Video recording of talk at Tuebingen: Individual differences in sentence processing

Here is the video recording of my talk from Feb 22, 2021:

Talk in Tuebingen: Individual differences in cue-weighting in sentence comprehension: An evaluation using Approximate Bayesian Computation

When: Feb 22 2021
Where: Universität Tübingen, Seminar für Sprachwissenschaft
How: Zoom

[This is part of the PhD work of Himanshu Yadav, and the project is led by him. Co-authors: Dario Paape, Garrett Smith, and Brian Dillon.]

Abstract
Cue-based retrieval theories of sentence processing assume that syntactic dependencies are resolved through a content-addressable search process. An important recent claim is that in certain dependency types, the retrieval cues are weighted such that one cue dominates. This cue-weighting proposal aims to explain the observed average behavior. We show that there is systematic individual-level variation in cue weighting. Using the Lewis and Vasishth cue-based retrieval model, we estimated individual-level parameters for processing speed and cue weighting using data from 13 published reading studies; hierarchical Approximate Bayesian Computation (ABC) with Gibbs sampling was used to estimate the parameters. The modeling reveals a nuanced picture about cue-weighting: we find support for the idea that some participants weight cues, but not all do; and only fast readers tend to have the predicted cue weighting, suggesting that reading proficiency might be associated with cue weighting. A broader achievement of the work is to demonstrate how individual differences can be investigated in computational models of sentence processing using hierarchical ABC.

Bayesian statistics: A tutorial taught at Experimental Methods for Language Acquisition research (EMLAR XVII 2021)

Bayesian statistics Taught by Shravan Vasishth (vasishth.github.io) When: Sometime between 13 and 15 April 2021 Where: https://emlar.wp.hum.uu.nl/tutorial/bayesian-statistics/ Bayesian methods are increasingly becoming mainstream in psychology and psycholinguistics. However, finding an entry point into using these methods is often difficult for researchers. In this tutorial, I will provide an informal introduction to the fundamental ideas behind Bayesian statistics, using examples illustrating applications to psycholinguistics. I will also illustrate some of the advantages of the Bayesian approach over the standardly used frequentist paradigms: uncertainty quantification, robust estimates, the ability to incorporate expert and/or prior knowledge into the data analysis, and the ability to flexibly define the generative process and thereby to directly address the actual research question (as opposed to a straw-man null hypothesis). Suggestions for further readings will be provided. References Bruno Nicenboim, Daniel Schad, and Shravan Vasishth. Introduction to Bayesian Data Analysis for Cognitive Science. 2021. Under contract with Chapman and Hall/CRC Statistics in the Social and Behavioral Sciences Series. https://vasishth.github.io/bayescogsci/ Daniel J. Schad, Michael Betancourt, and Shravan Vasishth. Towards a principled Bayesian workflow: A tutorial for cognitive science. Psychological Methods, 2020. In Press. https://osf.io/b2vx9/ Shravan Vasishth, Daniela Mertzen, Lena A. Jäger, and Andrew Gelman. The statistical significance filter leads to overoptimistic expectations of replicability. Journal of Memory and Language, 103:151-175, 2018. https://www.sciencedirect.com/science/article/pii/S0749596X18300640?via%3Dihub Shravan Vasishth, Bruno Nicenboim, Mary E. Beckman, Fangfang Li, and Eun Jong Kong. Bayesian data analysis in the phonetic sciences: A tutorial introduction. Journal of Phonetics, 71:141-161, 2018. https://osf.io/g4zpv/ Bruno Nicenboim and Shravan Vasishth. Statistical methods for linguistic research: Foundational Ideas - Part II. Language and Linguistics Compass, 10:591-613, 2016. https://onlinelibrary.wiley.com/doi/abs/10.1111/lnc3.12207