Information on each of the tenure tracks can be found below.<\/p>\n\n\n\n
\n\n\n\n
Tenure Track 1<\/strong><\/h2>\n\n\n\nComputational psycholinguistics of sentence processing<\/strong><\/h4>\n\n\n\nCentre for Language Studies, Radboud University
<\/strong>Starting date: August 01, 2015
PhD:\u00a0Chara Tsoukala\u00a0<\/strong>(graduated, defense date: April 21, 2021)
<\/p>\n\n\n\nResearcher<\/h5>\n\n\n\n\n\n\n![\"\"](\"https:\/\/www.dcc.ru.nl\/languageininteraction\/wp-content\/uploads\/2021\/08\/StefanFrank_small.png\")
<\/figure>\n<\/div>\n\n\n\n\nDr. Stefan Frank<\/strong>
Coordinator BQ1
Tenure track researcher
Profile page<\/a><\/p>\n<\/div>\n<\/div>\n\n\n\n<\/p>\n\n\n\n\n
Information on Tenure Track 1<\/h4>\n\n\n\n
As details of the cognitive processes and representations underlying language use continue to be uncovered, and ever increasing amounts of behavioural and (neuro)physiological data are collected, it becomes more and more difficult to capture the immense complexity of human language processing in theories that are specified only verbally. In contrast to verbal description, implemented computational models that simulate aspects of processing are able to generate fine-grained, quantitative predictions and can thereby expose how, exactly, observed properties of language comprehension and production may emerge.<\/p>\n\n\n\n
The general aim of this tenure track was the development and application of computational models of human sentence processing, bridging between linguistic and cognitive theory, psychological experimentation, and neuroimaging data; particularly in the context of multilingualism. The basic assumption behind this work is that the mind is for a large part a statistical system: It extracts (linguistic) patterns from observations and applies abstractions over these patterns when processing novel input. Any model of such a system embodies particular assumptions about the relevant processes and representations. The cognitively most plausible assumptions can then be identified by comparing how well different models\u2019 predictions fit human processing data. Thus, statistical models of language are developed, implemented, and trained on linguistic data; and their quantitative predictions of behavioural and\/or neural responses serve to evaluate to the models\u2019 value as cognitive theories. In addition, we ran human language comprehension experiments to test specific (model) predictions of language use as rooted in the application of language statistics.<\/p>\n\n\n\n
\n\n\n\nTenure Track 2 <\/a><\/strong><\/h2>\n\n\n\nHierarchical structure in natural language: Bridging computational linguistics, neurobiology and formal semantics<\/strong><\/h4>\n\n\n\nInstitute for Logic, Language, and Computation, University of Amsterdam
Starting date: September 01, 2015
PhD:\u00a0Dieuwke Hupkes\u00a0<\/strong>(graduated, defense date: June 17, 2020)<\/p>\n\n\n\nResearcher<\/h5>\n\n\n\n\n\n\n![\"Jelle](\"https:\/\/staff.fnwi.uva.nl\/w.zuidema\/wp-content\/uploads\/jelle2015-300x300.jpg\")
<\/figure>\n<\/div>\n\n\n\n\nDr. Jelle Zuidema<\/strong>
Tenure track researcher
Profile page<\/a><\/p>\n<\/div>\n<\/div>\n\n\n\n<\/p>\n\n\n\n\n
Information on Tenure Track 2<\/h4>\n\n\n\n
A unique feature of human language \u2013 and the key property that arguably sets it apart in nature \u2013 is its hierarchical compositionality: all languages allow meaningful atomic units (e.g. words) to be combined into larger units, and these larger units to be further combined to form meaningful sentences. The work performed within this tenure track investigates (1) how corpus data and computational models can be used to study the nature of this combinatory system, (2) how such a system may be implemented in a neural architecture, (3) how the system may be learned by children and machines, and ultimately, (4) how such a system emerged in evolution.<\/p>\n\n\n\n
\n\n\n\nTenure Track 3 <\/a><\/strong><\/h2>\n\n\n\nNeuropsychology of Language and Language Disorders<\/strong><\/h4>\n\n\n\nDonders Centre for Cognition and Donders Centre for Neuroscience, Radboud University
Starting date: June 01, 2016
PhD: Ileana Camerino<\/strong> (ongoing)<\/p>\n\n\n\nResearcher<\/h5>\n\n\n\n\n\n\n![\"Avatar\"](\"https:\/\/vitoriapiai.science\/author\/vitoria-piai\/avatar_hu5245552313492867de921fbbd058d92c_42497_270x270_fill_q90_lanczos_center.jpg\")
<\/figure>\n<\/div>\n\n\n\n
Centre for Language Studies, Radboud University Dr. Stefan Frank<\/strong> <\/p>\n\n\n\n\n As details of the cognitive processes and representations underlying language use continue to be uncovered, and ever increasing amounts of behavioural and (neuro)physiological data are collected, it becomes more and more difficult to capture the immense complexity of human language processing in theories that are specified only verbally. In contrast to verbal description, implemented computational models that simulate aspects of processing are able to generate fine-grained, quantitative predictions and can thereby expose how, exactly, observed properties of language comprehension and production may emerge.<\/p>\n\n\n\n The general aim of this tenure track was the development and application of computational models of human sentence processing, bridging between linguistic and cognitive theory, psychological experimentation, and neuroimaging data; particularly in the context of multilingualism. The basic assumption behind this work is that the mind is for a large part a statistical system: It extracts (linguistic) patterns from observations and applies abstractions over these patterns when processing novel input. Any model of such a system embodies particular assumptions about the relevant processes and representations. The cognitively most plausible assumptions can then be identified by comparing how well different models\u2019 predictions fit human processing data. Thus, statistical models of language are developed, implemented, and trained on linguistic data; and their quantitative predictions of behavioural and\/or neural responses serve to evaluate to the models\u2019 value as cognitive theories. In addition, we ran human language comprehension experiments to test specific (model) predictions of language use as rooted in the application of language statistics.<\/p>\n\n\n\n Institute for Logic, Language, and Computation, University of Amsterdam Dr. Jelle Zuidema<\/strong> <\/p>\n\n\n\n\n A unique feature of human language \u2013 and the key property that arguably sets it apart in nature \u2013 is its hierarchical compositionality: all languages allow meaningful atomic units (e.g. words) to be combined into larger units, and these larger units to be further combined to form meaningful sentences. The work performed within this tenure track investigates (1) how corpus data and computational models can be used to study the nature of this combinatory system, (2) how such a system may be implemented in a neural architecture, (3) how the system may be learned by children and machines, and ultimately, (4) how such a system emerged in evolution.<\/p>\n\n\n\n Donders Centre for Cognition and Donders Centre for Neuroscience, Radboud University
<\/strong>Starting date: August 01, 2015
PhD:\u00a0Chara Tsoukala\u00a0<\/strong>(graduated, defense date: April 21, 2021)
<\/p>\n\n\n\nResearcher<\/h5>\n\n\n\n\n
<\/figure>\n<\/div>\n\n\n\n
Coordinator BQ1
Tenure track researcher
Profile page<\/a><\/p>\n<\/div>\n<\/div>\n\n\n\nInformation on Tenure Track 1<\/h4>\n\n\n\n
\n\n\n\nTenure Track 2 <\/a><\/strong><\/h2>\n\n\n\n
Hierarchical structure in natural language: Bridging computational linguistics, neurobiology and formal semantics<\/strong><\/h4>\n\n\n\n
Starting date: September 01, 2015
PhD:\u00a0Dieuwke Hupkes\u00a0<\/strong>(graduated, defense date: June 17, 2020)<\/p>\n\n\n\nResearcher<\/h5>\n\n\n\n\n
<\/figure>\n<\/div>\n\n\n\n
Tenure track researcher
Profile page<\/a><\/p>\n<\/div>\n<\/div>\n\n\n\nInformation on Tenure Track 2<\/h4>\n\n\n\n
\n\n\n\nTenure Track 3 <\/a><\/strong><\/h2>\n\n\n\n
Neuropsychology of Language and Language Disorders<\/strong><\/h4>\n\n\n\n
Starting date: June 01, 2016
PhD: Ileana Camerino<\/strong> (ongoing)<\/p>\n\n\n\nResearcher<\/h5>\n\n\n\n\n
<\/figure>\n<\/div>\n\n\n\n