Abstract
This study provides implicit verb consequentiality norms for a corpus of 305 English verbs, for which Ferstl et al. (Behavior Research Methods, 43, 124-135, 2011) previously provided implicit causality norms. An online sentence completion study was conducted, with data analyzed from 124 respondents who completed fragments such as “John liked Mary and so…”. The resulting bias scores are presented in an Appendix, with more detail in supplementary material in the University of Sussex Research Data Repository (via https://doi.org/10.25377/sussex.c.5082122), where we also present lexical and semantic verb features: frequency, semantic class and emotional valence of the verbs. We compare our results with those of our study of implicit causality and with the few published studies of implicit consequentiality. As in our previous study, we also considered effects of gender and verb valence, which requires stable norms for a large number of verbs. The corpus will facilitate future studies in a range of areas, including psycholinguistics and social psychology, particularly those requiring parallel sentence completion norms for both causality and consequentiality.
Highlights
Language researchers have long used normative data both to investigate effects such as that of frequency on word identification and to control for those effects when other, more subtle, influences on those processes are under investigation
We looked at whether the four main semantic categories of verb showed the biases predicted by Crinean and Garnham (2006) and whether the consequentiality biases of the semantic classes were related to the causal biases in the way predicted in that paper
The results replicate the small number of previous studies on consequentiality, and allow for a detailed examination of the hypotheses of Crinean and Garnham (2006) about the relation between implicit causality and implicit consequentiality for the four classes of verbs standardly recognized in the implicit causality literature: Agent-Patient (AgPat), Agent-Evocator (AgEvo), Stimulus-Experiencer (StimExp), and Experiencer-Stimulus (ExpStim)
Summary
Language researchers have long used normative data both to investigate effects such as that of frequency on word identification and to control for those effects when other, more subtle, influences on those processes are under investigation. For less commonly investigated features, for example implicit causality of verbs, small-scale norms were often collected for individual studies. The SUBTLEX-UK norms for British English (Van Heuven, Mandera, Keuleers, & Brysbaert, 2014) are based on a corpus of around 200 million tokens, compared with the one millionword Brown Corpus that was used to create the classic Kučera and Francis (1967) norms, and have advantages over other sets of norms (see Van Heuven et al, 2014, for details) Another recent set of norms with multiple measures for a very. Crinean and Garnham showed that these relations held in a small corpus of implicit causality and consequentiality norms collected by Stewart, Pickering, and Sanford (1998b), but they have not been established more generally. The consequential relationship can be signalled linguistically, for example by a connective such as “and so”, as in (2)
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