Energetic Arousal and Language: Predictions From the Computational Theory of Quantifiers Processing

Objective: The author examines the relationship between energetic arousal (EA) and the processing of sentences containing natural-language quantifiers. Background: Previous studies and theories have shown that energy may differentially affect various cognitive functions. Recent investigations devoted to quantifiers strongly support the theory that various types of quantifiers involve different cognitive functions in the sentence-picture verification task. Method: In the present study, 201 students were presented with a sentence-picture verification task consisting of simple propositions containing a quantifier that referred to the color of a car on display. Color pictures of cars accompanied the propositions. In addition, the level of participants' EA was measured before and after the verification task. Results: It was found that EA and performance on proportional quantifiers (e.g., More than half of the cars are red) are in an inverted U-shaped relationship. Conclusion: This result may be explained by the fact that proportional sentences engage working memory to a high degree, and previous models of EA-cognition associations have been based on the assumption that tasks that require parallel attentional and memory processes are best performed when energy is moderate. Application: The research described in the present article has several applications, as it shows the optimal human conditions for verbal comprehension. For instance, it may be important in workplace design to control the level of arousal experienced by office staff when work is mostly related to the processing of complex texts. Energy level may be influenced by many factors, such as noise, time of day, or thermal conditions.