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.

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.