Selective attention in cross-situational statistical learning: evidence from eye tracking.

Chen Yu,Yiwen Zhong,Damian Fricker

doi:10.3389/fpsyg.2012.00148

Abstract

A growing set of data show that adults are quite good at accumulating statistical evidence across individually ambiguous learning contexts with multiple novel words and multiple novel objects (Yu and Smith, 2007; Fitneva and Christiansen, 2011; Kachergis et al., 2012; Yurovsky et al., under resubmission); experimental studies also indicate that infants and young children do this kind of learning as well (Smith and Yu, 2008; Vouloumanos and Werker, 2009). The present study provides evidence for the operation of selective attention in the course of cross-situational learning with two main goals. The first was to show that selective attention is critical for the underlying mechanisms that support successful cross-situational learning. The second one was to test whether an associative mechanism with selective attention can explain momentary gaze data in cross-situational learning. Toward these goals, we collected eye movement data from participants when they engaged in a cross-situational statistical learning task. Various gaze patterns were extracted, analyzed and compared between strong learners who acquired more word-referent pairs through training, and average and weak learners who learned fewer pairs. Fine-grained behavioral patterns from gaze data reveal how learners control their attention after hearing a word, how they selectively attend to individual objects which compete for attention within a learning trial, and how statistical evidence is accumulated trial by trial, and integrated across words, across objects, and across word–object mappings. Taken together, those findings from eye movements provide new evidence on the real-time statistical learning mechanisms operating in the human cognitive system.

Highlights

Everyday word learning occurs in noisy contexts with many words and many potential referents for those words, and much ambiguity about which word goes with which referent
Recent experimental studies showed that both adults and young children possess powerful statistical computation capabilities – they can infer the referent of a word from highly ambiguous contexts involving many words and many referents by aggregating cross-situational statistical information across contexts (Fisher et al, 1994; Akhtar and Montague, 1999; Smith and Yu, 2008; Vouloumanos et al, 2010; Scott and Fisher, 2011)
If participants generate similar looking patterns toward a to-be-learned object as what they did toward a pre-trained object, this observation can be used as evidence to infer that they learned that to-be-learned object in the course of statistical learning

Summary

Introduction

Everyday word learning occurs in noisy contexts with many words and many potential referents for those words, and much ambiguity about which word goes with which referent. One way to attempt to understand this learning process is to start with the simplest mechanisms that are known to exist in the human learning repertoire and see how well these simple and known mechanisms can do. One such possible learning process is Hebbian-like associative learning, a form of learning known to be fundamental to many perceptual and cognitive capabilities (Smith, 2000). In statistical cross-situational learning, a learner could store all associations between words and referents. Given four words {a, b, c, d} and four visual objects

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Discussion

Conclusion