For running psychology and neuroscience experiments
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Updated
May 29, 2024 - Python
For running psychology and neuroscience experiments
*SEM 2018: Learning Distributed Event Representations with a Multi-Task Approach
A Python package for processing research with Minimalist grammars
Template for a self-paced reading study, hosted on Ibex Farm
Simulation tools for Mixed Models
Dialogue Experimental Toolkit (DiET)
This repo documents the code that used for web application of the project "The Personality of Gangsters - Researching Quentin Tarantino’s Character Design with the Use of Computer Based Psycholinguistic Analysis".
Workshop on two-way ANOVA and multiple regression in R, presented at the SLAT Roundtable on Feb. 8-9, 2019.
GisPy: A Tool for Measuring Gist Inference Score in Text https://aclanthology.org/2022.wnu-1.5/
Gentle and praatio scripts for easy forced alignment
This Github repo has the sole purpose of enabling Binder environments to run R code in RStudio (https://mybinder.org/v2/gh/pablobernabeu/Modality-switch-effects-emerge-early-and-increase-throughout-conceptual-processing/master?urlpath=rstudio) and Shiny (https://mybinder.org/v2/gh/pablobernabeu/Modality-switch-effects-emerge-early-and-increase-t…
A library for introducing different metrics for measuring linguistic complexity.
Spoken word recognition of children with cochlear implants [BUCLD42]
Instructions for replicating the results of Howcroft & Demberg 2017.
This GUI provides a search engine for ELAN files (.eaf EAF Annotation Format) in a specified directory. Based on your ELAN annotations, it allows direct access to your ELAN through the GUI and can save you valuable time on researching your ELAN database.
An R package for analyzing semantic and affective alignment between conversation partners in natural language transcripts
An investigation into the role of individuals' ideologies on the processing and production of gender-neutral role nouns.
Expectation-based Minimalist Grammars
This repository contains codes for extracting various linguistic features of text.
Mahr and Edwards (2018). Using Language Input and Lexical Processing to Predict Vocabulary Size
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