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Saturday, January 16, 2021

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.

Saturday, January 02, 2021

Should statistical data analysis in psychology be like defecating?

 There was an interesting thread on twitter about linear mixed models (LMMs) that someone made me aware of recently. (I stopped following twitter because of its general inanity, but this thread is worth commenting on.) The gist of the complaints (trying to recreate this list from memory) were. My list is an amalgamation of comments from different people; I think that the thread started here:


To summarize the complaints:

-  LMMs take too long to fit (cf. repeated measures ANOVA). This slows down student output.

- Too much time is spent on thinking about what the right analysis is.

- The interpretation of LMMs can change dramatically depending on which model you fit.

- Reviewers will always object to whatever analysis one does and demand a different one. Often which  analysis one does doesn't matter as regards interpretation.

- The lme4 package exhibits all kinds of weird and unstable behavior. Should we trust its output?

- The focus has shifted away from substantive theoretical issues within psych* to statistical methods, but psych* people cannot be statisticians and can never know enough. This led to the colorful comment that doing statistics should be like taking a crap---it shouldn't become the center of your entire existence.

Indeed, a mathematical psychologist I know, someone who knows what they're doing, once told me that if  you cannot answer your question with a paired t-test, you are asking the wrong question. In fact, if I go back to my existing data-sets that I have published between 2002 and 2020, almost all of them can be reasonably analyzed using a series of paired t-tests. 

There is a presupposition that lies behind the above complaints: the purpose of data analysis is to find out whether an effect is significant or not. Once one understands that that's not the primary purpose of a statistical analysis, things start to make more sense. The problem is that it's just very hard to comprehend this point; this is because the idea of null hypothesis significance testing is very deeply entrenched in our minds. Walking away from it feels impossible. 

Here are some thoughts about the above objections. 

1.  If you want the simplicity of paired t-tests and repeated measures ANOVA, absolutely go for it. But release your data and code, and be open to others analyzing your data differently.  I think it's perfectly fine to spend your entire life doing just paired t-tests and publishing the resulting t and p-values.  Of course,  you are still fitting linear mixed models,  but heavily simplified ones. Sometimes it won't matter whether you fit a complicated model or a simple one, but sometimes it will. It has happened to me that a paired t-test was exactly the wrong thing to do, and I spent a lot of time trying to model the data differently. Should one care about these edge cases? I think this is a subjective decision that each one of us has to make individually. Here is another example of a simple two-condition study where a complicated model that took forever to fit gave new insight into the underlying process generating the data. The problem here comes down to the goal of a statistical analysis. If we accept the premise that statistical significance is the goal, then we should just go ahead and fit that paired t-test. If, instead, the goal is to model the generative process, then you will start losing time. What position you take really depends on what you want to achieve.

2. There is no one right analysis, and reviewers will always object to whatever analysis you present.  The reason that reviewers propose alternative analyses has nothing to do with the inherent flexibility of statistical methods. It has to do with academics being contrarians. I notice this in my own behavior: if my student does X, I want them to do Y!=X. If they do Y, I want them to do X!=Y. I suspect that academics are a self-selected lot, and one thing they are good at is objecting to whatever someone else says or does. So, the fact that reviewers keep asking for different analyses is just the price one has to pay for dealing with academics, it's not an inherent problem with  statistics per se. Notice that reviewers also object to the logic of a paper, and to the writing.  We are so used to dealing with  those things that we don't realize it's the same type of reaction we are seeing to the statistical analyses.

3.  If you want speed and still want to fit linear mixed models, use the right tools. There are plenty of ways to  fit linear mixed models fast. rstanarm, LMMs  in Julia, etc. E.g., Doug Bates, Phillip Alday, and Reinhold Kliegl taught a  one-week course on fitting LMMs super fast in Julia: see here.

4. The interpretation of linear mixed models depends on model specification.  This surprises many people, but the surprise is due to the fact that people have a very incomplete understanding of what they are doing. If you cannot be bothered to study linear mixed modeling theory (understandable, life is short), stick to paired t-tests.

5. lme4's unstable and weird behavior is problematic, but this is not enough reason to abandon linear mixed models.  The weirdness of messages, and the inconsistencies of lme4 are really frustrating, one has to admit that. Perhaps this is the price one has to pay for free software (although, having used non-free software like Word, SPSS, Excel, I'm not so sure there is any advantage). But the fact is that LMMs give you the power to incorporate variance components in a sensible way, and lme4 does the job, if you know what you are doing. Like any other instrument one thinks about using as a professional, if you  can't be bothered to learn to use  it, then just use some simpler method you do know how to use. E.g., I can't use fMRI; I don't have access to the equipment. I'm forced to work with simpler methods, and I have to live with that. If you want more control over your hierarchical models than lme4 provides, learn Stan. E.g., see our chapter on hierarchical models here.

Personally, I think that it is possible to learn enough statistics to be able to use linear mixed models competently; one doesn't need to become a statistician. The curriculum I think one needs in psych and related areas is encapsulated in our summer school on statistical methods, which we run annually at Potsdam. It's a time commitment, but it's worth  it.  I have seen many people go from zero knowledge to fitting sophisticated hierarchical models, so I know that people can learn all this without it taking over their entire life. 

Probably the biggest problem behind all these complaints is the misunderstanding surrounding null hypothesis significance testing. Unfortunately,p-values will rarely tell you anything useful, significant or not, unless you are willing to put in serious time and effort (the very thing people want to avoid doing). So it really not going to matter much whether you compute them using paired t-tests or linear mixed models.




Thursday, December 17, 2020

New paper: The effect of decay and lexical uncertainty on processing long-distance dependencies in reading

The effect of decay and lexical uncertainty on processing long-distance dependencies in reading

Kate Stone, Titus von der Malsburg, Shravan Vasishth

Download here: https://peerj.com/articles/10438/

Abstract:

 To make sense of a sentence, a reader must keep track of dependent relationships between words, such as between a verb and its particle (e.g. turn the music down). In languages such as German, verb-particle dependencies often span long distances, with the particle only appearing at the end of the clause. This means that it may be necessary to process a large amount of intervening sentence material before the full verb of the sentence is known. To facilitate processing, previous studies have shown that readers can preactivate the lexical information of neighbouring upcoming words, but less is known about whether such preactivation can be sustained over longer distances. We asked the question, do readers preactivate lexical information about long-distance verb particles? In one self-paced reading and one eye tracking experiment, we delayed the appearance of an obligatory verb particle that varied only in the predictability of its lexical identity. We additionally manipulated the length of the delay in order to test two contrasting accounts of dependency processing: that increased distance between dependent elements may sharpen expectation of the distant word and facilitate its processing (an antilocality effect), or that it may slow processing via temporal activation decay (a locality effect). We isolated decay by delaying the particle with a neutral noun modifier containing no information about the identity of the upcoming particle, and no known sources of interference or working memory load. Under the assumption that readers would preactivate the lexical representations of plausible verb particles, we hypothesised that a smaller number of plausible particles would lead to stronger preactivation of each particle, and thus higher predictability of the target. This in turn should have made predictable target particles more resistant to the effects of decay than less predictable target particles. The eye tracking experiment provided evidence that higher predictability did facilitate reading times, but found evidence against any effect of decay or its interaction with predictability. The self-paced reading study provided evidence against any effect of predictability or temporal decay, or their interaction. In sum, we provide evidence from eye movements that readers preactivate long-distance lexical content and that adding neutral sentence information does not induce detectable decay of this activation. The findings are consistent with accounts suggesting that delaying dependency resolution may only affect processing if the intervening information either confirms expectations or adds to working memory load, and that temporal activation decay alone may not be a major predictor of processing time.

Saturday, December 12, 2020

New paper: A Principled Approach to Feature Selection in Models of Sentence Processing

 A Principled Approach to Feature Selection in Models of Sentence Processing

Garrett Smith and Shravan Vasishth

Paper downloadable from: https://onlinelibrary.wiley.com/doi/epdf/10.1111/cogs.12918

Abstract

Among theories of human language comprehension, cue-based memory retrieval has proven to be a useful framework for understanding when and how processing difficulty arises in the resolution of long-distance dependencies. Most previous work in this area has assumed that very general retrieval cues like [+subject] or [+singular] do the work of identifying (and sometimes misidentifying) a retrieval target in order to establish a dependency between words. However, recent work suggests that general, handpicked retrieval cues like these may not be enough to explain illusions of plausibility (Cunnings & Sturt, 2018), which can arise in sentences like The letter next to the porcelain plate shattered. Capturing such retrieval interference effects requires lexically specific features and retrieval cues, but handpicking the features is hard to do in a principled way and greatly increases modeler degrees of freedom. To remedy this, we use well-established word embedding methods for creating distributed lexical feature representations that encode information relevant for retrieval using distributed retrieval cue vectors. We show that the similarity between the feature and cue vectors (a measure of plausibility) predicts total reading times in Cunnings and Sturt’s eye-tracking data. The features can easily be plugged into existing parsing models (including cue-based retrieval and self-organized parsing), putting very different models on more equal footing and facilitating future quantitative comparisons.

Tuesday, November 24, 2020

How to become a professor in Germany---a unique tutorial

How to become a Professor in Germany (Online-Seminar)

For sign-up details, see here: https://www.dhvseminare.de/index.php?module=010700&event=187&catalog_id=3&category_id=15&language_id=

Live-Online-Seminar

This seminar addresses young scientists who are considering a career as a professor at a German university or are already in the middle of an application process for a professorship in Germany. It will give the participants an overview of the career paths to a professorship, covering the legal requirements, the appointment procedure and the legal status of a professor. 

The seminar also addresses how to approach the search for relevant job advertisements, how to prepare the written application documents and how to make a good impression during the further steps in the selection process.
In the second part of the seminar, the participants will receive an overview of the next steps for the successful candidates. This includes the appointment negotiations with German universities, the legal framework and the strategic preparation for those negotiations.

 
Speaker:

RA Dr. Vanessa Adam, Justitiarin für Hochschul- und Arbeitsrecht im Deutschen Hochschulverband

RA (syn.) Katharina Lemke, Justitiarin für Hochschul- und Arbeitsrecht im Deutschen Hochschulverband


Schedule:

09:00-10:00 Career Paths to a Professorship (Fr. Lemke)

   10:00-10:15 Break

10:15-11:45 Application for a Professorship (Fr. Dr. Adam)

   11:45-12:15 Break

12:15-13:15 Negotiations with the University (Legal Framework) (Fr. Lemke)

  13:15-13:30 Break

13:30-14:30 Negotiations with the University (Strategy) (Fr. Dr. Adam)


Included in the price:

Seminar documents in electronic form (via download).


Thursday, November 12, 2020

New paper: A computational evaluation of two models of retrieval processes in sentence processing in aphasia

Here is another important paper from my lab, led by my PhD student Paula Lissón, with a long list of co-authors.

This paper, which also depends heavily on the amazing capabilities of Stan, investigates the quantitative predictions of two competing models of retrieval processes, the cue-based retrieval model of Lewis and Vasishth, and the direct-access model of McElree.  We have done such an investigation before, in a very exciting paper by Bruno Nicenboim, using self-paced reading data from a German number interference experiment

What is interesting about this new paper by Paula is that the data come from individuals with aphasia and control participants. Such data is extremely difficult to collect, and as a result many papers report experimental results from a handful of people with aphasia, sometimes as few as 7 people. This paper has much more data, thanks to the hard work of David Caplan

The big achievements of this paper are that it provides a principled approach  to comparing the two competing models' predictions, and it derives testable predictions (which we are about to evaluate with new data from German individuals with aphasia---watch this space). As is always the case in psycholinguistics, even with this relatively large data-set, there just isn't enough data to draw unequivocal inferences. Our policy in my lab is to be upfront about the ambiguities inherent in the inferences. This kind of ambiguous conclusion tends to upset reviewers, because they expect (rather, demand) big-news results. But big news is, more often than not, just illusions of certainty, noise that looks like a signal (see some of my recent papers in the Journal of Memory and Language). We could easily have over-dramatized the paper and dressed it up to say way more than is warranted by the analyses.  Our goal here was to tell the story with all its uncertainties laid bare. The more papers one can put out there that make more measured claims, with all the limitations laid out openly, the easier it will be for reviewers (and editors!) to learn to accept that one can learn something important from a modeling exercise without necessarily obtaining a decisive result.

Download the paper from here: https://psyarxiv.com/r7dn5

A computational evaluation of two models of retrieval processes in sentence processing in aphasia

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 (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 model furnishes a somewhat better quantitative fit to the data than the direct-access model. Model comparison using Bayes factors shows that, assuming an activation-based model, intermittent deficiencies may be the best explanation for the cause of impairments in IWAs. 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.

Wednesday, November 11, 2020

New paper: Modeling misretrieval and feature substitution in agreement attraction: A computational evaluation

This is an important new paper from our lab, led by Dario Paape, and with Serine Avetisyan, Sol Lago, and myself as co-authors. 

One thing that this paper accomplishes is that it showcases the incredible expressive power of Stan, a probabilistic programming language developed by Andrew Gelman and colleagues at Columbia for Bayesian modeling. Stan allows us to implement relatively complex process models of sentence processing and test their performance against data. Paape et al show how we can quantitatively evaluate the predictions of different competing models.  There are plenty of papers out there that test different theories of encoding interference. What's revolutionary about this approach is that one is forced to make a commitment about one's theories; no more vague hand gestures. The limitations of what one can learn from data and from the models is always going to be an issue---one never has enough data, even when people think they do.  But in our paper we are completely upfront about the limitations; and all code and data are available at https://osf.io/ykjg7/ for the reader to look at, investigate, and build upon on their own.

Download the paper from here: https://psyarxiv.com/957e3/

Modeling misretrieval and feature substitution in agreement attraction: A computational evaluation

Abstract

 We present a self-paced reading study investigating attraction effects on number agreement in Eastern Armenian. Both word-by-word reading times and open-ended responses to sentence-final comprehension questions were collected, allowing us to relate reading times and sentence interpretations on a trial-by-trial basis. Results indicate that readers sometimes misinterpret the number feature of the subject in agreement attraction configurations, which is in line with agreement attraction being due to memory encoding errors. Our data also show that readers sometimes misassign the thematic roles of the critical verb. While such a tendency is principally in line with agreement attraction being due to incorrect memory retrievals, the specific pattern observed in our data is not predicted by existing models. We implement four computational models of agreement attraction in a Bayesian framework, finding that our data are better accounted for by an encoding-based model of agreement attraction, rather than a retrieval-based model. A novel contribution of our computational modeling is the finding that the best predictive fit to our data comes from a model that allows number features from the verb to overwrite number features on noun phrases during encoding.