Quantification, prediction, and the online impact of sentence truth-value: Evidence from event-related potentials

Abstract

Do negative quantifiers like "few" reduce people's ability to rapidly evaluate incoming language with respect to world knowledge? Previous research has addressed this question by examining whether online measures of quantifier comprehension match the "final" interpretation reflected in verification judgments. However, these studies confounded quantifier valence with its impact on the unfolding expectations for upcoming words, yielding mixed results. In the current event-related potentials study, participants read negative and positive quantifier sentences matched on cloze probability and on truth-value (e.g., "Most/Few gardeners plant their flowers during the spring/winter for best results"). Regardless of whether participants explicitly verified the sentences or not, true-positive quantifier sentences elicited reduced N400s compared with false-positive quantifier sentences, reflecting the facilitated semantic retrieval of words that render a sentence true. No such facilitation was seen in negative quantifier sentences. However, mixed-effects model analyses (with cloze value and truth-value as continuous predictors) revealed that decreasing cloze values were associated with an interaction pattern between truth-value and quantifier, whereas increasing cloze values were associated with more similar truth-value effects regardless of quantifier. Quantifier sentences are thus understood neither always in 2 sequential stages, nor always in a partial-incremental fashion, nor always in a maximally incremental fashion. Instead, and in accordance with prediction-based views of sentence comprehension, quantifier sentence comprehension depends on incorporation of quantifier meaning into an online, knowledge-based prediction for upcoming words. Fully incremental quantifier interpretation occurs when quantifiers are incorporated into sufficiently strong online predictions for upcoming words.

Figure 1

Grand-average event-related potentials (ERPs) at electrode CP1 elicited by critical words in true and false sentences containing positive and negative quantifier expressions, across all participants and for the separate verification instructions (upper graphs). These ERP waveforms are high cut-off filtered at 5 Hz for presentation purposes, and negativity is plotted upward. An example sentence in each condition is shown below the ERP waveforms, along with the scalp distribution of the overall sentence truth-value effect (false minus true) for positive and negative quantifier sentences. The lower graphs illustrate the nature of the interaction pattern between cloze value, pretested truth-value, and quantifier type. For ease of exposition, these graphs show the mean fitted values from the mixed effects model results separately for sentences with high cloze values and low cloze values as scatterplots. See the online article for the color version of this figure.