JP van Paridon, PhD

 619 536 6806
vanparidon@wisc.edu
github.com/jvparidon
linkedin.com/in/jp-van-paridon
jvparidon.io/resume

Profile

I am a behavioral data scientist and computational cognitive scientist with a background in language sciences. In very general terms, my work uses online and in-person experiments and large public datasets to inform statistical and computational models of human cognition and behavior. My work has been published in top journals in psychology and linguistics and presented at international scientific conferences.

Employment

Postdoctoral Researcher, University of Wisconsin-Madison, 2020-present.

I work on “Learning from Language”, a project investigating how we leverage other people’s language use to learn about the meaning of words and how to correctly use those words in context. I collect behavioral data using online experiments and analyze them using statistical techniques including multilevel regression models, dimensionality reduction, and clustering algorithms. I also use and modify NLP models like word2vec and BERT to gain a better understanding of language learning mechanisms.

PhD Candidate, Max Planck Institute for Psycholinguistics, the Netherlands, 2015-2019.

Athletic Director, ALSRV Asopos de Vliet (Leiden University Rowing Club), 2013-2014.

Education

Open source software

I am the lead developer and maintainer of lmerMultiMember, an R package for modeling multiple membership random effects in (generalized) linear mixed effects models.

Skills

Published papers

  1. J. van Paridon & G. Lupyan (2022). Implicit communication of word meaning through co-occurrence. Proceedings of the Joint Conference on Language Evolution. [pdf]
  2. G. Montero-Melis, J. van Paridon, M. Ostarek, & E. Bylund (2022). No evidence for embodiment: The motor system is not needed to keep action verbs in working memory. Cortex. [pdf][doi]
    For this project, we developed a sequential testing framework using Bayesian multilevel regression models to efficiently sample participants without violating statistical assumptions. I also integrated a Python interface for a MIDI drumkit into our behavioral experiment protocol as a cheap and effective way to track motor activity in participants' hands and feet.
  3. M. Arunkumar, J. van Paridon, M. Ostarek, & F. Huettig (2021). Do illiterates have illusions? A conceptual (non) replication of Luria (1976). Journal of Cultural Cognitive Science. [pdf][doi]
  4. J. van Paridon, Q. Liu, & G. Lupyan (2021). How do blind people know that blue is cold? Distributional semantics encode color-adjective associations. Proceedings of the Annual Meeting of the Cognitive Science Society. [pdf][doi]
    For this project, I used a variety of NLP and statistical techniques to demonstrate that color knowledge in both blind and sighted people can be predicted from word embeddings. I also reworked the original word2vec algorithm to gain access to word embeddings during model training and track how specific sentences in the training corpus affect the final state of the embedding model.
  5. J. van Paridon, M. Ostarek, M. Arunkumar, & F. Huettig (2021). Does neuronal recycling result in destructive competition? The influence of learning to read on the recognition of faces. Psychological Science. [pdf][doi]
    In this project, I used Bayesian multilevel regression models to analyze behavioral data collected from illiterate participants to show that acquiring literacy does not degrade performance on other visual tasks (a claim that had been made repeatedly in the literature in prior years).
  6. J. van Paridon & B. Thompson (2021). subs2vec: Word embeddings from subtitles in 55 languages. Behavior Research Methods. [pdf][doi]
    In this project, we produced a novel set of word embeddings in 55 languages from a large archive of film and television subtitles, using the fastText algorithm. Using several classic evaluation metrics (predicting similarity ratings using cosine distances; solving lexical analogies) and a novel lexical norm prediction task (implemented using ridge regression), we demonstrate that embeddings trained on subtitles contain information not well-represented in embeddings trained on e.g. Wikipedia, for instance about how offensive a given word is. The subs2vec package I developed alongside this project also provides a lightweight framework for working with word embeddings in Python.
  7. G. Montero-Melis, P. Isaksson, J. van Paridon, & M. Ostarek (2020). Does using a foreign language reduce mental imagery? Cognition. [pdf][doi]
  8. M. Ostarek, J. van Paridon, & G. Montero-Melis (2019). Sighted people’s language is not helpful for blind individuals’ acquisition of typical animal colors. Proceedings of the National Academy of Sciences. [pdf][doi]
  9. J. van Paridon, A. Roelofs, & A. Meyer (2019). A lexical bottleneck in shadowing and translating of narratives. Language, Cognition and Neuroscience. [pdf][doi]
    In this project, I built a computational model to simulate the temporal coordination of speaking and listening processes during simultaneous interpreting, demonstrating that one significant limit on the rate of speech during this task is imposed by the demand to access lexical networks for both speech production and speech comprehension processes.
  10. C. M. Warren, K. D. Tona, L. Ouwerkerk, J. van Paridon, F. Poletiek, J. A. Bosch, & S. Nieuwenhuis (2019). The neuromodulatory and hormonal effects of transcutaneous vagus nerve stimulation as evidenced by salivary alpha amylase, salivary cortisol, pupil diameter, and the P3 event-related potential. Brain Stimulation. [pdf][doi]
  11. Z. Shao, J. van Paridon,, F. Poletiek, & A. Meyer (2019). Effects of phrase and word frequencies in noun phrase production. Journal of Experimental Psychology: Learning, Memory, and Cognition. [pdf][doi]

Technical whitepapers and preprints

  1. J. Sulik, J. van Paridon, & G. Lupyan (2021). Explanations in the Wild. Preprint. [pdf][doi]
  2. P. M. Alday & J. van Paridon (2020). Away from arbitrary thresholds: using robust statistics to improve artifact rejection in ERP. Technical whitepaper. [pdf][doi]
    In this paper, we make recommendations for improving EEG analyses by using robust models which can account for outliers without having to reject data after setting arbitrary thresholds. We provide examples of analyses using robust models in both frequentist (using a robust estimator) and Bayesian (using a heavy-tailed likelihood) frameworks.
  3. J. van Paridon & P. M. Alday (2020). A note on co-occurrence, transitional probability, and causal inference. Technical whitepaper. [pdf][doi]
    In this paper, we explain that the high degree of multicollinearity between various measures of word frequency and transitional probability in linear regression models used in psycholinguistic analyses means that coefficients are often misinterpreted. It is hard to make blanket recommendations for analytical decisions, but we argue that in many cases, a theoretically motivated choice for one specific predictor leads to the most interpretable outcome.
  4. M. Ostarek, J. van Paridon, & F. Huettig (2018). Cross-decoding reveals shared brain activity patterns between saccadic eye-movements and semantic processing of implicitly spatial words. Preprint. [pdf][doi]
    In this project, my role was to set up an analysis pipeline using (cross-validated) SVM classifiers trained on fMRI images from an eye-saccade task to predict the spatial orientation of words processed by the same participants in a different task from their corresponding fMRI images.
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