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. 

Two important new papers from my lab on lossy compression, encoding, and retrieval interference

My student Himanshu Yadav is on a roll; he has written two very interesting papers investigating alternative models of similarity-based interference. 

 The first one will appear in the Cognitive Science proceedings: 

Download: https://psyarxiv.com/76aex/

 Title: Feature encoding modulates cue-based retrieval: Modeling interference effects in both grammatical and ungrammatical sentences

Abstract: Studies on similarity-based interference in subject-verb number agreement dependencies have found a consistent facilitatory effect in ungrammatical sentences but no conclusive effect in grammatical sentences. Existing models propose that interference is caused either by a faulty representation of the input (encoding-based models) or by difficulty in retrieving the subject based on cues at the verb (retrieval-based models). Neither class of model captures the observed patterns in human reading time data. We propose a new model that integrates a feature encoding mechanism into an existing cue-based retrieval model. Our model outperforms the cue-based retrieval model in explaining interference effect data from both grammatical and ungrammatical sentences. These modeling results yield a new insight into sentence processing, encoding modulates retrieval. Nouns stored in memory undergo feature distortion, which in turn affects how retrieval unfolds during dependency completion.

The second paper will appear in the International Conference on Cognitive Modeling (ICCM) proceedings:

Download: https://psyarxiv.com/3et95/

Title: Is similarity-based interference caused by lossy compression or cue-based retrieval? A computational evaluation

Abstract: The similarity-based interference paradigm has been widely used to investigate the factors subserving subject-verb agreement processing. A consistent finding is facilitatory interference effects in ungrammatical sentences but inconclusive results in grammatical sentences. Existing models propose that interference is caused either by misrepresentation of the input (representation distortion-based models) or by mis-retrieval of the interfering noun phrase based on cues at the verb (retrieval-based models). These models fail to fully capture the observed interference patterns in the experimental data. We implement two new models under the assumption that a comprehender utilizes a lossy memory representation of the intended message when processing subject-verb agreement dependencies. Our models outperform the existing cue-based retrieval model in capturing the observed patterns in the data for both grammatical and ungrammatical sentences. Lossy compression models under different constraints can be useful in understanding the role of representation distortion in sentence comprehension.

Video recording of my talk at Stanford (April 20, 2021)

I gave a talk at Stanford on April 20, 2021. Here is the recording:

New paper in Cognitive Science (open access): A Computational Evaluation of Two Models of Retrieval Processes in Sentence Processing in Aphasia

 An exciting new paper by my PhD student Paula Lissón

Download from here: https://onlinelibrary.wiley.com/doi/10.1111/cogs.12956

Code and data: https://osf.io/kdjqz/

Title: A Computational Evaluation of Two Models of Retrieval Processes in Sentence Processing in Aphasia

Authors: Paula Lissón, Dorothea Pregla, Bruno Nicenboim, Dario Paape, Mick L. van het Nederend, Frank Burchert, Nicole Stadie, David Caplan, Shravan Vasishth

Abstract: 

Can sentence comprehension impairments in aphasia be explained by difficulties arising from dependency completion processes in parsing? Two distinct models of dependency completion difficulty are investigated, the Lewis and Vasishth (2005) activation‐based model and the direct‐access model (DA; McElree, 2000). These models' predictive performance is compared using data from individuals with aphasia (IWAs) and control participants. The data are from a self‐paced listening task involving subject and object relative clauses. The relative predictive performance of the models is evaluated using k‐fold cross‐validation. For both IWAs and controls, the activation‐based model furnishes a somewhat better quantitative fit to the data than the DA model. Model comparisons using Bayes factors show that, assuming an activation‐based model, intermittent deficiencies may be the best explanation for the cause of impairments in IWAs, although slowed syntax and lexical delayed access may also play a role. This is the first computational evaluation of different models of dependency completion using data from impaired and unimpaired individuals. This evaluation develops a systematic approach that can be used to quantitatively compare the predictions of competing models of language processing.

New paper (to appear in Open Mind):

A postdoc in our lab, Dario Paape, has had a paper accepted in the MIT Press open access journal Open Mind, which is one of the few serious open access journals available as an outlet for psycholinguists (another is Glossa Psycholinguistics). Unlike many of the so-called open access journals out there, Open Mind is a credible journal, not least because of its editorial board (the editor in chief is none other than Ted Gibson). The review process was as or more thoughtful and more thorough than I have experience in journals like Journal of Memory and Language (definitely a notch over Cognition). I am hopeful that we as a community can break free from these for-profit publishers and move towards open access journals like Open Mind and Glossa Psycholinguistics.

Download preprint from here: https://psyarxiv.com/2ztgw/

Title: Does local coherence lead to targeted regressions and illusions of grammaticality?

Authors: Dario Paape, Shravan Vasishth, and Ralf Engbert

Abstract: Local coherence effects arise when the human sentence processor is temporarily misled by a locally grammatical but globally ungrammatical analysis ("The coach smiled at THE PLAYER TOSSED A FRISBEE by the opposing team"). It has been suggested that such effects occur either because sentence processing occurs in a bottom-up, self-organized manner rather than being under constant grammatical supervision (Tabor, Galantucci, & Richardson, 2004), or because local coherence can disrupt processing due to readers maintaining uncertainty about previous input (Levy, 2008). We report the results of an eye-tracking study in which subjects read German grammatical and ungrammatical sentences that either contained a locally coherent substring or not and gave binary grammaticality judgments. In our data, local coherence affected on-line processing immediately at the point of the manipulation. There was, however, no indication that local coherence led to illusions of grammaticality (a prediction of self-organization), and only weak, inconclusive support for local coherence leading to targeted regressions to critical context words (a prediction of the uncertain-input approach). We discuss implications for self-organized and noisy-channel models of local coherence.

New paper: Individual differences in cue-weighting in sentence comprehension: An evaluation using Approximate Bayesian Computation

My PhD student Himanshu Yadav has recently submitted this amazing paper for review to a journal. This is the first in a series of papers that we are working on relating to the important topic of individual-level variability in sentence processing, a topic of central concern in our Collaborative Research Center on variability at Potsdam.

Download the preprint from here: https://psyarxiv.com/4jdu5/

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

Authors: Himanshu Yadav, Dario Paape, Garrett Smith, Brian Dillon, and Shravan Vasishth

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, but here 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 13 published datasets; hierarchical Approximate Bayesian Computation (ABC) was used to estimate the parameters. The modeling reveals a nuanced picture of cue weighting: we find support for the idea that some participants weight cues differentially, but not all participants do. Only fast readers tend to have the higher weighting for structural cues, 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 without compromising the complexity of the model.

New paper: The benefits of preregistration for hypothesis-driven bilingualism research

Download from: here

The benefits of preregistration for hypothesis-driven bilingualism research

Daniela Mertzen, Sol Lago and Shravan Vasishth

Preregistration is an open science practice that requires the specification of research hypoth- eses and analysis plans before the data are inspected. Here, we discuss the benefits of preregis- tration for hypothesis-driven, confirmatory bilingualism research. Using examples from psycholinguistics and bilingualism, we illustrate how non-peer reviewed preregistrations can serve to implement a clean distinction between hypothesis testing and data exploration. This distinction helps researchers avoid casting post-hoc hypotheses and analyses as con- firmatory ones. We argue that, in keeping with current best practices in the experimental sciences, preregistration, along with sharing data and code, should be an integral part of hypothesis-driven bilingualism research.

Freshly minted professor from our lab: Prof. Dr. Titus von der Malsburg

 One of my first PhD students, Titus von der Malsburg, has just been sworn in as a Professor of Psycholinguistics and Cognitive Modeling (tenure track assistant professor) at the Institute of Linguistics, University of Stuttgart in Germany. Stuttgart is one of the most exciting places to be in Germany for computationally oriented scientists.  

Titus is the eighth professor coming out of my lab.  He does very exciting work in psycholinguistics; check out his work here.

New paper: Workflow Techniques for the Robust Use of Bayes Factors

 

Workflow Techniques for the Robust Use of Bayes Factors

Download from: https://arxiv.org/abs/2103.08744

Daniel J. Schad, Bruno Nicenboim, Paul-Christian Bürkner, Michael Betancourt, Shravan Vasishth

Inferences about hypotheses are ubiquitous in the cognitive sciences. Bayes factors provide one general way to compare different hypotheses by their compatibility with the observed data. Those quantifications can then also be used to choose between hypotheses. While Bayes factors provide an immediate approach to hypothesis testing, they are highly sensitive to details of the data/model assumptions. Moreover it's not clear how straightforwardly this approach can be implemented in practice, and in particular how sensitive it is to the details of the computational implementation. Here, we investigate these questions for Bayes factor analyses in the cognitive sciences. We explain the statistics underlying Bayes factors as a tool for Bayesian inferences and discuss that utility functions are needed for principled decisions on hypotheses. Next, we study how Bayes factors misbehave under different conditions. This includes a study of errors in the estimation of Bayes factors. Importantly, it is unknown whether Bayes factor estimates based on bridge sampling are unbiased for complex analyses. We are the first to use simulation-based calibration as a tool to test the accuracy of Bayes factor estimates. Moreover, we study how stable Bayes factors are against different MCMC draws. We moreover study how Bayes factors depend on variation in the data. We also look at variability of decisions based on Bayes factors and how to optimize decisions using a utility function. We outline a Bayes factor workflow that researchers can use to study whether Bayes factors are robust for their individual analysis, and we illustrate this workflow using an example from the cognitive sciences. We hope that this study will provide a workflow to test the strengths and limitations of Bayes factors as a way to quantify evidence in support of scientific hypotheses. Reproducible code is available from this https URL.   

Also see this interesting  twitter thread on this paper by Michael Betancourt:

I believe this paper was initiated towards the end of drafting the Bayesian workflow in cognitive science paper with Daniel and @ShravanVasishth when I mentioned that many of the workflow ideas could be generalized to Bayes factor implementations with a little bit of work.

— \mathfrak{Michael "Shapes Dude" Betancourt} (@betanalpha) March 17, 2021

  

New paper: Is reanalysis selective when regressions are consciously controlled?

A new paper by Dr. Dario Paape; download from here: https://psyarxiv.com/gnehs 

Abstract

The selective reanalysis hypothesis of Frazier and Rayner (1982) states that readers direct their eyes towards critical words in the sentence when faced with garden-path structures (e.g., Since Jay always jogs a mile seems like a short distance to him). Given the mixed evidence for this proposal in the literature, we investigated the possibility that selective reanalysis is tied to conscious awareness of the garden-path effect. To this end, we adapted the well-known self-paced reading paradigm to allow for regressive as well as progressive key presses. Assuming that regressions in such a paradigm are consciously controlled, we found no evidence for selective reanalysis, but rather for occasional extensive, heterogeneous rereading of garden-path sentences. We discuss the implications of our findings for the selective reanalysis hypothesis, the role of awareness in sentence processing, as well as the usefulness of the bidirectional self-paced reading method for sentence processing research.

Talk at Stanford (April 20 2021) Dependency completion in sentence processing: Some recent computational and empirical investigations

Title: Dependency completion in sentence processing: Some recent computational and empirical investigations 

When: April 20, 2021, 9PM German time

Where: zoom.

How to watch: https://linguistics.stanford.edu/events/dependency-completion-sentence-processing-some-recent-computational-and-empirical

 Shravan Vasishth (vasishth.github.io) 

Abstract:

 Dependency completion processes in sentence processing have been intensively studied in psycholinguistics (e.g., Gibson 2000). I will discuss some recent work (e.g., Yadav et al. 2021) on computational models of dependency completion as they relate to a class of effects, so-called interference effects (Jäger et al., 2017). Using antecedent-reflexive and subject-verb number dependencies as a case study (Jäger et al., 2020), I will discuss the evidence base for some of the competing theoretical claims relating to these phenomena.  A common thread running through the talk will be that the well-known replication and statistical crisis in psychology and other areas (Nosek et al., 2015, Gelman and Carlin, 2014) is also unfolding in psycholinguistics and needs to be taken seriously (e.g., Vasishth, et al., 2018).

References 

Andrew Gelman and John Carlin (2014). Beyond power calculations: Assessing type S (sign) and type M (magnitude) errors. Perspectives on Psychological Science, 9(6), 641-651.

Edward Gibson, (2000). The dependency locality theory: A distance-based theory of linguistic complexity. Image, Language, Brain, 2000, 95-126. 

Lena A. Jäger, Felix Engelmann, and Shravan Vasishth, (2017). Similarity-based interference in sentence comprehension: Literature review and Bayesian meta-analysis. Journal of Memory and Language, 94:316-339. 

Lena A. Jäger, Daniela Mertzen, Julie A. Van Dyke, and Shravan Vasishth, (2020). Interference patterns in subject-verb agreement and reflexives revisited: A large-sample study. Journal of Memory and Language, 111. 

Brian A. Nosek, & Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716-aac4716.

Shravan Vasishth, Daniela Mertzen, Lena A. Jäger, and Andrew Gelman, (2018). The statistical significance filter leads to overoptimistic expectations of replicability. Journal of Memory and Language, 103:151-175. 

Shravan Vasishth and Felix Engelmann, (2021). Sentence comprehension as a cognitive process: A computational approach. Cambridge University Press. In Press.

Himanshu Yadav, Garrett Smith, and Shravan Vasishth, (2021). Feature encoding modulates cue-based retrieval: Modeling interference effects in both grammatical and ungrammatical sentences. Submitted.

Talk at Hong Kong Virtual Psycholinguistics Forum (VPF, 心理语言学线上论坛)

I'll be giving at talk at the Chinese University of Hong Kong.
When: 10 March 2021
When: 10AM Berlin time
Where: Zoom:
https://cuhk.zoom.us/j/779556638
https://cuhk.zoom.cn/j/779556638 (mainland China)
Title: Case and Agreement Attraction in Armenian: Experimental and Computational Investigations
Abstract: https://osf.io/3wn79/

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

Applications are open for the fifth summer school in statistical methods for linguistics and psychology (SMLP)

The annual summer school, now in its fifth edition, will happen 6-10 Sept 2021, and will be conducted virtually over zoom. The summer school is free and is funded by the DFG through SFB 1287.
Instructors: Doug Bates, Reinhold Kliegl, Phillip Alday, Bruno Nicenboim, Daniel Schad, Anna Laurinavichyute, Paula Lisson, Audrey Buerki, Shravan Vasishth.
There will be four streams running in parallel: introductory and advances courses on frequentist and Bayesian statistics. Details, including how to apply, are here.