New paper: Syntactic and semantic interference in sentence comprehension: Support from English and German eye-tracking data

This paper is part of a larger project that has been running for 4-5 years, on the predictions of cue-based retrieval theories.  This paper revisits Van Dyke 2007's design, using eye-tracking (the data are from comparable designs in English and German). The reading time patterns are consistent with syntactic interference at the moment of retrieval in both English. Semantic interference shows interesting differences between English and German---in English, semantic interference seems to happen simultaneously with syntactic interference, but in German, semantic interference is delayed (it appears in the post-critical region). The morphosyntactic properties of German could be driving the lag in semantic interference. We also discuss the data in the context of the quantitative predictions from the Lewis & Vasishth cue-based retrieval model.

One striking fact about psycholinguistics in general and interference effects in particular is that most of the data tend to come from English. Very few people work on non-English languages. I bet there are a lot of surprises in store for us once we step out of the narrow confines of English. I bet that most theories of sentence processing are overfitted to English and will not scale. And if you submit a paper to a journal using data from a non-English language, there will always be a reviewer or editor who asks you to explain why you chose language X!=English, and not English. Nobody ever questions you if you study English. A bizarre world.

Title: Syntactic and semantic interference in sentence comprehension: Support from English and German eye-tracking data

Abstract: 

A long-standing debate in the sentence processing literature concerns the time course of syntactic and semantic information in online sentence comprehension. The default assumption in cue-based models of parsing is that syntactic and semantic retrieval cues simultaneously guide dependency resolution. When retrieval cues match multiple items in memory, this leads to similarity-based interference. Both semantic and syntactic interference have been shown to occur in English. However, the relative timing of syntactic vs. semantic interference remains unclear. In this first-ever cross-linguistic investigation of the time course of syntactic vs. semantic interference, the data from two eye-tracking reading experiments (English and German) suggest that the two types of interference can in principle arise simultaneously during retrieval. However, the data also indicate that semantic cues may be evaluated with a small timing lag in German compared to English. This suggests that there may be cross-linguistic variation in how syntactic and semantic cues are used to resolve linguistic dependencies in real-time.

Download pdf from here: https://psyarxiv.com/ua9yv

New paper in Computational Brain and Behavior: Sample size determination for Bayesian hierarchical models commonly used in psycholinguistics

We have just had a paper accepted in the journal Computational Brain and Behavior. This is part of a special issue that responds to the following paper on linear mixed models:
van Doorn, J., Aust, F., Haaf, J.M. et al. Bayes Factors for Mixed Models. Computational Brain and Behavior (2021). https://doi.org/10.1007/s42113-021-00113-2
There are quite a few papers in that special issue, all worth reading, but I especially liked the contribution by Singmann et al: Statistics in the Service of Science: Don't let the Tail Wag the Dog (https://psyarxiv.com/kxhfu/) They make some very good points in reaction to van Doorn et al's paper.

Our paper: Shravan Vasishth, Himanshu Yadav, Daniel J. Schad, and Bruno Nicenboim. Sample size determination for Bayesian hierarchical models commonly used in psycholinguistics. Computational Brain and Behavior, 2021.
Abstract: We discuss an important issue that is not directly related to the main theses of the van Doorn et al. (2021) paper, but which frequently comes up when using Bayesian linear mixed models: how to determine sample size in advance of running a study when planning a Bayes factor analysis. We adapt a simulation-based method proposed by Wang and Gelfand (2002) for a Bayes-factor based design analysis, and demonstrate how relatively complex hierarchical models can be used to determine approximate sample sizes for planning experiments.
Code and data: https://osf.io/hjgrm/
pdf: here

New paper: Similarity-based interference in sentence comprehension in aphasia: A computational evaluation of two models of cue-based retrieval.

My PhD student Paula Lissón has just submitted this important new paper for review to a journal. This paper is important for several reasons but the most important one is that it's the first to quantitatively compare two competing computational models of retrieval in German sentence processing using data from unimpaired controls and individuals with aphasia. The work is the culmination of four years of hard work involving collecting a relatively large data-set (this amazing feat was achieved by Dorothea Pregla, and documented in a series of papers she has written, for example see this one in Brain and Language), and then developing computational models in Stan to systematically evaluate competing theoretical claims. This line of work should raise the bar in psycholinguistics when it comes to testing predictions of different theories. It is pretty common in psycholinguistics to wildly wave one's hands and say things like "sentence processing in individuals with aphasia is just noisy", and be satisfied with that statement and then publish it as a big insight into sentence processing difficulty. An important achievement of Paula's work, which builds on Bruno Nicenboim's research on Bayesian cognitive modeling, is to demonstrate how to nail down the claim and how to test it quantitatively. It seems kind of obvious that one should do that, but surprisingly, this kind of quantitative evaluation of models is still relatively rare in the field.


Title: Similarity-based interference in sentence comprehension in aphasia: A computational evaluation of two models of cue-based retrieval.


Abstract: Sentence comprehension requires the listener to link incoming words with short-term memory representations in order to build linguistic dependencies. The cue-based retrieval theory of sentence processing predicts that the retrieval of these memory representations is affected by similarity-based interference. We present the first large-scale computational evaluation of interference effects in two models of sentence processing – the activation-based model, and a modification of the direct-access model – in individuals with aphasia (IWA) and control participants in German. The parameters of the models are linked to prominent theories of processing deficits in aphasia, and the models are tested against two linguistic constructions in German: Pronoun resolution and relative clauses. The data come from a visual-world eye-tracking experiment combined with a sentence-picture matching task. The results show that both control participants and IWA are susceptible to retrieval interference, and that a combination of theoretical explanations (intermittent deficiencies, slow syntax, and resource reduction) can explain IWA’s deficits in sentence processing. Model comparisons reveal that both models have a similar predictive performance in pronoun resolution, but the activation-based model outperforms the direct-access model in relative clauses.


Download: here. Paula also has another paper modeling English data from unimpaired controls and individuals in aphasia, in Cognitive Science.

A confusing tweet on (not) transforming data keeps reappearing on the internet

I keep seeing this misleading comment on the internet over and over again:

Non-normality is relatively unimportant; at worst you just may lose a bit of power. I strongly recommend @StatModeling & Hill (2007, pp. 45-47)'s summary of key regression model assumptions. Normality of errors literally gets LOWEST priority. My experience supports this. 3/3 pic.twitter.com/R0BfQCoxdK

— Roger Levy (@roger_p_levy) December 8, 2018

Gelman is cited above, but Gelman himself has spoken out on this point and directly contradicts the above tweet: https://statmodeling.stat.columbia.edu/2019/08/21/you-should-usually-log-transform-your-positive-data/
Even the quoted part from the Gelman and Hill 2007 book is highly misleading because it is most definitely not about null hypothesis significance testing.
Non-normality is relatively unimportant in statistical data analysis the same way that a cricket ball is relatively unimportant in a cricket match. The players, the pitch, the bat, are much more important, but everyone would look pretty silly on the cricket field without that ball.
I guess if we really, really need a slogan to be able to do data analysis, it should be what one should call the MAM principle: model assumptions matter.

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 paper: When nothing goes right, go left: A large-scale evaluation of bidirectional self-paced reading

 Here's an interesting and important new paper led by the inimitable Dario Paape:

Title: When nothing goes right, go left: A large-scale evaluation of bidirectional self-paced reading

Download from: here.

Abstract: 

In two web-based experiments, we evaluated the bidirectional self-paced reading (BSPR) paradigm recently proposed by Paape and Vasishth (2021). We used four sentence types: NP/Z garden-path sentences, RRC garden-path sentences, sentences containing inconsistent discourse continuations, and sentences containing reflexive anaphors with feature-matching but grammatically unavailable antecedents. Our results show that regressions in BSPR are associated with a decrease in positive acceptability judgments. Across all sentence types, we observed online reading patterns that are consistent with the existing eye-tracking literature. NP/Z but not RRC garden-path sentences also showed some indication of selective rereading, as predicted by the selective reanalysis hypothesis of Frazier and Rayner (1982). However, selective rereading was associated with decreased rather than increased sentence acceptability, which is not in line with the selective reanalysis hypothesis. We discuss the implications regarding the connection between selective rereading and conscious awareness, and for the use of BSPR in general.

Special issue in the journal Linguistics on the Replication Crisis

 

Applications are open: 2022 summer school on stats methods for ling and psych

 Applications are now open for the sixth SMLP summer school, to be held in person (hopefully) in the Griebnitzsee campus of the University of Potsdam, Germany, 12-16 Sept 2022.

Apply here: https://vasishth.github.io/smlp2022/

Podcast interview with me in "Betancourting disaster"

Michael Betancourt is a major force in applied Bayesian statistics. Over the years, he has written a huge number of case studies and tutorials relating to practical aspects of Bayesian modeling using Stan. He has also lectured at our summer school on statistics, which is held annually at Potsdam. He also has a large collection of publicly available talks that are worth watching. 

We have collaborated with Michael to produce two really important papers for cognitive scientists:

1. Daniel J. Schad, Michael Betancourt, and Shravan Vasishth. Toward a principled Bayesian workflow: A tutorial for cognitive science. Psychological Methods, 2020. Download here: https://arxiv.org/abs/1904.12765.

2. Daniel J. Schad, Bruno Nicenboim, Paul-Christian Bürkner, Michael Betancourt, and Shravan Vasishth. Workflow Techniques for the Robust Use of Bayes Factors. Available from arXiv:2103.08744v2, 2021. Download here: https://arxiv.org/abs/2103.08744.

He has a podcast, called Betancourting disaster. Michael recently interviewed me, and we talked about the challenges associated with modeling cognitive processes (e.g., reading processes and their interaction with sentence comprehension). You can listen to the whole thing here (it's about an hour-long conversation):

https://www.patreon.com/posts/50550798

New paper (Vasishth and Gelman): How to embrace variation and accept uncertainty in linguistic and psycholinguistic data analysis

A new paper, just accepted in the journal Linguistics:

Download: https://psyarxiv.com/zcf8s/

Title: How to embrace variation and accept uncertainty in linguistic and psycholinguistic data analysis 

Abstract: The use of statistical inference in linguistics and related areas like psychology typically involves a binary decision: either reject or accept some null hypothesis using statistical significance testing. When statistical power is low, this frequentist data-analytic approach breaks down: null results are uninformative, and effect size estimates associated with significant results are overestimated. Using an example from psycholinguistics, several alternative approaches are demonstrated for reporting inconsistencies between the data and a theoretical prediction. The key here is to focus on committing to a falsifiable prediction, on quantifying uncertainty statistically, and learning to accept the fact that—in almost all practical data analysis situations—we can only draw uncertain conclusions from data, regardless of whether we manage to obtain statistical significance or not. A focus on uncertainty quantification is likely to lead to fewer excessively bold claims that, on closer investigation, may turn out to be not supported by the data.

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.

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: 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.

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.