Publications

Abstract

It has long been recognised that phrases and sentences are organised hierarchically, but many computational models of language treat them as sequences of words without computing constituent structure. Against this background, we conducted two experiments which showed that participants interpret ambiguous noun phrases, such as second blue ball, in terms of their abstract hierarchical structure rather than their linear surface order. When a neural network model was tested on this task, it could simulate such “hierarchical” behaviour. However, when we changed the training data such that they were not entirely unambiguous anymore, the model stopped generalising in a human-like way. It did not systematically generalise to novel items, and when it was trained on ambiguous trials, it strongly favoured the linear interpretation. We argue that these models should be endowed with a bias to make generalisations over hierarchical structure in order to be cognitively adequate models of human language.

Abstract

Recent research has established that cortical activity “tracks” the presentation rate of syntactic phrases in continuous speech, even though phrases are abstract units that do not have direct correlates in the acoustic signal. We investigated whether cortical tracking of phrase structures is modulated by the extent to which these structures compositionally determine meaning. To this end, we recorded electroencephalography (EEG) of 38 native speakers who listened to naturally spoken Dutch stimuli in different conditions, which parametrically modulated the degree to which syntactic structure and lexical semantics determine sentence meaning. Tracking was quantified through mutual information between the EEG data and either the speech envelopes or abstract annotations of syntax, all of which were filtered in the frequency band corresponding to the presentation rate of phrases (1.1–2.1 Hz). Overall, these mutual information analyses showed stronger tracking of phrases in regular sentences than in stimuli whose lexical-syntactic content is reduced, but no consistent differences in tracking between sentences and stimuli that contain a combination of syntactic structure and lexical content. While there were no effects of compositional meaning on the degree of phrase-structure tracking, analyses of event-related potentials elicited by sentence-final words did reveal meaning-induced differences between conditions. Our findings suggest that cortical tracking of structure in sentences indexes the internal generation of this structure, a process that is modulated by the properties of its input, but not by the compositional interpretation of its output.

Abstract

Visual narratives make use of various means to convey referential and co-referential meaning, so comprehenders
must recognize that different depictions across sequential images represent the same character(s). In this study,
we investigated how the order in which different types of panels in visual sequences are presented affects how
the unfolding narrative is comprehended. Participants viewed short comic strips while their electroencephalo-
gram (EEG) was recorded. We analyzed evoked and induced EEG activity elicited by both full panels (showing a
full character) and refiner panels (showing only a zoom of that full panel), and took into account whether they
preceded or followed the panel to which they were co-referentially related (i.e., were cataphoric or anaphoric).
We found that full panels elicited both larger N300 amplitude and increased gamma-band power compared to
refiner panels. Anaphoric panels elicited a sustained negativity compared to cataphoric panels, which appeared
to be sensitive to the referential status of the anaphoric panel. In the time-frequency domain, anaphoric panels
elicited reduced 8–12 Hz alpha power and increased 45–65 Hz gamma-band power compared to cataphoric
panels. These findings are consistent with models in which the processes involved in visual narrative compre-
hension partially overlap with those in language comprehension.