BayesPrag@EEG: The Interaction of Bayesian Pragmatics and Lexical Semantics in Linguistic Interpretation: Using Event-related Potentials to Investigate Hearers’ Probabilistic Predictions

Principal investigator:
Prof. Dr. Markus Werning
Ruhr-Universität Bochum

Project description:
How do discourse contexts influence the way sentence meaning is composed from lexical meaning? We contrast two views of how contextual influence can be explained. The Semantic Similarity View maintains that discourse context affects sentence meaning mainly because of the semantic similarity between the words in the discourse context and the words in the target sentence (e.g., as in semantic priming, Otten & Van Berkum, 2008). The Free Pragmatic View, in contrast, defends the claim that also pragmatic aspects of the discourse context, other than the mere resolution of indexicals and anaphors, can immediately affect sentence meaning composition, allowing for a free pragmatic enrichment at any stage of sentence meaning composition (Recanati, 2010). Our project aims at adjudicating between these competing views. To this aim, we introduce a Predictive Completion Task in which the hearer at every moment in a communicative situation has to generate a probabilistic prediction about how a sentence/discourse being uttered by the speaker will be continued (Cosentino et al., 2017). The Semantic Similarity View and the Free Pragmatic View make different predictions as to how this task will be solved by the hearer. These predictions can be quantitatively determined, on the one hand, by using Latent Semantic Analysis (Landauer & Dumais, 1997) to obtain semantic similarity values and, on the other hand, by adopting the framework of Bayesian Pragmatics (Frank & Goodman, 2012) to calculate the pragmatic influence. We use the so-called Qualia Structure introduced in the Generative Lexicon approach by Pustejovsky (1995) as a model of lexical structure and focus on how discourse contexts interact with the Telic, the Agentive and the Formal components in the lexical entry of concrete nouns. We will test the predictions of the Semantic Similarity and the Free Pragmatic view in EEG using the empirically well-established observation that the conditional probability of a word given a preceding context is negatively correlated with the amplitude of its N400 component. In the light of the experimental data we will combine tools of formal semantics with those of Bayesian Pragmatics to develop a model of how discourse context affects the composition of sentence meanings by means of free pragmatic enrichment. We expect the results of the project to have a major impact on the debate in the philosophy of language between Semantic Minimalism and Truth-Conditional Pragmatics, and especially on whether compositionality should be understood in a more or less rigorous way. We aim at readjusting the principle of compositionality in a way that is consistent with the experimental findings. The project may also gain insight into the structure of mentally represented lexical and sentence meanings and the neuro-cognitive processes underlying lexical retrieval and sentence meaning composition.

Frank, M. C., & Goodman, N. D. (2012). Predicting Pragmatic Reasoning in Language Games. Science, 336, 998.
Cosentino, E., Baggio, G., Kontinen, J., & Werning, M. (2017). The time-course of sentence meaning composition. N400 effects of the interaction between context-induced and lexically stored affordances. Frontiers in Psychology, 8(818). DOI: 10.3389/fpsyg.2017.00813.
Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review, 104, 211–240.
Otten, M., & Van Berkum, J. J. A. (2008). Discourse-Based Word Anticipation During Language Processing: Prediction or Priming? Discourse Processes, 45, 464–496.
Pustejovsky, J. (1995). The Generative Lexicon. Cambridge, MA: MIT Press.
Recanati, F. (2010). Truth-Conditional Pragmatics. Oxford University Press.