the power of correctly hypothesizing a word’s meaning: Participants who select the correct referent for a label on a given trial are more likely to select the correct referent for that label on future trials than participants who made an incorrect selection (Koehne et al., 2013; Trueswell et al., 2013; see also Medina, Snedeker, Trueswell & Gleitman, 2011). Thus, researchers employing the HT paradigm have concluded that the participants learn by forming a single hypothesis for the likely label-object mapping and do not store multiple word-object occurrences at the same time. However, overall learning effects are relatively modest. Participants responded correctly on approximately 33% of the 12 trials in the final block of Experiment 1 (their 5 th exposure to the label) in Trueswell et al., (2013), correctly selecting 1.6 more referents than expected by chance. In the “AL” paradigm, each trial has a Many:Many structure. Participants are provided with multiple labels and objects, often 4 of each when adults are tested (e.g., Romberg & Yu, 2013; Suanda & Namy, 2012; Yu & Smith, 2007). The paradigm consists of a training phase, during which participants do not make any responses, and a test phase during which participants select the most likely referent from an array of objects presented in the study. Overall learning effects are higher in this paradigm compared with the HT paradigm. For example, adult participants responded correctly on 35% of the 18 test trials in Romberg & Yu (2013), correctly selecting 5.3 more referents than expected by chance (after 6 exposures to each label). The authors employing the AL paradigm have argued that participants learn by aggregating co-occurrence statistics across trials and encode multiple associations between labels and objects presented on individual trials. The two paradigms set up very different processing tasks for the participants, as illustrated in Figure 1. The HT paradigm is characterized by repeated oscillation between processing and retrieval. The task structure either explicitly or implicitly encourages participants to engage in some computation (e.g., updating representations of statistical structure) and retrieval (i.e., bringing a hypothesis to explicit awareness) on every trial. The fact that only 1 label is provided means that participants have limited ability to “control” information flow because they have no choice about which label to select for attention and can only associate objects with 1 label on each trial. The AL paradigm, in contrast, is characterized by a continuous sequence of