The PhD candidate will work on a project on the development and application of novel statistical techniques to analyze psycholinguistic data, including, among others, reaction times and reading times. The project will combine ideas, methods, and data from psycholinguistics, statistics, and computational linguistics.

Project description:
Psycholinguistic studies attempt to infer information about the mechanisms that drive language processing through behavioral measures in experimental tasks. Frequently, these behavioral measures are temporal in nature: how long before participants press a button, how long before they read a word, how long they look at a word during reading, how long before a word shifts meaning in language change, and many others. 

Traditionally, analyses of temporal response variables in psycholinguistic studies have focused on effects of predictors on the mean of the response variable distribution. The effects of predictors, however, need not be constant over time: some may arise early, while others may appear later. In addition, the qualitative nature of predictor effects may change over time. The aim of the research that you will be conducting is to (further) explore the potential of Piecewise Additive Mixed Models (PAMMs), a state-of-the-art statistical technique that offers insight into the temporal dynamics of predictor effects, even when the response variable consists of a single measurement. 

Recently, a few studies explored the application of PAMMs to linguistic data (see e.g., Hendrix & Sun, 2021; Hendrix & Sun, 2020). Hendrix & Sun (2021) carried out a PAMM analysis of visual lexical decision data. They observed an early inhibitory effect of word length followed by a later facilitatory effect, which would have canceled each other in a traditional response time analysis. The time-dependent effect of word length suggests that at least two distinct cognitive mechanisms underlie the effect of word length in the visual lexical decision task. As such, a PAMM analysis revealed a novel insight into one of the most enigmatic effects in the psycholinguistic literature that could not have been obtained through a traditional analysis of the mean. 

There are several available opportunities for uncovering hitherto hidden information from linguistic data using PAMMs. The project aims to:

1. apply PAMMs to linguistic data sets to further explore their potential for psycholinguistic research, studying isolated word processing, sentence processing, and language change using existing publicly released large-scale sets. 
2. equip psycholinguistic researchers with the tools necessary for independently conducting PAMM analyses of experimental data. 

The novel insights gained through the project will help develop theories and computational models of language processing.

• Master’s degree related to psychology, statistics, computational linguistics, computational cognitive science, artificial intelligence, or a related field. If you expect to obtain your degree before September 2024, you are also eligible.
• Knowledge of Python or R (and willingness to learn the other)
• Some familiarity with at least one of the following: 
  o    linear mixed-effect models
  o    analysis of psycholinguistic data, e.g., (primed) lexical decision tasks, reading times, age of acquisition data, etc…
  o    corpus studies
  o    word embeddings or language models
• Knowledge of generalized additive (mixed) models is a plus.
• Excellent knowledge of English and good academic writing skills. 

What we offer

• A full-time position.
• The selected candidate will start with a contract for one year. Upon a positive outcome of the first-year evaluation, the candidate will be offered an employment contract for the remaining three years.
• A minimum gross salary of € 2.541,- per month up to a maximum of € 3.247,- in the fourth year.
• A holiday allowance of 8% and an end-of-year bonus of 8.3% (annually).
• Researchers from outside the Netherlands may qualify for a tax-free allowance equal to 30% of their taxable salary (the 30% tax regulation). The University will apply for such an allowance on their behalf.
• Assistance in finding accommodation (for foreign employees).
• Benefits such as an options model for terms and conditions of employment and reimbursement of moving expenses, also including excellent technical infrastructure, savings schemes and excellent sport facilities.

The collective labor agreement of the Dutch Universities applies.

Tilburg School of Humanities and Digital Sciences

Research and education at the Tilburg School of Humanities and Digital Sciences (TSHD) has a unique focus on humans in the context of the globalizing digital society, on the development of artificial intelligence and interactive technologies, on their impact on communication, culture and society, and on moral and existential challenges that arise. The School of Humanities and Digital Sciences consists of four departments: Communication and Cognition, Cognitive Science and Artificial Intelligence, Culture Studies and Philosophy; several research institutes and a faculty office. Also the University College Tilburg is part of the School. Each year around 275 students commence a Bachelor or (Pre) Master Program. The School has approximately 2000 students and 250 employees.

Tilburg School of Humanities and Digital Sciences

Department of Cognitive Science and Artificial Intelligence

The Cognitive Science and Artificial Intelligence department (https://csai.nl/) at Tilburg University is part of the Tilburg School of Humanities and Digital Sciences.
The department emphasizes innovative research spanning a wide array of fields including computational linguistics and language technology, data-science, human-robot interaction, virtual reality and serious games. CSAI is a member of the Benelux Association for Artificial Intelligence, participates in the Special Interest Group for AI (SIG AI), and also participates in the Confederation of Laboratories for Artificial Intelligence Research in Europe (CLAIRE).
Tilburg University and the CSAI department offer a vibrant international environment, with current CSAI staff originating from more than 10 countries.