Publication Cover
The Journal of Psychology
Interdisciplinary and Applied
Volume 155, 2021 - Issue 1
498
Views
1
CrossRef citations to date
0
Altmetric
Articles

Neural and Linguistic Considerations for Assessing Moral Intuitions Using Text-Based Stimuli

Pages 90-114 | Received 27 Jul 2020, Accepted 29 Sep 2020, Published online: 12 Nov 2020
 

Abstract

This review takes a focused look at neural and linguistic considerations for assessing moral intuitions using text-based stimuli. Relevant neural correlates of moral salience, emotional processing, moral emotions (shame and guilt), semantic processing, implicit stereotype activation (e.g., gender, age, and race stereotypes), and functional brain network development (the default mode network and salience network) are considered insofar as they relate to unique considerations for text-based instruments. What emerge are not only key considerations for researchers assessing moral intuitions using text-based stimuli but also considerations for the study of moral psychology more broadly, especially in developmental and educational contexts.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Brandon L. Bretl

Brandon L. Bretl is currently a visiting assistant professor at the University of Wisconsin-Eau Claire where he teaches courses in educational psychology and conducts research focused on adolescent moral development.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 143.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.