Syllabus

syllabus-1 (old version: please see the updated schedule below).pdf

Course Schedule

• Course schedule is subject to change.
• See Canvas course website for readings that are not linked.
• Click the toggle button to see the full course schedule.

Introduction to R, Python, and (PC)Ibex

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See tutorials below:

• (PC)Ibex: https://github.com/addrummond/ibex/blob/master/docs/manual.md and https://doc.pcibex.net
• Python: https://docs.python.org/3/tutorial/ (minimally sections 3, 4, 6, 7 will be relevant)
• R: https://learningstatisticswithr.com/lsr-0.6.pdf </aside>

Utils and Exercises

Language Model Demos (Python)

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Simple language model

• One-hot encoding [Google Colab]
• Simple bigram [Google Colab]

Using model output (BERT-based)

• Getting similarity scores and semantically (un)related words [Google Colab]
  • Uses Gensim and scipy (cosine similarity)
• Calculating word predictability; comparing it with Federmeier & Kutas (1999) N400 effect [Google Colab]
  • Uses HuggingFace Transformers and minicons

Using model output (GPT-2-based)

• Tokenization, logits, log probs, surprisal; comparing it with Parker & An (2018) attraction effect [Google Colab]
  • Uses HuggingFace Transformers and minicons </aside>

Data Visualization (R)

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• R Markdown for plotting box plot (and companion .csv data that can be used for plotting)

  bargraph-simplified

  results-demo.csv

• R Markdown for plotting line graph (and companion .csv data that can be used for plotting)

  linegraph

  results_anonymized.csv

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Online Experiment (PCIbex)

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• PCIbex basic setup: Lexical decision task

  priming-lexical-decision

• PCIbex basic setup: Self-paced reading task

  attraction-template

  gardenpath-template

• Introduction to PCIbex

  pci-intro

NOTE: The code above uses Ibex Farm syntax.

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Assignments

• All submissions should be made on Canvas.