Sentence-Based Attentional Mechanisms in Word Learning: Evidence from a Computational Model

Afra Alishahi,Afsaneh Fazly,Matthew W Crocker,Judith Koehne

doi:10.3389/fpsyg.2012.00200

Abstract

When looking for the referents of novel nouns, adults and young children are sensitive to cross-situational statistics (Yu and Smith, 2007; Smith and Yu, 2008). In addition, the linguistic context that a word appears in has been shown to act as a powerful attention mechanism for guiding sentence processing and word learning (Landau and Gleitman, 1985; Altmann and Kamide, 1999; Kako and Trueswell, 2000). Koehne and Crocker (2010, 2011) investigate the interaction between cross-situational evidence and guidance from the sentential context in an adult language learning scenario. Their studies reveal that these learning mechanisms interact in a complex manner: they can be used in a complementary way when context helps reduce referential uncertainty; they influence word learning about equally strongly when cross-situational and contextual evidence are in conflict; and contextual cues block aspects of cross-situational learning when both mechanisms are independently applicable. To address this complex pattern of findings, we present a probabilistic computational model of word learning which extends a previous cross-situational model (Fazly et al., 2010) with an attention mechanism based on sentential cues. Our model uses a framework that seamlessly combines the two sources of evidence in order to study their emerging pattern of interaction during the process of word learning. Simulations of the experiments of (Koehne and Crocker, 2010, 2011) reveal an overall pattern of results that are in line with their findings. Importantly, we demonstrate that our model does not need to explicitly assign priority to either source of evidence in order to produce these results: learning patterns emerge as a result of a probabilistic interaction between the two clue types. Moreover, using a computational model allows us to examine the developmental trajectory of the differential roles of cross-situational and sentential cues in word learning.

Highlights

Learning a language involves mapping words to their corresponding meanings in the outside world
It has been suggested that children draw on syntactic cues that the linguistic context provides in order to guide word learning, a hypothesis known as syntactic bootstrapping (Gleitman, 1990; Gillette et al, 1999)
EXPERIMENTAL RESULTS We present results and analyses of our simulations of two experiments, where we examine the role of informative linguistic context and its interplay with cross-situational evidence in a controlled word learning setup: (i) K&C 2010-Experiment 2, where the two sources of cross-situational and sentence-level evidence provide complementary information (Section 1); and (ii) K&C 2011-Experiment 2, in which the two sources provide redundant information (Section 2)

Summary

Introduction

Learning a language involves mapping words to their corresponding meanings in the outside world. The performance of the participants in selecting the correct referent of a novel noun is taken to reflect how these two sources of evidence interact in human word learning Their results reveal that adults can successfully learn from both cross-situational and sentence-level constraints in parallel. Most existing computational models of word learning focus on the informativeness of cross-situational evidence in learning word–meaning mappings (Siskind, 1996; Li et al, 2004; Regier, 2005; Yu, 2005; Frank et al, 2007; Fazly et al, 2010) Extensions of these models integrate certain types of social cues such as gaze and gesture (Frank et al, 2007; Yu and Ballard, 2008), or shallow syntactic cues such as lexical categories of words (Yu, 2006; Alishahi and Fazly, 2010). A few models explicitly study the role of sentential context in word learning (Niyogi, 2002; Maurits et al, 2009), extremely limiting the possibilities for the syntactic context of the words to be learned

Methods

Results

Conclusion