Learning Nouns with Domain-General Associative Learning Mechanisms

Learning Nouns with Domain-General Associative Learning Mechanisms George Kachergis gkacherg@indiana.edu Department of Psychological & Brain Science / Cognitive Science Program Bloomington, IN 47405 USA Abstract with a subset of these principles that relate to how people map new words to objects. Mutual exclusivity (ME) is the assumption that every object has only one name (Markman & Wachtel, 1988). A fill-the-lexical gap bias, which causes children to want to find a name for an object with no known name, has also been proposed (Clark, 1983) and argued (Merriman and Bowman, 1989). When given a set of familiar and unfamiliar objects, it has been shown that 28-month-olds assume that a new label maps to an unfamiliar object (e.g., Mervis & Bertrand, 1994). Similarly, the principle of contrast states that an infant given a new word will seek to attach it to an unlabeled object (Clark, 1983). Fill-the-gap, ME, and contrast make many of the same predictions made by the more general novel name-nameless category principle (N3C), which states that novel labels map to novel objects (Golinkoff, Mervis, & Hirsh-Pasek, 1994). It is not our goal to explore the overlapping and nuanced ways that these various principles interact. Indeed, we hope to avoid this confusing plurality of explanations by showing that many of the behaviors ascribed to these theories can be explained by domain-general learning mechanisms uncovered by studies of associative learning. Nor are we the first to suggest that human language acquisition—as fast and yet difficult as it is—can be explained with domain- general learning mechanisms: Smith (2000) argued as much, and much recent work in statistical learning (described below) is motivated by this premise. Recent work has even found that children show a 1-to-1 bias in domains other than language: voices to faces (Moher, Feigenson, & Halberda, 2010) and actions to objects (Childers & Tomasello, 2003). However, few direct analogies have been drawn between the models and paradigms of word learning and associative learning, but see Ramscar et al. (2010). After introducing some associative learning paradigms and linking them to word learning, we discuss how universal attentional biases may account for many behaviors observed across domains. Finally, we report a new empirical word learning study using an associative learning highlighting design, and explain the results with a word-learning model that has competing attentional biases for familiarity and uncertainty. Associative learning has been meticulously studied in many species, and diverse effects have been explained using a handful of basic assumptions and mechanisms. Human language acquisition proceeds remarkably quickly and is of great interest, but is arguably more difficult to capture under the microscope. Nonetheless, empirical investigations have led researchers to theorize a variety of language learning principles and constraints. While there may indeed be language-specific learning mechanisms that are distinct from more universal associative learning mechanisms, we seek to explain some basic principles of language acquisition using domain-general mechanisms. Using an experiment and a model, we show how the principles of mutual exclusivity—an assumption of 1-to-1 word-object mappings, contrast, and other constraints related to fast mapping may stem from attention mechanisms attributed to associative learning effects such as blocking and highlighting, but directed by competing biases for familiar and unfamiliar pairs instead of surprise. Keywords: statistical learning; language acquisition; cross- situational learning; associative learning; attention Introduction All organisms learn, but only humans master human languages. Since many neural structures and basic learning mechanisms are conserved across species, it bears asking how much of human language learning can be explained with domain-general mechanisms, without appealing to innate (i.e., evolved) linguistic knowledge, exemplified by the work of Noam Chomsky, or domain-specific principles and constraints, whether innate or developed early in life (e.g., Markman, 1992). One essential part of language learning is learning word- object mappings—nouns. Two border collies have been shown to learn hundreds of nouns over years of training (Pilley & Reid, 2011; Kaminski, Call, & Fischer, 2004). Of course, this feat pales in comparison to human language learning: infants begin producing words at 1 year, and by the end of high school have command of 60,000 words, conservatively (Bloom, 2000). However, both dogs and infants have been shown to fast map: given a new word, they will choose a new object over an object with a known label, and retain the mapping weeks later (see Bloom, 2000). Fast mapping is a powerful ability for word learning, but is it based on domain-general or domain-specific learning mechanisms? One approach to studying language acquisition views word learning as a problem of induction with an enormous hypothesis space, and proposes a number of constraints to restrict the space (Markman, 1992). In this view, infants generate hypotheses that are consistent with this set of constraints and principles. The present paper is concerned Associative Learning Associative learning paradigms typically present one or more perceptual cues (e.g., objects, sounds), learners make a response (e.g., a button press), and feedback is given (e.g., food, a shock). When one cue q 1 is paired with outcome o on each trial, the resulting q 1 -o association is stronger than q 1 -o when two simultaneous cues {q 1 , q 2 } predict o during training; thus, q 2 is said to overshadow q 1 (Pavlov, 1927). A