Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Neuroinformatics
Published in: Neuroinformatics 1/2014

01-01-2014 | Original Article

Towards a Computational Model of Actor-Based Language Comprehension

Authors: Phillip M. Alday, Matthias Schlesewsky, Ina Bornkessel-Schlesewsky

Published in: Neuroinformatics | Issue 1/2014

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.
previous article Prolegomena to a Neurocomputational Architecture for Human Grammatical Encoding and Decoding
next article Template Construction Grammar: From Visual Scene Description to Language Comprehension and Agrammatism

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Appendix
Available only for authorised users
Footnotes
1
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.
 
2
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).
 
3
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.
 
4
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.
 
5
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.
 
6
Of course, contextual effects also play a role in normal language use.
 
7
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)
 
8
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.
 
9
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.
 
10
\(d(x,y) = \sum \limits _{i} | y_{i} - x_{i} |\)
 
11
\(d(x,y) ={\sqrt {\sum \limits _{i}{( y_{i} - x_{i} )^{2}}}}\)
 
12
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.
 
13
This follows very straightforwardly from the definition and standard properties of the dot product:
$$\begin{array}{@{}rcl@{}} \vec{w}\cdot\vec{NP2}-\vec{w}\cdot\vec{NP1} &=& \sum\limits_{i} w_{i} \cdot \mathrm{NP2}_{i} - \sum\limits_{i} w_{i} \cdot \mathrm{NP1}_{i} \\ &=& \sum\limits_{i} w_{i} \cdot (\mathrm{NP2}_{i} - \mathrm{NP1}_{i}) \\ &=& \vec{w} \cdot (\vec{NP2} - \vec{NP1}) \end{array} $$
 
14
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.
 
15
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, \(c\vec {v}\cdot {}c\vec {w} = c(\vec {v}\cdot {}\vec {w})\).
 
16
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.
 
17
The models resolved for ambiguity in the P600 time window have only random intercepts, as models with random slopes failed to converge.
 
18
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.
 
19
Models in which the parameters for one model form a proper subset for the parameters of the other.
 
20
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.
 
21
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).
 
22
Vosse and Kempen (2009) describe their parsing framework as a “dynamic model of syntactic parsing based on activation and inhibitory competition.”
 
23
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.
 
24
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).
 
Literature
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. doi:10.1109/TAC.1974.1100705.CrossRef
Arbib, M., Bonaiuto, J., Bornkessel-Schlesewsky, I., Kemmerer, D., MacWhinney, B., Nielsen, F., Oztop, E. (this issue). Action and language mechanisms in the brain: data, models and neuroinformatics. Neuroinformatics.
Baayen, R., Davidson, D., Bates, D. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59, 390–412.CrossRef
Barr, D. J., Levy, R., Scheepers, C., Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: keep it maximal. Journal of Memory and Language, 68, 255–278.CrossRef
Basten, U., Biele, G., Heekeren, H., Fiebach, C. (2010). How the brain integrates costs and benefits during decision making. Proceedings of the National Academy of Sciences, 107(50), 21,767–21,772.CrossRef
Bates, E., McNew, S., MacWhinney, B., Devescovi, A., Smith, S. (1982). Functional constraints on sentence processing: a cross-linguistic study. Cognition, 11, 245–299.PubMedCrossRef
Bates, E., Devescovi, A., Wulfeck, B. (2001). Psycholinguistics: a cross-language perspective. Annual Review of Psychology, 52, 369–396.PubMedCrossRef
Bornkessel, I. (2002). The argument dependency model: a neurocognitive approach to incremental interpretation (Vol. 28). Leipzig: MPI Series in Cognitive Neuroscience.
Bornkessel, I., & Schlesewsky, M. (2006). The extended argument dependency model: a neurocognitive approach to sentence comprehension across languages. Psychological Review, 113(4), 787–821.PubMedCrossRef
Bornkessel, I., Fiebach, C. J., Friederici, A. D. (2004a). On the cost of syntactic ambiguity in human language comprehension: an individual differences approach. Cognitive Brain Research, 21, 11–21.CrossRef
Bornkessel, I., McElree, B., Schlesewsky, M., Friederici AD (2004b). Multi-dimensional contributions to garden path strength: dissociating phrase structure from case marking. Journal of Memory and Language, 51, 495–522.CrossRef
Bornkessel, I., Zysset, S., Friederici, A. D., von Cramon, D. Y., Schlesewsky, M. (2005). Who did what to whom? The neural basis of argument hierarchies during language comprehension. Neuroimage, 26(1), 221—233.PubMedCrossRef
Bornkessel-Schlesewsky, I., & Schlesewsky, M. (2008). An alternative perspective on “semantic P600” effects in language comprehension. Brain Research Reviews, 59, 55–73.PubMedCrossRef
Bornkessel-Schlesewsky, I., & Schlesewsky, M. (2009). The role of prominence information in the real-time comprehension of transitive constructions: a cross-linguistic approach. Language and Linguistics Compass, 3(1), 19–58.CrossRef
Bornkessel-Schlesewsky, I., & Schlesewsky, M. (2013a). Neurotypology: Modelling cross-linguistic similarities and differences in the neurocognition of language comprehension. In M. Sanz, I. Laka, M.K. Tanenhaus (Eds.), The cognitive and biological basis for linguistic structure: new approaches and enduring themes, (pp. 241–252). Oxford: Oxford University Press.CrossRef
Bornkessel-Schlesewsky, I., & Schlesewsky, M. (to appear). Competition in argument interpretation: evidence from the neurobiology of language. In B. MacWhinney, A. Malchukov, E. Moravcsik (Eds.), Competing motivations in grammar and usage. Oxford: Oxford University Press.
Bornkessel-Schlesewsky, I., Kretzschmar, F., Tune, S., Wang, L., Genç, S., Philipp, M., Roehm, D., Schlesewsky, M. (2011). Think globally: cross-linguistic variation in electrophysiological activity during sentence comprehension. Brain and Language, 117(3), 133–152.PubMedCrossRef
Bourguignon, N., Drury, J. E., Valois, D., Steinhauer, K. (2012). Decomposing animacy reversals between agents and experiencers: an ERP study. Brain and Language, 122(3), 179–189.PubMedCrossRef
Brouwer, H., Fitz, H., Hoeks, J. (2012). Getting real about semantic illusions: rethinking the functional role of the P600 in language comprehension. Brain Research, 1446, 127–143.PubMedCrossRef
Burnham, K. P., & Anderson, D. R. (2002). Model selection and multi-model inference: a practical information-theoretic approach. Springer.
Choudhary, K. K., Schlesewsky, Bickel, B., Bornkessel-Schlesewsky, I. (2010). An actor-preference in a split-ergative language: electrophysiological evidence from Hindi. In Proceedings from 23rd annual meeting of the cuny conference on human sentence processing. New York City.
Coulson, S., King, J. W., Kutas M (1998). ERPs and domain specificity: beating a straw horse. Language and Cognitive Processes, 13, 653–672.CrossRef
Crocker, M. W. (1994). On the nature of the principle-based sentence processor. In J.C. Clifton, L. Frazier, K. Rayner (Eds.), Perspectives on sentence processing, (pp. 245–266). Hillsdale: Erlbaum.
Croft, W. A. (2001). Radical construction grammar: syntactic theory in typological perspective. Oxford: Oxford University Press.CrossRef
Dahl, Ö. (2008). Animacy and egophoricity: grammar, ontology and phylogeny. Lingua, 118, 141–150.CrossRef
Deco, G., Rolls, E. T., Romo, R. (2009). Stochastic dynamics as a principle of brain function. Progress in Neurobiology, 88(1), 1–16.PubMedCrossRef
Deco, G., Rolls, E. T., Albantakis, L., Romo, R. (2012). Brain mechanisms for perceptual and reward-related decision-making. Progress in Neurobiology.
DeLong, K. A., Urbach, T. P., Kutas, M. (2005). Probabilistic word pre-activation during language comprehension inferred from electrical brain activity. Nature Neuroscience, 8(8), 1117–1121.PubMedCrossRef
Demiral, Ş., Schlesewsky, M., Bornkessel-Schlesewsky, I. (2008). On the universality of language comprehension strategies: evidence from Turkish. Cognition, 106(1), 484–500.PubMedCrossRef
DeWitt, I., & Rauschecker, J. (2012). Phoneme and word recognition in the auditory ventral stream. Proceedings of the National Academy of Sciences, E505–E514.
Fausey, C., & Boroditsky, L. (2011). Who dunnit? Cross-linguistic differences in eye-witness memory. Psychonomic Bulletin and Review, 18, 150–157.PubMedCrossRef
Fausey, C. M., Long, B. L., Inamori, A., Boroditsky, L. (2010). Constructing ageny: the role of language. Frontiers in Psychology, 1(162).
Felleman, D., & Van Essen, D. (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral cortex, 1(1), 1–47.PubMedCrossRef
Fodor, J. A. (1983). Modularity of mind. An essay on faculty psychology. Cambridge: MIT Press.
Frazier, L. (1987). Syntactic processing: evidence from dutch. Natural Language and Linguistic Theory, 5, 519–559. doi:10.1007/BF00138988.CrossRef
Frenzel, S., Schlesewsky, M., Bornkessel-Schlesewsky, I. (2011). Conflicts in language processing: a new perspective on the N400–P600 distinction. Neuropsychologia, 49(3), 574–579.PubMedCrossRef
Friederici, A. D. (2002). Towards a neural basis of auditory sentence processing. Trends in Cognitive Sciences, 6(2), 78–84.PubMedCrossRef
Frisch, S., & Schlesewsky, M. (2001). The N400 reflects problems of thematic hierarchizing. NeuroReport, 12(15), 3391–3394.PubMedCrossRef
Frith, U., & Frith, C. D. (2010). The social brain: allowing humans to boldly go where no other species has been. Philosophical Transactions of the Royal Society B, 365, 165–176.CrossRef
Grewe, T., Bornkessel, I., Zysset, S., Wiese, R., von Cramon, D. Y., Schlesewsky, M. (2006). Linguistic prominence and broca’s area: The influence of animacy as a linearization principle. Neuroimage, 32, 1395–1402.PubMedCrossRef
Haggard, P. (2008). Human volition: towards a neuroscience of will. Nature Reviews Neuroscience, 9, 934–946.PubMedCrossRef
Hagoort, P. (2003). How the brain solves the binding problem for language: a neurocomputational model of syntactic processing. Neuroimage, 20, 18–29.CrossRef
Hagoort, P. (2005). On broca, brain, and binding: a new framework. Trends in Cognitive Sciences, 9(9), 416–422.PubMedCrossRef
Hahne, A., & Friederici, A. D. (2002). Differential task effects on semantic and syntactic processes as revealed by ERPs. Cognitive Brain Research, 13, 339–356.PubMedCrossRef
Haupt, F. S., Schlesewsky, M., Roehm, D., Friederici, A. D., Bornkessel-Schlesewsky, I. (2008). The status of subject–object reanalyses in the language comprehension architecture. Journal of Memory and Language, 59, 54–96.CrossRef
Heekeren, H., Marrett, S., Bandettini, P., Ungerleider, L. (2004). A general mechanism for perceptual decision-making in the human brain. Nature, 431(7010), 859–862.PubMedCrossRef
Huynh, H., & Feldt, L. S. (1970). Conditions under which mean square ratios in repeated measurements designs have exact f-distributions. Journal of the American Statistical Association, 65(332), 1582–1589.CrossRef
Jonides, J., Lewis, R., Nee, D., Lustig, C. (2008). The mind and brain of short-term memory. Annual Review of Psychology, 59, 193–224.PubMedCrossRef
Kempe, V., & MacWhinney, B. (1999). Processing of morphological and semantic cues in Russian and German. Language and Cognitive Processes, 14(2), 129–171.CrossRef
Kim, A., & Osterhout, L. (2005). The independence of combinatory semantic processing: evidence from event-related potentials. Journal of Memory and Language, 52(2), 205–225.CrossRef
Kolk, H. H., Chwilla, D. J., van Herten, M., Oor, P. (2003). Structure and limited capacity in verbal working memory: a study with event-related potentials. Brain and Language, 85, 1–36.PubMedCrossRef
Kretzschmar, F. (2010). The electrophysiological reality of parafoveal processing: on the validity of language-related ERPs in natural reading. PhD thesis, University of Marburg.
Kuperberg, G. R. (2007). Neural mechanisms of language comprehension: challenges to syntax. Brain Research, 1146, 23–49.PubMedCrossRef
Kutas, M., & Federmeier, K. D. (2000). Electrophysiology reveals semantic memory use in language comprehension. Trends in Cognitive Sciences, 4(12), 463–470.PubMedCrossRef
Kutas, M., & Hillyard, S. A. (1980). Reading senseless sentences: brain potentials reflect semantic incongruity. Science, 207(4427), 203–205.PubMedCrossRef
Lau, E. F., Phillips, C., Poeppel, D. (2008). A cortical network for semantics: (de)constructing the N400. Nature Reviews Neuroscience, 9(12), 920–933.PubMedCrossRef
Leslie, A. M. (1995). A theory of agency. In D. Sperber, D. Premack, A.J. Premack (Eds.), Causal cognition. A multidisciplinary debate (pp. 121–141). Oxford: Clarendon Press.
Lewis, R., & Vasishth, S. (2005). An activation-based model of sentence processing as skilled memory retrieval. Cognitive Science: A Multidisciplinary Journal.
Lewis, R., Vasishth, S., Dyke, J. V. (2006). Computational principles of working memory in sentence comprehension. Trends in Cognitive Sciences, 10(10), 447–454.PubMedCentralPubMedCrossRef
Lewis, R. L. (2000). Specifying architectures for language processing: process, control, and memory in parsing and interpretation. In Mechanisms for language processing.
Lotze, N., Tune, S., Schlesewsky, M., Bornkessel-Schlesewsky, I. (2011). Meaningful physical changes mediate lexical-semantic integration: top-down and form-based bottom-up information sources interact in the N400. Neuropsychologia, 49, 3573–3582.PubMedCrossRef
MacWhinney, B., & Bates, E. (1989). The cross-linguistic study of sentence processing. New York: Cambridge University Press.
MacWhinney, B., Bates, E., Kliegl, R. (1984). Cue validity and sentence intERPretation in english, German and Italian. Journal of Verbal Learning and Verbal Behavior, 23(2), 127–50.CrossRef
Magnusdottir, S., Fillmore, P., den Ouden, D., Hjaltason, H., Rorden, C., Kjartansson, O., Bonilha, L., Fridriksson, J. (2012). Damage to left anterior temporal cortex predicts impairment of complex syntactic processing: a lesion-symptom mapping study. Human Brain Mapping.
Manning, C. D., & Schütze, H. (2000). Foundations of statistical natural language processing. Cambridge: MIT Press.
Maris, E. (2004). Randomization tests for ERP topographies and whole spatiotemporal data matrices. Psychophysiology, 41, 142–151.PubMedCrossRef
Maris, E., & Oostenveld, R. (2007). Nonparametric statistical testing of EEG- and MEG-data. Journal of Neuroscience Methods, 164, 177–190.PubMedCrossRef
Marslen-Wilson, W. (1973). Linguistic structure and speech shadowing at very short latencies. Nature, 244, 522–533.PubMedCrossRef
Martin, A. E., & McElree, B. (2008). A content-addressable pointer mechanism underlies comprehension of verb-phrase ellipsis. Journal of Memory and Language, 58, 879–906.CrossRef
McElree, B. (2006). Accessing recent events. In B.H. Ross (Ed.), The psychology of learning and motivation (Vol. 46). San Diego: Academic Press.
McElree, B., Foraker, S., Dyer, L. (2003). Memory structures that subserve sentence comprehension. Journal of Memory and Language, 48(1), 67–91.CrossRef
Muralikrishnan, R., Schlesewsky, M., Bornkessel-Schlesewsky, I. (2008). Universal and cross-linguistic influences on the processing of word order and animacy: Neurophysiological evidence from Tamil. In Proceedings from 21st annual CUNY conference on human sentence processing, Chapel Hill.
New, J., Cosmides, L., Tooby, J. (2007). Category-specific attention for animals reflects ancestral priorities, not expertise. Proceedings of the National Academy of Sciences, 104(42), 16,598–16,603.CrossRef
Newell, A. (1990). Unified theories of cognition. Cambridge: Harvard University Press.
Nieuwenhuis, S., Aston-Jones, G., Cohen, J. D. (2005). Decision making, the p3, and the locus coerulus-norepinephrine system. Psychological Bulletin, 131, 510–532.PubMedCrossRef
Oldfield, R. C. (1971). The assessment and analysis of handedness: The edinburgh inventory. Neuropsychologia, 9, 97–113.PubMedCrossRef
Penolazzi, B., Vincenzi, M. D., Angrilli, A., Job, R. (2005). Processing of temporary syntactic ambiguity in Italian “who”-questions: a study with event-related potentials. Neuroscience Letters, 377(2), 91–96.PubMedCrossRef
Philipp, M., Bornkessel-Schlesewsky, I., Bisang, W., Schlesewsky, M. (2008). The role of animacy in the real time comprehension of Mandarin Chinese: evidence from auditory event-related brain potentials. Brain and Language, 105(2), 112–133.PubMedCrossRef
Rauschecker, J. (1998). Cortical processing of complex sounds. Current Opinion in Neurobiology, 8(4), 516–521.PubMedCrossRef
Rauschecker, J., & Scott, S. (2009). Maps and streams in the auditory cortex: nonhuman primates illuminate human speech processing. Nat Neurosci, 12(6), 718–724.PubMedCentralPubMedCrossRef
Roehm, D., Schlesewsky, M., Bornkessel, I., Frisch, S., Haider, H. (2004). Fractionating language comprehension via frequency characteristics of the human EEG. NeuroReport, 15(3), 409–412.PubMedCrossRef
Roehm, D., Bornkessel-Schlesewsky, I., Rösler, F., Schlesewsky, M. (2007). To predict or not to predict: influences of task and strategy on the processing of semantic relations. Journal of Cognitive Neuroscience, 19, 1259–1274.PubMedCrossRef
Sassenhagen, J., Kretzschmar, F., Mueller, E., Schlesewsky, M., Bornkessel-Schlesewsky, I. (2013). Independent components dominating ERP responses to linguistic stimuli also respond to domain-general events. Manuscript submitted for publication.
Schlesewsky, M., & Bornkessel, I. (2004). On incremental interpretation: degrees of meaning accessed during sentence comprehension. Lingua, 114(9–10), 1213–1234.CrossRef
Schlesewsky, M., Fanselow, G., Kliegl, R., Krems, J. (2000). The subject preference in the processing of locally ambiguous wh-questions in German. In B. Hemforth, & L. Konieczny (Eds.), German sentence processing (pp. 65–93). Dordrecht: Kluwer.CrossRef
Schriefers, H., Friederici, A. D., Kuhn, K. (1995). The processing of locally ambiguous relative clauses in German. Journal of Memory and Language, 34(4), 499–520.CrossRef
Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461–464.CrossRef
Silverstein, M. (1976). Hierarchy of features and ergativity. In R.M. Dixon (Ed.), Grammatical categories in Australian languages (pp. 112–171). New Jersey: Humanities Press.
Simon, H. A. (1962). The architecture of complexity. Proceedings of the American Philosophical Society, 106, 467–482.
Stevens, S. S. (1951). Mathematics, measurement and psychophysics. In S.S. Stevens (Ed.) Handbook of experimental psychology (pp. 1–49). Wiley .
Stroud, C., & Phillips, C. (2012). Examining the evidence for an independent semantic analyzer: an ERP study in spanish. Brain and Language, 120, 108–126.PubMedCrossRef
Tomasello, M. (2003). Constructing a language: a usage-based theory of language acquisition. Cambridge: Harvard University Press.
Tomlin, R. (1986). Basic word order: Functional principles. London: Croom Helm.
van de Meerendonk, N., Kolk, H. H., Chwilla, D. J., Vissers, C.T.W.M. (2009). Monitoring in language perception. Language and Linguistics Compass, 3, 1211–1224.CrossRef
van de Meerendonk, N., Kolk, H. H., Vissers, C.T.W.M., Chwilla, D. J. (2010). Monitoring in language perception: mild and strong conflicts elicit different ERP patterns. Journal of Cognitive Neuroscience, 22, 67–82.PubMedCrossRef
Vincenzi, M. D. (1991). Filler-gap dependencies in a null-subject language: referential and non-referential whs. Journal of Psycholinguistic Research, 20(3), 197–213.CrossRef
Vissers, C.T.W.M., Chwilla, D., Kolk, H. (2006). Monitoring in language perception: The effect of misspellings of words in highly constrained sentences. Brain Research, 1106, 150–163.PubMedCrossRef
Vosse, T., & Kempen, G. (2000). Syntactic structure assembly in human parsing: a computational model based on competitive inhibition and a lexicalist grammar. Cognition, 75, 105–143.PubMedCrossRef
Vosse, T. G., & Kempen, G. AM. (2008). Parsing verb-final clauses in German: garden-path and ERP effects modeled by a parallel dynamic parser. In B. Love, K. McRae, V. Sloutsky (Eds.), Proceedings of the 30th annual conference of the cognitive science society (Washington, DC, July 2008). Austin: Cognitive Science Society.
Vosse, T., & Kempen, G. (2009). The unification space implemented as a localist neural net: predictions and error-tolerance in a constraint-based parser. Cognitive Neurodynamics, 3, 331–346.PubMedCentralPubMedCrossRef
Wang, L., Schlesewsky, M., Bickel, B., Bornkessel-Schlesewsky, I. (2009). Exploring the nature of the ‘subject’-preference: evidence from the online comprehension of simple sentences in Mandarin Chinese. Language and Cognitive Processes, 24(7/8), 1180–1226.CrossRef
Weckerly, J., & Kutas, M. (1999). An electrophysiological analysis of animacy effects in the processing of object relative sentences. Psychophysiology, 36(05), 559–570.PubMedCrossRef
Whitney, C., Huber, W., Klann, J., Weis, S., Krach, S., Kircher, T. (2009). Neural correlates of narrative shifts during auditory story comprehension. NeuroImage, 47, 360–366.PubMedCrossRef
Wolff, S., Schlesewsky, M., Hirotani, M., Bornkessel-Schlesewsky, I. (2008). The neural mechanisms of word order processing revisited: Electrophysiological evidence from Japanese. Brain and Language, 107, 133–157.PubMedCrossRef
Zipf, G. K. (1935). The psycho-biology of language. Boston: Houghton Mifflin Company.
Zipf, G. K. (1949). Human behavior and the principle of least effort. Cambridge: Addison-Wesley.
Metadata
Title
Towards a Computational Model of Actor-Based Language Comprehension
Authors
Phillip M. Alday
Matthias Schlesewsky
Ina Bornkessel-Schlesewsky
Publication date
01-01-2014
Publisher
Springer US
Published in
Neuroinformatics / Issue 1/2014
Print ISSN: 1539-2791
Electronic ISSN: 1559-0089
DOI
https://doi.org/10.1007/s12021-013-9198-x

Other articles of this Issue 1/2014

Neuroinformatics 1/2014 Go to the issue

Original Article

Template Construction Grammar: From Visual Scene Description to Language Comprehension and Agrammatism

Original Article

A Neuroinformatics of Brain Modeling and its Implementation in the Brain Operation Database BODB

Editorial

Action, Language and Neuroinformatics: An Introduction

Original Article

Action and Language Mechanisms in the Brain: Data, Models and Neuroinformatics

Original Article

Prolegomena to a Neurocomputational Architecture for Human Grammatical Encoding and Decoding

Original Article

Ontogenetic Ritualization of Primate Gesture as a Case Study in Dyadic Brain Modeling

Premium Partner

    Image Credits