Semantic Richness Effects in Spoken Word Recognition: A Lexical Decision and Semantic Categorization Megastudy

Pseudowords play an important role in psycholinguistic experiments, either because they are required for performing tasks, such as lexical decision, or because they are the main focus of interest, such as in nonword-reading and nonce-inflection studies. We present a pseudoword generator that improves on current methods. It allows for the generation of written polysyllabic pseudowords that obey a given language's phonotactic constraints. Given a word or nonword template, the algorithm can quickly generate pseudowords that match the template in subsyllabic structure and transition frequencies without having to search through a list with all possible candidates. Currently, the program is available for Dutch, English, German, French, Spanish, Serbian, and Basque, and, with little effort, it can be expanded to other languages.

Emotion influences most aspects of cognition and behavior, but emotional factors are conspicuously absent from current models of word recognition. The influence of emotion on word recognition has mostly been reported in prior studies on the automatic vigilance for negative stimuli, but the precise nature of this relationship is unclear. Various models of automatic vigilance have claimed that the effect of valence on response times is categorical, an inverted U, or interactive with arousal. In the present study, we used a sample of 12,658 words and included many lexical and semantic control factors to determine the precise nature of the effects of arousal and valence on word recognition. Converging empirical patterns observed in word-level and trial-level data from lexical decision and naming indicate that valence and arousal exert independent monotonic effects: Negative words are recognized more slowly than positive words, and arousing words are recognized more slowly than calming words. Valence explained about 2% of the variance in word recognition latencies, whereas the effect of arousal was smaller. Valence and arousal do not interact, but both interact with word frequency, such that valence and arousal exert larger effects among low-frequency words than among high-frequency words. These results necessitate a new model of affective word processing whereby the degree of negativity monotonically and independently predicts the speed of responding. This research also demonstrates that incorporating emotional factors, especially valence, improves the performance of models of word recognition. PsycINFO Database Record (c) 2014 APA, all rights reserved.