The Interaction of Memory and Attention in Novel Word Generalization: A Computational Investigation

People exhibit a tendency to generalize a novel noun to the basic-level in a hierarchical taxonomy -- a cognitively salient category such as "dog" -- with the degree of generalization depending on the number and type of exemplars. Recently, a change in the presentation timing of exemplars has also been shown to have an effect, surprisingly reversing the prior observed pattern of basic-level generalization. We explore the precise mechanisms that could lead to such behavior by extending a computational model of word learning and word generalization to integrate cognitive processes of memory and attention. Our results show that the interaction of forgetting and attention to novelty, as well as sensitivity to both type and token frequencies of exemplars, enables the model to replicate the empirical results from different presentation timings. Our results reinforce the need to incorporate general cognitive processes within word learning models to better understand the range of observed behaviors in vocabulary acquisition

Simple Search Algorithms on Semantic Networks Learned from Language Use

Recent empirical and modeling research has focused on the semantic fluency task because it is informative about semantic memory. An interesting interplay arises between the richness of representations in semantic memory and the complexity of algorithms required to process it. It has remained an open question whether representations of words and their relations learned from language use can enable a simple search algorithm to mimic the observed behavior in the fluency task. Here we show that it is plausible to learn rich representations from naturalistic data for which a very simple search algorithm (a random walk) can replicate the human patterns. We suggest that explicitly structuring knowledge about words into a semantic network plays a crucial role in modeling human behavior in memory search and retrieval; moreover, this is the case across a range of semantic information sources

Predicting and Explaining Human Semantic Search in a Cognitive Model

Recent work has attempted to characterize the structure of semantic memory and the search algorithms which, together, best approximate human patterns of search revealed in a semantic fluency task. There are a number of models that seek to capture semantic search processes over networks, but they vary in the cognitive plausibility of their implementation. Existing work has also neglected to consider the constraints that the incremental process of language acquisition must place on the structure of semantic memory. Here we present a model that incrementally updates a semantic network, with limited computational steps, and replicates many patterns found in human semantic fluency using a simple random walk. We also perform thorough analyses showing that a combination of both structural and semantic features are correlated with human performance patterns.Comment: To appear in proceedings for CMCL 201

Arguments and Adjuncts: A Computational Explanation of Asymmetries in Attachment Preferences

An explanatory model of ambiguity resolution in human parsing must denve a multitude of preference behaviors from a concise computational framework. One behavior that has been difficult to account for concisely is the preference to interpret an ambiguous phrase as an argument of a predicate, rather than as a modifier that is less integrally related to a phrase (an adjunct). Previous accounts of the argument preference have rehed on assumptions about adjuncts requiring a more complex structure or entaiJing a delay in their mterpretation. This paper explores a more fundamental distinction between arguments and adjuncts—that the numberof potential arguments of a predicate is fixed, while the number of adjuncts for a phrase is unpredictable. This simple difference has important computational consequences withm the competitive attachment model of human parsing. The model exhibits a preference for arguments over adjuncts due to the necessary differences in competitive properties of the two types of attachment site. The competitive differences also entail that adjuncts accommodate more easily than arguments to contextual effects. The model thus provides a concise and explanatory account of these argument/adjunct asymmetries, avoiding the unnecessary structural or interpretive assumptions made within other approaches

Automatic Acquisition of Knowledge About Multiword Predicates

PACLIC 19 / Taipei, taiwan / December 1-3, 200

Modeling developmental and linguistic relativity effects in color term acquisition

We model two patterns related to the acquisition of color termsin Russian and English: children produce overextension errorsfor some colors but not others, and language-specific distinc-tions affect color discrimination in a non-linguistic task. Botheffects, as well as a reasonable convergence with adult linguis-tic behavior, are shown by a Self-Organizing Map trained onnaturalistic input. We investigate the effect of different waysof representing colors, i.e., as perceptual features or in terms ofthe cognitive biases on categorization extracted from crosslin-guistic color naming data. We also consider the influence ofcolor term frequency. Our results suggest effects of all three ofterm frequency, cognitive biases, and perceptual features

A comparison of homonym meaning frequency estimates derived from movie and television subtitles, free association, and explicit ratings

First Online: 10 September 2018Most words are ambiguous, with interpretation dependent on context. Advancing theories of ambiguity resolution is important for any general theory of language processing, and for resolving inconsistencies in observed ambiguity effects across experimental tasks. Focusing on homonyms (words such as bank with unrelated meanings EDGE OF A RIVER vs. FINANCIAL INSTITUTION), the present work advances theories and methods for estimating the relative frequency of their meanings, a factor that shapes observed ambiguity effects. We develop a new method for estimating meaning frequency based on the meaning of a homonym evoked in lines of movie and television subtitles according to human raters. We also replicate and extend a measure of meaning frequency derived from the classification of free associates. We evaluate the internal consistency of these measures, compare them to published estimates based on explicit ratings of each meaning’s frequency, and compare each set of norms in predicting performance in lexical and semantic decision mega-studies. All measures have high internal consistency and show agreement, but each is also associated with unique variance, which may be explained by integrating cognitive theories of memory with the demands of different experimental methodologies. To derive frequency estimates, we collected manual classifications of 533 homonyms over 50,000 lines of subtitles, and of 357 homonyms across over 5000 homonym–associate pairs. This database—publicly available at: www.blairarmstrong.net/homonymnorms/—constitutes a novel resource for computational cognitive modeling and computational linguistics, and we offer suggestions around good practices for its use in training and testing models on labeled data

Make a Difference with Effective Actions for Healthy Children – The New Georgia Nutrition and Physical Activity Assessment Tool!

Presenter will Provide