Himanshu Yadav, PhD

Today Himanshu defended his dissertation. His dissertation consists of three published papers.

1. Himanshu Yadav, Garrett Smith, Sebastian Reich, and Shravan Vasishth. Number feature distortion modulates cue-based retrieval in reading. Journal of Memory and Language, 129, 2023.

2. Himanshu Yadav, Dario Paape, Garrett Smith, Brian W. Dillon, and Shravan Vasishth. Individual differences in cue weighting in sentence comprehension: An evaluation using Approximate Bayesian Computation. Open Mind, 2022

3. Himanshu Yadav, Garrett Smith, Daniela Mertzen, Ralf Engbert, and Shravan Vasishth Proceedings of the Annual Meeting of the Cognitive Science Society, 44, 2022.

Congratulations to Himanshu for his truly outstanding work!

Summer School “Methods in Language Sciences” (16-20 August 2022, Ghent, Belgium): Registrations open

I was asked to advertise this summer school (I will be teaching a 2.5 day course on linear mixed modeling, and will give a keynote lecture on the use of Bayesian methods in linguistics/psychology). The text below is from the organizers.

Summer School “Methods in Language Sciences” 2022:

Registrations are open

Top quality research requires outstanding methodological skills. That is why the Department

of Linguistics and the Department of Translation, Interpreting and Communication of Ghent

University will jointly organize the (second edition of the) Summer School “Methods in

Language Sciences” on 16-20 August 2022.

This Summer School is targeted at both junior and senior researchers and offers nine multi-

day modules on various topics, ranging from quantitative to qualitative methods and

covering introductory and advanced statistical analysis, Natural Language Processing

(NLP), eye-tracking, survey design, ethnographic methods, as well as specific tools such

as PRAAT and ELAN. In 2022 we have a new module on Linear Mixed Models. All lecturers

are internationally recognized experts with a strong research and teaching background.

Because the modules will partly be held in parallel sessions, participants have to choose one

or two modules to follow (see the  Programme  for details). No prerequisite knowledge or

experience is required, except for Modules 2 and 9, which deal with advanced statistical data

analysis.

We are proud to welcome two keynote speakers at this year’s summer school: Shravan

Vasishth and Crispin Thurlow, who both also act as lecturers.

This is your opportunity to take your methodological skills for research in (applied)

linguistics, translation or interpreting studies to the next level. We are looking forward to

meeting you in Ghent!

New opinion paper in Trends in Cognitive Sciences: Data Assimilation in Dynamical Cognitive Science (Engbert et al.)

 Here's a new opinion paper in Trends in Cognitive Sciences, by Ralf Engbert, Max Rabe, et al. 

Link to paper: here

New paper accepted in MIT Press Journal Open Mind: Individual differences in cue weighting in sentence comprehension: An evaluation using Approximate Bayesian Computation

My PhD student Himanshu Yadav has just had an important paper on modeling individual differences provisionally accepted in the open access journal Open Mind. One reason that this paper is important is that it demonstrates why it is crucial to understand systematic individual-level behavior in the data, and what this observed data implies for computational models of sentence processing. As Blastland and Spiegelhalter put it, "The average is an abstraction. The reality is variation." Our focus should be on understanding and explaining the variation, not just average behavior. More exciting papers on this topic are coming soon from Himanshu!


The reviews from Open Mind were very high quality, certainly as high or higher quality than I have received from many top closed-access journals over the last 20 years. The journal has a top-notch editorial board, led by none other than Ted Gibson. This is our second paper in Open Mind; the first was this one. I plan to publish more of our papers in this journal (along with the other open access journal, Glossa Psycholinguistics, also led by a stellar set of editors, Fernanda Ferreira and Brian Dillon). I hope that these open access journals can become the norm for our field. I wonder what it will take for that to happen.


Himanshu Yadav, Dario Paape, Garrett Smith, Brian W. Dillon, and Shravan Vasishth. Individual differences in cue weighting in sentence comprehension: An evaluation using Approximate Bayesian Computation. Open Mind, 2021. Provisionally accepted.


The pdf is here.

Book: Sentence comprehension as a cognitive process: A computational approach (Vasishth and Engelmann)

 

My book with Felix Engelmann has just been published. It puts together in one place 20 years of research on retrieval models, carried out by my students, colleagues, and myself.

New Psych Review paper by Max Rabe et al: A Bayesian approach to dynamical modeling of eye-movement control in reading of normal, mirrored, and scrambled texts

 An important new paper by Max Rabe, a PhD student in the psychology department at Potsdam:

Open access pdf download: https://psyarxiv.com/nw2pb/

Reproducible code and data: https://osf.io/t9sbf/ 

Title: A Bayesian approach to dynamical modeling of eye-movement control in reading of normal, mirrored, and scrambled texts

Abstract: In eye-movement control during reading, advanced process-oriented models have been developed to reproduce behavioral data. So far, model complexity and large numbers of model parameters prevented rigorous statistical inference and modeling of interindividual differences. Here we propose a Bayesian approach to both problems for one representative computational model of sentence reading (SWIFT; Engbert et al., Psychological Review, 112, 2005, pp. 777–813). We used experimental data from 36 subjects who read the text in a normal and one of four manipulated text layouts (e.g., mirrored and scrambled letters). The SWIFT model was fitted to subjects and experimental conditions individually to investigate between-subject variability. Based on posterior distributions of model parameters, fixation probabilities and durations are reliably recovered from simulated data and reproduced for withheld empirical data, at both the experimental condition and subject levels. A subsequent statistical analysis of model parameters across reading conditions generates model-driven explanations for observable effects between conditions.