I am looking for a PhD student in Psycholinguistics (sentence processing). The candidate will have to apply for the Ph.D. program in the Department of Cognitive Science at IIT Kanpur. The selected candidate will be eligible to work with me in the Language Processing Lab at Kanpur on research projects involving sentence processing, individual differences, cognitive modeling, and language evolution.
Who can apply?
Candidates with a Master's in Linguistics or Cognitive Science who have cleared the GATE/NET/COGJET exam can apply! The preference will be given to the ones who have prior training in either experimental work or computational modeling. If you are interested in studying the human mind through observing language behavior, I will encourage you to consider this opportunity.
How to apply?
This will be an institute-funded PhD position. You will have to apply for the Ph.D. program in the Department of Cognitive Science at IIT Kanpur. The application deadline will be announced soon. See the institute's PG admission portal for details guidelines on how to apply: https://www.iitk.ac.in/doaa/pgadmission/
The admission process will consist of a written examination followed by an interview. The written exam will test applicants for research aptitude. The requirements and application procedure are available on this page: https://www.cgs.iitk.ac.in/PhD_Program.php. Drop me an email to inquire more about the research projects in the lab and the application procedure.
About me and our lab:
My name is Himanshu Yadav. I am an Assistant Professor in the Department of Cognitive Science at the Indian Institute of Technology Kanpur, India. I lead the Kanpur Language Processing (KaLP) lab.
At KaLP lab, we study cognitive processes that underlie language comprehension and production in humans. We are interested in two broad questions: (1) How is language processed in the mind, and how does it interact with other cognitive processes such as working memory and attention? (2) How does language as a complex adaptive system evolve in its quantitative distribution of structural preferences? We use computational modeling, psycholinguistic experimentation, and crosslinguistic corpora analysis to investigate these questions. Here is a list of ongoing research projects in our lab: https://sites.google.com/view/language-processing-kanpur/projects
Potential PhD thesis projects:
Here is a list of potential PhD projects in our lab. For a detailed description of all the projects, see the project page: https://sites.google.com/view/language-processing-kanpur/projects.
How do prediction and memory constraints interact at the individual level? Working memory constraints are argued be to central to sentence comprehension, but there is little evidence for working memory effects in the verb-final languages. Instead, a predictive processing strategy is considered as the key factor in these languages. Individual difference modeling approach can be instrumental in understanding how working memory constraints and prediction interact to generate the observed processing behavior (locality effect) in verb-final language speakers.
Methods: Self-paced reading and eye-tracking experiments, Bayesian modeling
Status: Ongoing
Publications: ICCM 2024
The role of cue weighting in sentence comprehension. Due to their varying language exposure and learning strategies at an early age, individuals differ in which linguistic cues they rely on during real-time sentence comprehension. For instance, some individuals may weigh syntactic cues higher over the semantic and word order cues. The cue-based retrieval theory and the cue competition theory predict that an individual's cue weighting strategy can considerably affect their behavior on a sentence processing task. This study aims to provide a more conservative test for the hypothesis that the comprehender distributes a fixed, limited resource to different types of linguistic cues to optimize the comprehension process.
Methods: Self-paced reading and eye-tracking experiments, Bayesian modeling
Status: Ongoing
Publications: JML 2023, CogSci 2022
The distortion-preactivation-retrieval theory of sentence comprehension. We are working on a new theory of sentence processing that makes three simple assumptions: (i) When linguistic input is temporarily stored in memory, it distorts to a non-veridical representation probabilistically, (ii) the (probabilistically) distorted input in memory preactivates certain linguistic items based on their history of co-occurrences, and (iii) dependency completion is driven by a content-addressable search in memory, which operates on potentially distorted input and might be facilitated by preactivation. The assumptions are well-motivated and have found independent empirical support in sentence processing data. This project aims to develop a fully computational model based on these assumptions and test it against competing models using reading times data.
Methods: Computational modeling, cross-validation, reading experiments
Status: Not started
Working memory constraints and predictability maximization as opposite pressures to explain the variability in structural preferences across languages. Under some theoretical assumptions, the constraints on working memory and predictability considerations predict the opposite about the placement of constituents in a phrase/sentence. We pursue a theory of language evolution that assumes that a language fluctuates between the working memory pressure and predictability maximization pressure over time. Do all human languages lie on a continuum between absolute working memory minimization and absolute predictability maximization?
Methods: large-scale corpora analysis, random graph generation algorithms
Status: Ongoing
Publications: Open Mind 2022
A unified theory of sentence processing in individuals with Aphasia and in controls. We plan to study the individuals with Aphasia (IWAs) on sentence processing tasks in an Indian language. The goal is to implement and test a model that can potentially explain why IWAs differ from controls and from each other in their processing behavior.
Methods: Eye-tracking experiments, Bayesian modeling
Status: Not started
Contact
Himanshu Yadav
Department of Cognitive Science
507, ESB II
Indian Institute of Technology Kanpur
Homepage: https://sites.google.com/site/himanshuyadavjnu/
Lab page: https://sites.google.com/view/language-processing-kanpur/