Conveying (discrete) emotionality with novel words.

Affective dimensions (i.e., valence and arousal) and discrete basic emotions (i.e., anger, disgust, fear, happiness, sadness) are the main affective sources of information that explain the semantic features of words. Recent studies suggest that humans are able to assign emotionality even to pseudowords, plausible verbal stimuli that do not belong to a given language which serve as proxies for never encountered, real words (i.e., novel words). So far, evidence at our disposal is mainly limited to valence (i.e., the hedonic tone of a word's reference, from pleasant to unpleasant) and arousal (i.e., the degree of activation/excitation elicited by stimuli), while investigating discrete emotionality may facilitate a more refined understanding of the processes at hand. Here, across three experiments, we probed (a) humans' ability to convey discrete emotions when generating novel word stimuli to express the meanings of given emotional words and (b) humans' ability to decode or understand such emotionality when processing these human-generated novel words. Leveraging estimates from a word embedding model, results showed that individuals can reliably and equally encode novel conceptual information carrying affective information for all categories of emotions. However, a better performance for anger, fear, and happiness stimuli was found when implicitly decoding affective meaning in novel words. Theoretically, these processes can be interpreted from an evolutionary perspective, and more broadly, they can be traced back to humans' ability to process systematic, nonarbitrary form-meaning information. We discuss the implications of these findings for language and emotional models dealing with the representation of affective conceptual features. (PsycInfo Database Record (c) 2026 APA, all rights reserved).