Association for Behavior Analysis International

The Association for Behavior Analysis International® (ABAI) is a nonprofit membership organization with the mission to contribute to the well-being of society by developing, enhancing, and supporting the growth and vitality of the science of behavior analysis through research, education, and practice.


48th Annual Convention; Boston, MA; 2022

Event Details

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Symposium #236
CE Offered: BACB
Diversity submission Relational Frames of Prejudice and Intersectionality: Promoting Diversity and Advocacy
Sunday, May 29, 2022
11:00 AM–11:50 AM
Meeting Level 1; Room 156B
Area: CSS/VRB; Domain: Translational
Chair: Jessica M. Hinman (University of Illinois at Chicago )
CE Instructor: Jessica M Hinman, M.S.

Utilizing behavioral interventions to functionally influence socially relevant topics such as discrimination and bias is what behavior analysis was always intended to do. By integrating elements of Relational Frame Theory (RFT), Relational Density Theory (RDT), and Acceptance and Commitment Theory (ACT) the field can begin to predict and influence barriers that people endure based on immutable characteristics such as gender or race. The current series of presentations will address issues of prejudice and discrimination from a behavioral lens. Implicit biases and related clusters of gender and racial discrimination will be discussed, as well as a model to describe arbitrary features associated with the biases held in gender stereotyping. Next, we will discuss biases associated with sexual orientation through a RDT procedure, as well as the potential for using ACT to improve the experience of LGBTQIA+ college students with relevant resources and supports. Lastly, we discuss the binary and nonbinary genders and how we can defuse associated biases through an ACT intervention. Results provide implications that may better guide research, clinical practitioners, and policy to understand the detrimental behaviors people engage in, as well as inspire the field to produce change lead by intention, science, and advocacy.

Instruction Level: Intermediate
Keyword(s): LGBTQIA+, Prejudice, Racism, Sexism
Target Audience:

Behavior analysts, students, and faculty

Learning Objectives: (1) describe challenges experienced by disadvantaged communities; (2) describe the role of relational frames in the development of prejudice; (3) describe the role of third wave interventions in affecting meaningful change for disadvantaged groups
Diversity submission 

Modelling Bias and Prejudice with Relational Density Theory: Gender, Race, and ArbitrAliens

(Applied Research)
ELANA KEISSA SICKMAN (Missouri State University), Jordan Belisle (Missouri State University)

Utilizing Relational Frame Theory (RFT)?models?to?analyze implicit?bias?and discrimination against disadvantaged communities?has been a growing area of interest for the field of Applied Behavior Analysis.?Implicit bias has been successful modelled using procedures like the?Implicit Relational Assessment Procedure (IRAP)?and,?Implicit Association Test?(IAT). Relational Density Theory provides another approach that may successfully?model the interrelatedness of relations that produce bias and prejudice against people. First, we will discuss our research on gender stereotyping showing that relational frames organize into binary gendered clusters that can influence how people respond to others when variables other than gender are held constant. Second, we combined stimuli from multiple IRAP and IAT studies to reveal?complex interrelations that may participate in racial prejudice. Finally, in order to?develop a model of how these relations arrive, we implemented a relational training procedure to?create biased and prejudiced relations among arbitrary features of invented aliens (arbitrAliens) to?demonstrate how prejudice may emerge around relatively arbitrary characteristics of gender and race that can disadvantage members of these communities. Prejudice was measured in a recall?test?and through participant responses across repeated scenarios. Results correspond with our density analysis and mirror results from?the prior?studies.??

Diversity submission Supporting LGBTQIA+ College Students: Psychological Flexibility and Promoting Verbal Behavior of Support and Inclusion
(Applied Research)
BREANNA LEE (Missouri State University), Dana Paliliunas (Missouri State University), Chynna Brianne Frizell (Missouri State University), Elana Keissa Sickman (Missouri State University)
Abstract: LGBTQIA+ youth consistently report lower levels of psychological well-being, often as a result of external stressors (Smithies & Byrom, 2018). Acceptance and Commitment Therapy (ACT) has been used in reducing self-stigma pertaining to sexual orientation. Participants in this research reported decreases in depression, anxiety, and stress, as well as improvements in quality of life and perceived social support (Yadavaia & Hayes, 2012). The degree of available social support from members of local communities, such as students and faculty on a campus, as well as implicit biases of those individuals have the potential to influence the experience of LGBTQIA+ students, positively or negatively. First, the relationship between psychological flexibility, self-compassion, and perceived social support reported by LGBTQIA+ students was explored, and implications for ACT-based interventions for this population will be discussed. Second, a Relational Density Theory framework was utilized to explore biases related to sexual orientation among college student participants and a relational task designed to defuse relations will be evaluated to examine the effectiveness of targeted interventions to reduce implicit biases regarding sexual orientation. Avenues for behavior analytic approaches to both supporting psychological well-being among LGBTQIA+ college students and reducing bias and increasing social support on campuses will be discussed.
Diversity submission Measuring Gender-Related Biases and Exploring Methods to Diminish Bias by Targeting Relations for Defusion
(Applied Research)
CHYNNA BRIANNE FRIZELL (Missouri State University), Breanna Lee (Missouri State University), Dana Paliliunas (Missouri State University)
Abstract: Biases related to gender are an important area of empirical attention in the United States due to social challenges related to prejudice, stereotyping, and discrimination. The purpose of this study is to evaluate potential bias related to binary and nonbinary gender using a measure of relational responding rooted in Relational Density Theory (RDT) (Belisle & Dixon, 2020). Mass and volume of networks in terms of gendered stereotypical relations are assessed to further examine binary gendered stereotypes and to examine relations regarding nonbinary genders in the context of traditionally masculine and feminine labels. Implicit biases regarding male and female genders have been examined, however less research on nonbinary gender biases and stereotypes is available. Using this approach, gender stereotypes are expected to tightly cluster, but the relations may become less dense using an Acceptance and Commitment Therapy (ACT) technique to weaken stereotypical relations that create bias. A defusion procedure was utilized to elaborate relational networks, using an approach adapted from previous research (Belisle, Palilunas, Dixon, & Speelman, 2018). An empirical investigation measuring the effects of a defusion procedure on gendered stereotypical relational responding will be reviewed and discussed in terms of avenues for intervention to diminish unhelpful bias and stereotypical responding.



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