Psycholinguistic measures for German verb pairs: Semantic transparency, semantic relatedness, verb family size, and age of reading acquisition

A Chinese verb semantic feature dataset (CVFD)

Language is an advanced cognitive function of humans, and verbs play a crucial role in language. To understand how the human brain represents verbs, it is critical to analyze what knowledge humans have about verbs. Thus, several verb feature datasets have been developed in different languages such as English, Spanish, and German. However, there is still a lack of a dataset of Chinese verbs. In this study, we developed a semantic feature dataset of 1140 Chinese Mandarin verbs (CVFD) with 11 dimensions including verb familiarity, agentive subject, patient, action effector, perceptual modality, instrumentality, emotional valence, action imageability, action complexity, action intensity, and the usage scenario of action. We calculated the semantic features of each verb and the correlation between dimensions. We also compared the difference between action, mental, and other verbs and gave some examples about how to use CVFD to classify verbs according to different dimensions. Finally, we discussed the potential applications of CVFD in the fields of neuroscience, psycholinguistics, cultural differences, and artificial intelligence. All the data can be found at https://osf.io/pv29z/ .

Wolf-hound vs. sled-dog: neurolinguistic evidence for semantic decomposition in the recognition of German noun-noun compounds

Animacy is an intrinsic semantic property of words referring to living things. A long line of evidence shows that words with animate referents require lower processing costs during word recognition than words with inanimate referents, leading among others to a decreased N400 amplitude in reaction to animate relative to inanimate objects. In the current study, we use this animacy effect to provide evidence for access to the semantic properties of constituents in German noun-noun compounds. While morphological decomposition of noun-noun compounds is well-researched and illustrated by the robust influence of lexical constituent properties like constituent length and frequency, findings for semantic decomposition are less clear in the current literature. By manipulating the animacy of compound modifiers and heads, we are able to manipulate the relative ease of lexical access strictly due to intrinsic semantic properties of the constituents. Our results show additive effects of constituent animacy, with a higher number of animate constituents leading to gradually attenuated N400 amplitudes. We discuss the implications of our findings for current models of complex word recognition, as well as stimulus construction practices in psycho-and neurolinguistic research.