Giter Site home page Giter Site logo

thesis's Introduction

thesis

Open in gitpod

Scripts from my graduate thesis looking at emotion word processing. I'll pop the abstract down below, but feel free to reach out if you'd like the full paper or presentation posters! (update: it's here!)

Abstract

Current literature suggests emotion-label words (e.g., sad) and emotion-laden words (e.g., funeral) are processed differently. The central focus of the present study was to investigate how valence and emotion word type influence how words are processed. A satiation paradigm was used to characterize the relationship between the processing of emotion-label and emotion-laden words of positive and negative valence. It was hypothesized that, in addition to the standard slowed response times to satiated words, emotion-label words would exhibit greater satiation and priming effects than emotion-laden words. Analyses indicated expected priming and satiation effects across a range of other stimulus characteristics. Neutral words, which were included as a comparison stimulus type for both valence and word type variables, were shown to elicit much slower reaction times than either emotion word type. The results of the present study indicate the importance of valence in word processing, even when other word characteristics and experimental variables are at play. Current models of word processing do not sufficiently account for emotional characteristics of words, and implications for word processing models are discussed.

Keywords: word processing, emotion words, satiation, priming, attention, emotion-laden words

Code & Analyses

Here is the knit output of this R code, containing most data processing, analyses, and graphics for the study.

Outputs

The main repeated measures ANOVAs are included here:

And some plots showing the main findings of interest:

Mean Reaction Time by Primed/Satiated

Mean Reaction Time by Word Type

Mean Reaction Time by Word Type & Primed/Satiated

Mean Reaction Time by Word Type, Primed/Satiated, and Valence

Summary of Findings

  • Slower RT’s for negative targets may be a result of negative words’ increased attention capture, impairing disengagement from the target in order to complete subsequent task
  • Conversely, the lack of observed satiation for negative emotion-label targets may relate to the salience of negative stimuli, making their meanings more difficult to satiate in the first place
  • While neutral targets also elicited slower RT’s, this may instead be due to the ambiguity inherent in categorizing their valence, as they are not immediately recognized as positive or negatively valenced
  • Compared to tasks commonly used in the semantic satiation literature, the task in the current study resulted in lower accuracy rates, potentially indicating that the present task was exceptionally difficult. This may have obscured expected effects from the IVs

thesis's People

Contributors

ryancahildebrandt avatar

Watchers

 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.