Idiom Recognition Task
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An Idiom Recognition Task is a MWE recognition task that is restricted to idioms.
- See: Idiom Recognition System.
References
2014
- (Cacciari & Tabossi, 2014) ⇒ Cristina Cacciari, and Patrizia Tabossi. (2014). “Idioms: Processing, Structure, and Interpretation.” Psychology Press,
2010
- (Vespignani et al., 2010) ⇒ Francesco Vespignani, Paolo Canal, Nicola Molinaro, Sergio Fonda, and Cristina Cacciari. (2010). “Predictive Mechanisms in Idiom Comprehension.” Journal of Cognitive Neuroscience 22, no. 8
- ABSTRACT: Prediction is pervasive in human cognition and plays a central role in language comprehension. At an electrophysiological level, this cognitive function contributes substantially in determining the amplitude of the N400. In fact, the amplitude of the N400 to words within a sentence has been shown to depend on how predictable those words are: The more predictable a word, the smaller the N400 elicited. However, predictive processing can be based on different sources of information that allow anticipation of upcoming constituents and integration in context. In this study, we investigated the ERPs elicited during the comprehension of idioms, that is, prefabricated multiword strings stored in semantic memory. When a reader recognizes a string of words as an idiom before the idiom ends, she or he can develop expectations concerning the incoming idiomatic constituents. We hypothesized that the expectations driven by the activation of an idiom might differ from those driven by discourse-based constraints. To this aim, we compared the ERP waveforms elicited by idioms and two literal control conditions. The results showed that, in both cases, the literal conditions exhibited a more negative potential than the idiomatic condition. Our analyses suggest that before idiom recognition the effect is due to modulation of the N400 amplitude, whereas after idiom recognition a P300 for the idiomatic sentence has a fundamental role in the composition of the effect. These results suggest that two distinct predictive mechanisms are at work during language comprehension, based respectively on probabilistic information and on categorical template matching.