Valence, arousal, familiarity, concreteness, and imageability ratings for 292 two-character Chinese nouns in Cantonese speakers in Hong Kong
By Lydia Ting Sum Yee. 2017
47 citations recorded by the source corpus.
Words are frequently used as stimuli in cognitive psychology experiments, for example, in recognition memory studies. In these experiments, it is often desirable to control for the words’ psycholinguistic properties because differences in such properties across experimental conditions might introduce undesirable confounds. In order to avoid confounds, studies typically check to see if various affective and lexico-semantic properties are matched across experimental conditions, and so databases that contain values for these properties are needed. While word ratings for these variables exist in English and other European languages, ratings for Chinese words are not comprehensive. In particular, while ratings for single characters exist, ratings for two-character words—which often have different meanings than their constituent characters, are scarce. In this study, ratings for 292 two-character Chinese nouns were obtained from Cantonese speakers in Hong Kong. Affective variables, including valence and arousal, and lexico-semantic variables, including familiarity, concreteness, and imageability, were rated in the study. The words were selected from a film subtitle database containing word frequency information that could be extracted and listed alongside the resulting ratings. Overall, the subjective ratings showed good reliability across all rated dimensions, as well as good reliability within and between the different groups of participants who each rated a subset of the words. Moreover, several well-established relationships between the variables found consistently in other languages were also observed in this study, demonstrating that the ratings are valid. The resulting word database can be used in studies where control for the above psycholinguistic variables is critical to the research design.
Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Cognitive Science & Psychology
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Citations in the corpus
- Towards fairer datasets: filtering and balancing the distribution of the people subtree in the ImageNet hierarchy (2019, background): “Prior works have demonstrated a strong correlation between familiarity and imageability [9, 32, 79], which explains the low imageability of the less frequent cotter .”