Abstract
Neurophysiological data from a range of typologically diverse languages provide evidence for a cross-linguistically valid, actor-based strategy of understanding sentence-level meaning. This strategy seeks to identify the participant primarily responsible for the state of affairs (the actor) as quickly and unambiguously as possible, thus resulting in competition for the actor role when there are multiple candidates. Due to its applicability across languages with vastly different characteristics, we have proposed that the actor strategy may derive from more basic cognitive or neurobiological organizational principles, though it is also shaped by distributional properties of the linguistic input (e.g. the morphosyntactic coding strategies for actors in a given language). Here, we describe an initial computational model of the actor strategy and how it interacts with language-specific properties. Specifically, we contrast two distance metrics derived from the output of the computational model (one weighted and one unweighted) as potential measures of the degree of competition for actorhood by testing how well they predict modulations of electrophysiological activity engendered by language processing. To this end, we present an EEG study on word order processing in German and use linear mixed-effects models to assess the effect of the various distance metrics. Our results show that a weighted metric, which takes into account the weighting of an actor-identifying feature in the language under consideration outperforms an unweighted distance measure. We conclude that actor competition effects cannot be reduced to feature overlap between multiple sentence participants and thereby to the notion of similarity-based interference, which is prominent in current memory-based models of language processing. Finally, we argue that, in addition to illuminating the underlying neurocognitive mechanisms of actor competition, the present model can form the basis for a more comprehensive, neurobiologically plausible computational model of constructing sentence-level meaning.
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Notes
Note that, though the model is hierarchically organized, it is not modular in the traditional Fodorian sense (Fodor 1983). Firstly, due to the cascaded nature of processing, a particular processing step need not be fully complete before the next step is initiated. Secondly, from a neurobiological perspective, connections within each pathway are inherently bidirectional such that top-down modulations of information processing are always possible. Nevertheless, we assume that there is an asymmetry in the directionality of information flow based on the tenet of hierarchical organization.
In this regard, the assumptions of the eADM differ from those of the Competition Model (e.g. Bates et al. 1982, 2001, MacWhinney and Bates 1989), which assumes that a strong cue for the undergoer role (e.g. accusative case in a language such as German or Hungarian) can, to all intents and purposes, exclude an argument from being considered a potential actor. The eADM, by contrast, posits that a sole argument is always considered for the actor role no matter how bad a candidate it is—unless there is a second, more optimal candidate (for discussion, see Bornkessel-Schlesewsky and Schlesewsky to appear).
In an ergative language such as Hindi, the actor argument in a transitive (two-participant) event is not morphosyntactically “privileged” in the sense that it does not agree with the verb, for example. Thus, it does not qualify for grammatical subjecthood in the same way as a transitive actor in a non-ergative language such as German, Dutch or Italian. The results from Hindi thus provide strong converging support for the assumption that the actor preference is interpretive rather than grammatical in nature.
This restriction exists primarily to simplify the implementation in a single Python program (see Technical Notes); to better model the waterfall data flow, coroutines could be used or Stage 1 and Stage 2 could be split into two programs connected by Unix pipes.
Furthermore, the article also carries number information, albeit with a small ambiguity that is resolved through further adjectives or the marking on the head noun.
Of course, contextual effects also play a role in normal language use.
For the purposes of the present paper,+dep may simply be considered a convenient label for a poor actor candidate. For an in-depth discussion of \({\pm }\)dep and a motivation in terms of a previous version of the eADM, see (Bornkessel and Schlesewsky 2006)
This is perhaps an advantage—in languages where inclusive and exclusive first person are morphologically distinct, this could be represented by the interaction of \({\pm }\)1st. Person and \({\pm }\)2nd. Person. This added complexity nonetheless introduces its own cost and brings language specific features deeper into the model.
In the case of a single argument, e.g. intransitivity, the distinction measure is not performed and the model depends solely on the threshold comparison. The present experiment included only monotransitive sentences.
\(d(x,y) = \sum \limits _{i} | y_{i} - x_{i} |\)
\(d(x,y) ={\sqrt {\sum \limits _{i}{( y_{i} - x_{i} )^{2}}}}\)
In as far as all features are treated equally—some languages may not take advantage of certain features, e.g. English largely does not use case.
This follows very straightforwardly from the definition and standard properties of the dot product:
$$\begin{array}{@{}rcl@{}} \vec{w}\cdot\vec{NP2}-\vec{w}\cdot\vec{NP1} &=& \sum\limits_{i} w_{i} \cdot \mathrm{NP2}_{i} - \sum\limits_{i} w_{i} \cdot \mathrm{NP1}_{i} \\ &=& \sum\limits_{i} w_{i} \cdot (\mathrm{NP2}_{i} - \mathrm{NP1}_{i}) \\ &=& \vec{w} \cdot (\vec{NP2} - \vec{NP1}) \end{array} $$As noted by an anonymous reviewer, the EEG experiment presented below does not include any number or animacy contrasts. This however does not present any great problem for the data at hand: due to properties of the metrics at hand, the non contrasting features simply cancel out and do not even introduce additional parametric levels into the respective prominence metrics. These features remain in the models present because their presence does not detract from the comparisons in question and avoids an experiment-specific model. One subtle disadvantage does come into play here though: the fit of the weights for these two features is not tested. Especially our ranking of position relative to animacy may prove problematic and, as such, more explicit testing, manipulation and determination of model weights is planned for future research.
This follows from the notion of an actor space—we can expand or contract the space by a constant multiple without changing the inherent properties of it. Specifically, \(c\vec {v}\cdot {}c\vec {w} = c(\vec {v}\cdot {}\vec {w})\).
Subject to the constraints of the effects this has on precision and representation on the computing machine in question. Theoretically, we could divide all of these values by 1000 (the maximum weight given here), giving us coefficients on \([0,1]\), which would reflect their impact in the notation of probability theory. This is a very interesting approach, as the deterministic impact of case would receive a (probability) coefficient of one—certainty. However, this all too easily leads to the assumption that there is necessarily a single feature which, when unambiguous, is singularly deterministic in its influence. Or, in the particular case of German, that the impact of case is always deterministic—clearly, this is not the case as all too often, the morphological marking is ambiguous: \(0 \times 1000\) is still 0.
The models resolved for ambiguity in the P600 time window have only random intercepts, as models with random slopes failed to converge.
Higher order interactions were excluded for three reasons. First, comparing models which differ in random-effect structure is less straightforward than those which differ in only fixed-effect structure. (Even for the fixed effects, the comparison between non nested models requires information-theoretic criteria, see main text.) Second, models with higher order interactions in the random-effects structure did not always converge and due to the aforementioned complexities of comparing random-effects structures, it is not clear which of several higher-order models to choose from. Finally, computational complexity increases extremely quickly with random effect complexity. Limiting the random-effects structure to the maximal common one provides an acceptable balance between estimation accuracy, ease of comparison, and computer time.
Models in which the parameters for one model form a proper subset for the parameters of the other.
More rigorous methods are available for dynamically determining the time window and topographical distribution of components. Maris (2004) and Maris and Oostenveld (2007) propose the necessary methods for non parametric method testing and determination of the effects in time and space (topography). Issues of computational tractability as well as data set size (different (sub)sets of data have to be used for determining the spatiotemporal distribution and testing it) reaffirmed our decision against introducing too many non-traditional methods for this initial computational model.
The slightly better performance of dist in this comparison of the unambiguous conditions is twofold: (1) it has fewer degrees of freedom and hence a smaller overfitting penalty in the AIC measure, and (2) the positive-only nature of dist lines up with the directionality of the positivity (but not the negativity).
Vosse and Kempen (2009) describe their parsing framework as a “dynamic model of syntactic parsing based on activation and inhibitory competition.”
Note that an explanation along these lines can also account for the dissociation between mild and strong conflicts observed by van de Meerendonk et al. (2010). As it appears plausible to assume that only the strong conflicts were registered as behaviorally significant, our account derives the finding of a late positivity for these conflicts, while no such effect was observed for mild conflicts. This explanation leads to the testable prediction that, with different task instructions (e.g. a judgment task emphasizing that even mild implausibilities should be classified as such), van de Meerendonk et al. (2010)’s mild conflict stimuli should also engender a late positivity.
This proposal of a tight interrelationship between domain-general and linguistic actor features is supported by the recent observation that properties of an ideal actor may depend—at least to some degree—on the characteristics of one’s native language (Fausey et al. 2010; Fausey and Boroditsky 2011).
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We would like to thank Rick Lewis and Joakim Nivre for valuable discussions and suggestions related to the development of the computational model. We would also like to thank Isabel Plauth for the data acquisition.
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Parts of the research reported here were supported by the German Research Foundation (grant BO 2471/3-2) and the EEG study was performed while IBS was at the Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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Alday, P.M., Schlesewsky, M. & Bornkessel-Schlesewsky, I. Towards a Computational Model of Actor-Based Language Comprehension. Neuroinform 12, 143–179 (2014). https://doi.org/10.1007/s12021-013-9198-x
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DOI: https://doi.org/10.1007/s12021-013-9198-x