Breaking the World into Symbols

Breaking the World into Symbols Adam D. November (adam.november@gmail.com) Stanford University - Palo Alto, CA 94305 USA Nicolas Davidenko (ndaviden@stanford.edu) Stanford University - Palo Alto, CA 94305 USA Michael Ramscar (ramscar@gmail.com) Stanford University - Palo Alto, CA 94305 USA Abstract How do people come to assign symbolic labels to continuous dimensions? Previous work has shown that prediction-error- driven models are sensitive to the order of labels and exem- plars during training; similar patterns of learning are found found in adult learners trained to associate labels with dis- crete visual stimuli. Here we provide further evidence in sup- port of the hypothesis that an error-driven mechanism under- lies word learning, using continuous stimuli to explore the in- teractions of temporal structure, stimulus frequency, and dis- tinctiveness in shaping associative learning. We conclude that learning to use features of exemplars to predict labels results in over-representation of diagnostic information, as shown by improved associative performance on stimuli near category boundaries. This is consistent with an error-driven model of label acquisition, and highlights the importance of the asso- ciative and prediction-based (rather than exclusively syntactic) aspects of symbolic cognition. Keywords: Symbolic Cognition; Categorization; Language; Learning; Representation; Concepts; Computational model- ing; Prediction Introduction The world is full of perceptually similar stimuli that prompt behaviorally diverse responses. Picking ripe fruit, avoiding poisonous creatures, and even interpreting subtle facial ex- pressions all rely upon careful discriminations of subtle cues. What role does language play in learning such discrimina- tions? How can the process of learning a symbol change what is seen? Classic referential theories of language have little to say on the content of individual words, instead focusing on the syntactical rules for combining abstract symbols. Such theo- ries largely ignore the process of learning words; for exam- ple, Fodor (1981) has argued that the concept of carburetor must be innate, restricting the problem of word learning to the problem of finding the proper mapping between new words and the appropriate innately elaborated concepts. Many have discussed this and other limitations of such referential theo- ries of language (Tomasello, 2003; Wittgenstein, 1953). Re- cent work has rejected the referential view of language, and has instead argued the importance of prediction for develop- ing symbolic representations (Ramscar, Yarlett, Dye, Denny, & Thorpe, 2010). Specifically, this analysis applies error- driven learning to the problem of determining which cues in the world to associate with a word, and shows how varying the structure of information in time elicits different patterns of association. Within a predictive framework, words can either be used as predictors or as predictions. Used as predictors, the words ‘dog’ or ‘wolf’ often predict very similar outcomes: canine creatures which share many features, and thus error driven- systems will learn quite similar representations for each over time. In contrast, systems using the appearance of a dog or a wolf to predict the relevant label will have highly distinct outcomes (either the label ‘dog’ or the label ‘wolf’). Over time, these highly distinct outcomes can serve as a useful pre- diction targets, allowing an error-driven learner to isolate the diagnostic features (e.g. ‘ear floppiness’) that lead to correct prediction. Recently, Ramscar et al. (2010) demonstrated effects of in- formation structure on symbolic learning by training subjects to learn to name novel stimuli while manipulating temporal order. We argue that this sensitivity to information structure is the result of a fundamental aspect of symbols: informa- tionally impoverished, symbols must serve as abstractions of the things they represent. When learning the symbol’s many associations with the world, it can be advantageous to learn these symbols as consequents predicted from cues in the world, rather than as cues used to predict the world. Such an effect was found for for participants learning to label various fribble categories (Tarr, 2000). In the case of fribbles, the relevant cues consist of a number of discrete features. When trained to predict from Features to Labels (FL), subjects were better able to learn the relevant categories, than when trained from Labels to Features (LF). Modeling suggested that FL- trained subjects improved by learning to use the diagnostic features to inhibit potentially conflicting labels. In contrast, LF learning developed a more ‘veridical’ but less discrimina- tive model that represents the relative feature frequencies for each label. This Feature-Label-Order (FLO) effect suggested that learning natural symbols is sensitive to information struc- ture. Here, we attempt to explore if such FLO effects can be found while learning to discriminate the boundaries of con- tinuous dimensions. Is learning a mapping between continuous exemplars and discrete labels also affected by the information structure available at learning? Demonstrating a FLO effect in the realm of continuous stimuli would reinforce the idea that prediction is fundamental to symbolic learning and category learning. However, it is possible that a continuous dimension would not provide the consistent ‘hooks’ for discriminative