Challenging incrementality in human language processing: two operations for a cognitive architecture
The description of language complexity and the cognitive load related to the different linguistic phenomena is a key issue for the understanding of language processing. Many studies have focused on the identification of specific parameters that can lead to a simplification or on the contrary to a complexification of the processing (e.g. the different difficulty models proposed in (Gibson, 2000), (Warren and Gibson, 2002), (Hawkins, 2001) ). Similarly, different simplification factors can be identified, such as the notion of activation, relying on syntactic priming effects making it possible to predict (or activate) a word (Vasishth, 2003). Several studies have shown that complexity factors are cumulative (Keller, 2005), but can be offset by simplification (Blache et al., 2006). It is therefore necessary to adopt a global point of view of language processing, explaining the interplay between positive and negative cumulativity, in other words compensation effects. From the computational point of view, some models can account more or less explicitly for these phenomena. This is the case of the Surprisal index (Hale, 2001), offering for each word an assessment of its integration costs into the syntactic structure. This evaluation is done starting from the probability of the possible solutions. On their side, symbolic approaches also provide an estimation of the activation degree, depending on the number and weight of syntactic relations to the current word (Blache et al., 2006); (Blache, 2013). These approaches are based on the classical idea that language processing is incremental and occurs word by word. There are however several experimental evidences showing that a higher level of processing is used by human subjects. Eyetracking data show for example that fixations are done by chunks, not by words (Rauzy and Blache, 2012). Similarly, EEG experiments have shown that processing multiword expressions (for example idioms) relies on global mechanisms (Vespignani et al., 2010); (Rommers et al., 2013). Starting from the question of complexity and its estimation, I will address in this presentation the problem of language processing and its organization. I propose more precisely, using computational complexity models, to define a cohesion index between words. Such an index makes it possible to define chunks (or more generally units) that are built directly, by aggregation, instead of syntactic analysis. In this hypothesis, parsing consists in two different processes: aggregation and integration.
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Challenging incrementality in human language processing: two operations for a cognitive architecture
Semantic Scholar · Linguistics · 2014
Abstract
The description of language complexity and the cognitive load related to the different linguistic phenomena is a key issue for the understanding of language processing. Many studies have focused on the identification of specific parameters that can lead to a simplification or on the contrary to a complexification of the processing (e.g. the different difficulty models proposed in (Gibson, 2000), (Warren and Gibson, 2002), (Hawkins, 2001) ). Similarly, different simplification factors can be identified, such as the notion of activation, relying on syntactic priming effects making it possible to predict (or activate) a word (Vasishth, 2003). Several studies have shown that complexity factors are cumulative (Keller, 2005), but can be offset by simplification (Blache et al., 2006). It is therefore necessary to adopt a global point of view of language processing, explaining the interplay between positive and negative cumulativity, in other words compensation effects. From the computational point of view, some models can account more or less explicitly for these phenomena. This is the case of the Surprisal index (Hale, 2001), offering for each word an assessment of its integration costs into the syntactic structure. This evaluation is done starting from the probability of the possible solutions. On their side, symbolic approaches also provide an estimation of the activation degree, depending on the number and weight of syntactic relations to the current word (Blache et al., 2006); (Blache, 2013). These approaches are based on the classical idea that language processing is incremental and occurs word by word. There are however several experimental evidences showing that a higher level of processing is used by human subjects. Eyetracking data show for example that fixations are done by chunks, not by words (Rauzy and Blache, 2012). Similarly, EEG experiments have shown that processing multiword expressions (for example idioms) relies on global mechanisms (Vespignani et al., 2010); (Rommers et al., 2013). Starting from the question of complexity and its estimation, I will address in this presentation the problem of language processing and its organization. I propose more precisely, using computational complexity models, to define a cohesion index between words. Such an index makes it possible to define chunks (or more generally units) that are built directly, by aggregation, instead of syntactic analysis. In this hypothesis, parsing consists in two different processes: aggregation and integration.