The Effect of Semantic Similarity is a Function of Contextual Constraint

The Effect of Semantic Similarity is a Function of Contextual Constraint Hongoak Yun (hyun3@buffalo.edu) Gail Mauner (mauner@buffalo.edu) Department of Psychology, 204 Park Hall Buffalo, NY14260 USA Douglas Roland (droland@buffalo.edu) Jean-Pierre Koenig (jpkoenig@buffalo.edu) Department of Linguistics, 609 Baldy Hall Buffalo, NY 14260 USA Abstract We investigate how the degree to which a context constrains the words that could occur in a sentence affects the processing of the word that does occur. Roland et al. (2012) found that processing was facilitated when target words were more semantically similar to word alternatives that could have appeared. Because this effect is independent of word predictability, it suggests that comprehenders may have separate expectations for words and more general semantic features. We show that the semantic similarity effect is modulated by the degree of contextual constraint. We found that facilitation due to semantic similarity was greater when contexts were less constraining, and lower when contexts were more constraining, independent of word predictability. We interpret these results as suggesting that in highly constraining contexts, comprehenders may expect specific words, and face difficulties when these expectations are violated, while in less constraining contexts, they may have more general expectations for semantic properties shared between the words that could occur. Keywords: sentence processing; semantic similarity; predictability; entropy; contextual constraint; expectation- based language comprehension Introduction In expectation-based models of sentence comprehension, contextual information has an enormous effect on how words are integrated into sentences. These models predict that the degree of difficulty a reader encounters in integrating a new word into a sentence is either entirely or in large measure a function of how predictable that word is given prior context (e.g., Levy, 2008). Presumably this is because predicted words are activated by context in advance of when they are encountered, making them easier to retrieve from memory or because predictable words are easier to integrate into the representations being constructed during comprehension. The effect of predictability on processing time has been observed in many studies (e.g., Bicknell, Elman, Hare, McRae, & Kutas, 2010; Ehrlich & Rayner, 1981; Frisson, Rayner, & Pickering, 2005; Staub, 2011; DeLong, Urbach, & Kutas, 2005; Federmeier, Wlotko, De Ochoa-Dewald, & Kutas, 2007; Otten & Van Berkum, 2008; Van Berkum, Brown, Zwitserlood, Kooijman, & Hagoort, 2005). The relationship between a word’s predictability and the amount of effort required to process it has been formalized in a number of computational models of language processing known as surprisal models (e.g., Boston, Hale, Patil, Kliegl, & Vasishth, 2008; Hale, 2001; Levy, 2008; Pado, Crocker, & Keller, 2009). In these models, the amount of cognitive effort required to integrate a word into a sentence depends on the negative log probability of that word given its preceding context. Surprisal models have had considerable success in predicting differences in reading times based on a word’s predictability given its preceding context. As it turns out, the amount of processing effort associated with integrating a word into a sentence cannot be entirely reduced to its predictability given its preceding context. Roland, Yun, Koenig, and Mauner (2012) examined the effects of the semantic cohort of a target word (i.e., the other words that could appear in the same position/context as the target word) on the processing of a target word. They found that words that are more semantically similar to their semantic cohort are easier to process when word predictability and other factors are controlled for. This result is important because it points to a limitation in expectation- based computational accounts of sentence processing that claim that a word’s probability given its context is the sole predictor of processing effort (e.g., Levy, 2008). The results we present further constrain expectation-based accounts of sentence processing by showing that the effect of semantic similarity between a target word and its semantic cohort interacts with the degree of constraint provided by context. The findings of Schwanenfluegel and LaCount (1988) motivate the possibility that the benefits of semantic similarity on word integration might be modulated by contextual constraint. Schwanenflugel and LaCount found that unpredictable words that were semantically related to the most predictable word that could occur in the same position were processed faster than other equally unpredictable words that were also semantically unrelated to that most predictable word. What is crucial for this discussion is that the benefit of shared semantic information was not consistently observed for all unpredictable words. Shared semantic information only facilitated the processing of unpredictable words when contexts were weakly constraining.