Blink by Malcolm Gladwell doesn’t specifically mention NLP, but the book covers the concept of the processing done by the conscious and unconscious very well. It also provides links to this very interesting test on Implicit Association, where the conscious-unconscious divergences might reveal how we act. ie. Our conscious mind might for instance be very PC (politically correct) and tell us to say one thing, but the whole rest of our being wants to do something else. There are many other reasons for the differences.
The Implicit Association Tests are being carried out at Project Implicit being carried out by implicit social cognition labs at University of Virginia, University of Washington, and Harvard University. They blend basic research and educational outreach in a virtual laboratory at which visitors can examine their own hidden biases. Project Implicit is the product of research by three scientists whose work produced a new approach to understanding of attitudes, biases, and stereotypes.
It is well known that people don’t always ‘speak their minds’, and it is suspected that people don’t always ‘know their minds’. Understanding such divergences is important to scientific psychology.
This web site presents a method that demonstrates the conscious-unconscious divergences much more convincingly than has been possible with previous methods. This new method is called the Implicit Association Test, or IAT for short.
In addition, this site contains various related information. The value of this information may be greatest if you try at least one test first…
Take one of the tests
List of tests at time of publishing
- Age IAT – Age (‘Young – Old’ IAT). This IAT requires the ability to distinguish old from young faces. This test often indicates that Americans have automatic preference for young over old.
- Weapons IAT – Weapons (‘Weapons – Harmless Objects’ IAT). This IAT requires the ability to recognize White and Black faces, and images of weapons or harmless objects.
- Gender-Science IAT – Gender – Science. This IAT often reveals a relative link between liberal arts and females and between science and males.
- Skin-tone IAT – Skin-tone (‘Light Skin – Dark Skin’ IAT). This IAT requires the ability to recognize light and dark-skinned faces. It often reveals an automatic preference for light-skin relative to dark-skin.
- Arab-Muslim IAT – Arab-Muslim (‘Arab Muslim – Other People’ IAT). This IAT requires the ability to distinguish names that are likely to belong to Arab-Muslims versus people of other nationalities or religions.
- Asian IAT – Asian American (‘Asian – European American’ IAT). This IAT requires the ability to recognize White and Asian-American faces, and images of places that are either American or Foreign in origin.
- Gender-Career IAT – Gender – Career. This IAT often reveals a relative link between family and females and between career and males.
- Weight IAT – Weight (‘Fat – Thin’ IAT). This IAT requires the ability to distinguish faces of people who are obese and people who are thin. It often reveals an automatic preference for thin people relative to fat people.
- Religion IAT – Religion (‘Religions’ IAT). This IAT requires some familiarity with religious terms from various world religions.
- Sexuality IAT – Sexuality (‘Gay – Straight’ IAT). This IAT requires the ability to distinguish words and symbols representing gay and straight people. It often reveals an automatic preference for straight relative to gay people.
- Race IAT – Race (‘Black – White’ IAT). This IAT requires the ability to distinguish faces of European and African origin. It indicates that most Americans have an automatic preference for white over black.
- Disability IAT – Disability (‘Disabled – Abled’ IAT). This IAT requires the ability to recognize symbols representing abled and disabled individuals.
- Presidents IAT – Presidents (‘Presidential Popularity’ IAT). This IAT requires the ability to recognize photos of Barack Obama and one or more previous presidents.
- Native IAT – Native American (‘Native – White American’ IAT). This IAT requires the ability to recognize White and Native American faces in either classic or modern dress, and the names of places that are either American or Foreign in origin.
Links to this research
UnderstandingPrejudice.org was established in 2002 with funding from the National Science Foundation (Grant Number 9950517) and McGraw-Hill Higher Education. The purpose of the site is to offer educational resources and information on prejudice, discrimination, multiculturalism, and diversity, with the ultimate goal of reducing the level of intolerance and bias in contemporary society.
July 7, 2009.
"How implicit stereotypes affect gender equity in science"
European Research Headlines.
June 25, 2009.
"The Roots of Racism: What we don’t know can hurt us"
Newsweek. Raina Kelly
June 23, 2009.
"Study Associates Implicit Gender Stereotypes with Science/Math Achievement"
ScienceMag.org. Alan Kotok
June 23, 2009.
"UVA study: Gender bias remains on sexes, science"
Lynchburg News Advance. Rachana Dixit
June 23, 2009.
"We’re all prejudiced and we need to admit it"
August 2, 2008.
"How (un)ethical are you?"
Harvard Business Publishing. Mahzarin Banaji, Max Bazerman, Dolly Chugh
HR Magazine. Pamela Babcock
- June 17, 2003.
Harvard Gazette. William J. Cromie.
- May, 2003.
gradPSYCH. Etienne Benson. *
- March 25, 2003.
"Racism caught in the net."
Sydney Morning Herald. Steve Dow.