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Towards a model of predictive processing of Implicit Causality

Oliver Bott & Torgrim Solstad

Bielefeld University

Implicit Causality (IC) verbs constitute a central topic in research on prediction in natural language processing. Selecting for two animate arguments, IC verbs display a strong preference for an explanation focusing on one argument:

(1) Mary fascinated John because … she was very clever.                          fascinate: subject bias

(2) Mary congratulated John because … he won the competition.             congratulate: object bias

The predictive nature of IC is still insufficiently understood, however, as witnessed by the recurring debate on integration vs. focusing/prediction. Key questions include: What is predicted? A referent, a particular realization of the referent (she/Mary) or a type of explanation [1; 10]? Furthermore, what triggers the prediction: lexical semantics or world knowledge [2; 8; 10]? And finally, what is the processing profile of IC [3]?

Based on a formal theory of IC [1; 10], results from experimental research [3; 5; 9] and recent models of predictive processing [6; 7], we propose a comprehensive framework for the processing of IC. Crucially, we consider in detail the relation between the nature of what is predicted (the predictee [8]) and the properties of particular linguistic expressions such as pronouns that may be taken to (in)validate predictions. Based on previous research, we also evaluate the range of top-down and bottom-up processes: Which linguistic levels are involved and how do they interact? Our study shows that a closer investigation of the relation between predictees and (in)validators of predictions in general may contribute towards a better understanding of – and potentially more precise models of – language-based prediction.

  • 1. Bott, Oliver and Torgrim Solstad (2014): "From verbs to discourse – a novel account of implicit causality". In B. Hemforth, B. Mertins, & C. Fabricius-Hansen (Eds.), Psycholinguistic approaches to meaning and understanding across languages, 213–251. Springer.
  • 2. Marcelle Crinean and Alan Garnham (2006): Implicit causality, implicit consequentiality and semantic roles. Language and Cognitive Processes 21(5), 636–648.
  • 3. Alan Garnham, Scarlett Child, and Sam Hutton (2020): Anticipating causes and consequences. Journal of Memory and Language 114, Article 104130.
  • 4. Kamide, Yuki (2008): Anticipatory processes in sentence processing. Language and Linguistics Compass 2(4), 647–670.
  • 5. Koornneef, Arnout W., & Joost van Berkum (2006): On the use of verb-based implicit causality in sentence comprehension: Evidence from self-paced reading and eye tracking. Journal of Memory and Language 54(4), 445–465.
  • 6. Kuperberg, Gina and Florian T. Jaeger (2016): What do we mean by prediction in language comprehension? Language, Cognition and Neuroscience 31(1), 32–59.
  • 7. Pickering, Martin J. and Chiara Gambi (2018): Predicting while comprehending language: A theory and review. Psychological Bulletin 144(10), 1002–1044.
  • 8. Pickering, Martin J. and Asifa Majid (2007): What are implicit causality and consequentiality? Language and Cognitive Processes 22(5), 780–788.
  • 9. Pyykkönen, Pirita and Juhani Järvikivi (2010): Activation and persistence of implicit causality information in spoken language comprehension. Experimental Psychology 57(1), 5–16.
  • 10. Solstad, Torgrim and Oliver Bott (2022): On the nature of implicit causality and consequentiality: the case of psychological verbs. Language, Cognition and Neuroscience 37(10), 1311–1340