Is predicting during language processing worth it? Effects of cloze probability and semantic similarity on failed predictions.

Abstract

Prediction during language processing has been hypothesized to lead to processing benefits. These possible benefits have led to several prominent theories that center around prediction as an essential mechanism in language processing. Such theories typically assume predicting is better than not predicting at all, but do not always account for the potential processing costs from failed predictions. Predicting wrongly can be costly, but the cost may depend on how wrong the prediction was. Across three experiments, we manipulate cloze probability, semantic relatedness, and language modality (production vs. comprehension) to determine whether predicting almost correctly is better than predicting completely incorrectly, and if so, if predicting almost correctly is better than not predicting at all. Results showed that when a predicted ending is replaced with a related term, it is processed faster than when it is replaced with an unrelated term, but that related term is not named more quickly than when it appears after a low constraint sentence. This pattern held regardless of whether participants were asked to produce the sentence-final term by naming a picture (Experiments 1 and 2), or if they were asked to perform a semantic classification of the sentence-final word (Experiment 3). Thus, predicting almost correctly is better than predicting completely incorrectly, but it's not better than not predicting at all. This carries implications for current accounts that argue for processing benefits of prediction during language processing, and suggests that prediction may be used to fine-tune the language system rather than to speed language processing. (PsycInfo Database Record (c) 2024 APA, all rights reserved).