For the March ABAI Symbolic Language and Thought Blog I am thrilled to have Dr. Lynn Farrell talking about Behavior Analysis and gender bias in STEM. Lynn was a doctoral student at the UCD CBS lab under my supervision. I am extremely proud to have been a small part of the career of such an inspiring researcher. Her work always puts equality at the fore. I know you’ll enjoy this one.
– Dr. Louise McHugh, coordinator of the ABAI Symbolic Language and Thought blog series 2020.
Dr Lynn Farrell is a Research Fellow at the School of Psychology, Queen’s University Belfast (QUB). She graduated with a PhD in Psychology from University College Dublin where she was an Irish Research Council scholar. Her research focused on the nature and malleability of implicit bias towards women in STEM. She was awarded the Student Spotlight award from the Association for Contextual Behavioral Science. Dr Farrell is currently part of the Research Team for the EPSRC funded ‘Inclusion Really Does Matter’ project based at QUB which aims to improve attitudes towards Gender Equality Initiatives among STEM academics. Her research interests include the nature and malleability of gender bias and stereotypes, particularly in relation to STEM fields, and the application of Relational Frame Theory to social psychological phenomena.
Off the top of your head can you name a well-known female scientist? How about more than one? It turns out many adults struggle to do so1. From the age of 7-8, children are more likely to draw a man when prompted to draw a scientist2 and adults are more likely to name a man when asked to name a scientist3. When we think of scientists it’s easier to think of men. Stereotypes about gender and science are important contributing factors to this.
Men and scientists are perceived as more similar than women and scientists4. Across at least 66 nations5, men are more strongly associated with STEM both implicitly and explicitly. This can lead to more positive evaluations of men relative to women for STEM roles6 and tasks7. These biases and stereotypes must be challenged to diversify STEM domains and create supportive working environments regardless of gender.
Where Does Relational Frame Theory Fit In?
Relational Frame Theory (RFT) has the ability to provide a modern behavioral approach to the study of social psychological phenomena. This was highlighted in the original RFT text (the infamous ‘Purple Book’8) and even earlier via stimulus equivalence studies of attitudes and stereotypes. These studies demonstrated that attitudes are verbal behavior formed via relational framing which transform the functions of relevant stimuli. They may form even if individuals don’t have direct experience with the relevant stimuli and can be influenced via contextual control9.
Attitudes and stereotypes are therefore dynamic, context-dependent and influenced by social contingencies, precisely because they are forms of arbitrarily applicable relational responding maintained by current and historical contextual factors. This conceptualization aids empirical examinations of the construction of gender and gender stereotypes, the particular contextual factors that support these relations and their influence on social behavior10. Contextual factors, for instance, are more contactable than a hypothetical mental construct or ‘attitude’ after all.
One way to capture relevant patterns of relational responding is via the Implicit Relational Assessment Procedure (IRAP11). The IRAP is a latency-based computer task which has participants respond to the relations between stimuli under time pressure and according to pre-determined rules. These rules switch across the task’s blocks such that half of the blocks require one set of rules while the remaining half require the inverse pattern of responding. In this way, it measures the strength and probability of relations between stimuli, established by an individual’s history of derived relational responding. Cartwright and colleagues (2017)12 demonstrated the utility of the IRAP in the domain of gender by exploring binary gender stereotypes. They found that feminine traits were coordinated with women and not men (opposition and/or distinction), and masculine traits were coordinated with men but not women.
This approach can shed light on levels of gender bias in STEM also. My own doctoral research with Dr Louise McHugh used the IRAP to examine patterns of relational responding relevant to gender-STEM bias. A number of studies confirmed a strong relation between men and STEM (implicitly and explicitly), which correlated with stereotype-consistent behavior such as choosing men as better performers on STEM-related tasks13. However, surprisingly we found evidence for a positive relation between women and STEM also14, 15. Though often weak among individuals in general, it was strongest among women studying STEM subjects. This represented a promising entry point for intervention. Roche and colleagues (2001)16 suggest that it is likely easier to elaborate on existing verbal relational networks than to establish new conflicting networks. Therefore, it may be more fruitful to influence relations already established in an individual’s behavioral repertoire and determine which contextual factors support the positive relations we wish to strengthen.
The aim of intervention in relation to gender-STEM bias is not to reduce the relation between men and STEM per se but to strengthen the positive relation between women and STEM to make it as normal or as likely to occur. The opportunity to further derive a positive relation between women and STEM through engaging with relevant interventions may increase the relational coherence of these responses and increase relational flexibility. This should elaborate relevant relational networks and impact on IRAP performance.
In a paper currently under review, myself and my colleagues (Dr Louise McHugh & Dr Finiki Nearchou) compared a number of interventions and found psychoeducation about implicit gender-STEM bias and exposure to counter-stereotypical exemplars to be most effective at influencing gender-STEM relations mainly by increasing the positive relation between women and STEM compared to a control group. An increase in this positive relation among control group participants the day after the intervention further demonstrated the impact of contextual factors on implicit responding and suggested that even completing an IRAP itself may influence responding. These results represent an interesting basis for future intervention in this domain aided by an RFT-based approach.
It’s All About Framing
RFT provides the framework and tools to explore gender relations in complex and meaningful ways and continues to refine our understanding of patterns of relational responding with developments such as the hyper-dimensional, multi-level (HDML) framework17. The HDML framework considers the four dimensions of relational responding (complexity, derivation, coherence and flexibility) and five levels of relational development (mutual entailing; relational framing; relational networking; relating relations; relating relational networks) in combination with the orienting (or recognition) and evoking functions of stimuli to aid our understanding and analysis of the dynamics of arbitrarily applicable relational responding.
The IRAP, of course, represents only one approach, and STEM, only one domain in which the application of RFT to social psychological phenomenon can and has been explored. For example, the Function Acquisition Speed Test (FAST18) represents an alternative approach that compares response class acquisition rates across learning history-consistent and learning history-inconsistent blocks. Additionally, there are professions where men are underrepresented (e.g., caring professions) that may benefit from an examination of the patterns of relational responding contributing to such trends.
RFT offers hope that implicit bias can and should be influenced by contextual factors which provides a strong rationale for intervention and supports research demonstrating that stereotypes can and have changed over time19. Although it is important to target systemic change also, a change in stereotypes and bias at the individual level is necessary to help create supportive contexts in which gender equality can thrive. For example, if we no longer perceive women as less suitable for STEM careers, then men and women should be held to equal evaluative standards for STEM roles. Perhaps then when we think of scientists, we’ll call to mind the names of women as easily as men.
- Green, C. (2014, May 14). Could you name more than one female scientist? Independent. https://www.independent.co.uk/news/science/could-you-name-more-than-one-female-scientist-9391307.html
- Miller, D. I., Nolla, K. M., Eagly, A. H., & Uttal, D. H. (2018). The development of children’s gender‐science stereotypes: A meta‐analysis of 5 decades of US Draw‐a‐Scientist studies. Child development, 89(6), 1943-1955.
- L’Oréal. (2015, September 17th). The L’Oréal Foundation unveils the results of its exclusive international study #ChangeTheNumbers. https://www.loreal.com/media/press-releases/2015/sep/the-loreal-foundation-unveils-the-results-of-its-exclusive-international-study
- Carli, L. L., Alawa, L., Lee, Y., Zhao, B., & Kim, E. (2016). Stereotypes about gender and science: Women≠ scientists. Psychology of Women Quarterly, 40(2), 244-260.
- Miller, D. I., Eagly, A. H., & Linn, M. C. (2015). Women’s representation in science predicts national gender-science stereotypes: Evidence from 66 nations. Journal of Educational Psychology, 107(3), 631.
- Moss-Racusin, C. A., Dovidio, J. F., Brescoll, V. L., Graham, M. J., & Handelsman, J. (2012). Science faculty’s subtle gender biases favor male students. Proceedings of the national academy of sciences, 109(41), 16474-16479.
- Reuben, E., Sapienza, P., & Zingales, L. (2014). How stereotypes impair women’s careers in science. Proceedings of the National Academy of Sciences, 111(12), 4403-4408.
- Hayes, S. C., Barnes-Holmes, D., & Roche, B. (2001). Relational frame theory: A post-Skinnerian account of human language and cognition. Springer Science & Business Media.
- Grey, I. M., & Barnes, D. (1996). Stimulus equivalence and attitudes. The Psychological Record, 46(2), 243.
- Roche, B., Barnes-Holmes, Y., Barnes-Holmes, D., Stewart, I., & O’Hora, D. (2002). Relational frame theory: A new paradigm for the analysis of social behavior. The Behavior Analyst, 25(1), 75-91.
- Barnes-Holmes, D., Barnes-Holmes, Y., Power, P., Hayden, E., Milne, R., & Stewart, I. (2006). Do you really know what you believe? Developing the Implicit Relational Assessment Procedure (IRAP) as a direct measure of implicit beliefs. The Irish Psychologist, 32(7), 169-177.
- Cartwright, A., Hussey, I., Roche, B., Dunne, J., & Murphy, C. (2017). An investigation into the relationship between the gender binary and occupational discrimination using the implicit relational assessment procedure. The Psychological Record, 67(1), 121-130.
- Farrell, L., & McHugh, L. (2020). Exploring the relationship between implicit and explicit gender-STEM bias and behavior among STEM students using the Implicit Relational Assessment Procedure. Journal of Contextual Behavioral Science, 15, 142-152.
- Farrell, L., Cochrane, A., & McHugh, L. (2015). Exploring attitudes towards gender and science: The advantages of an IRAP approach versus the IAT. Journal of Contextual Behavioral Science, 4(2), 121-128.
- Farrell, L., & McHugh, L. (2017). Examining gender-STEM bias among STEM and non-STEM students using the Implicit Relational Assessment Procedure (IRAP). Journal of Contextual Behavioral Science, 6(1), 80-90.
- Roche, B., Barnes-Holmes, Y., Barnes-Holmes, D., & Hayes, S. C. (2001). Social Processes. In S. C. Hayes, D. Barnes-Holmes, & B. Roche (Eds.) Relational frame theory: A post-Skinnerian account of human language and cognition. (pp. 197-209). Springer Science & Business Media.
- Barnes-Holmes, D., Barnes-Holmes, Y., & McEnteggart, C. (2020). Updating RFT (More Field than Frame) and its Implications for Process-based Therapy. The Psychological Record, 1-20.
- O’Reilly, A., Roche, B., Ruiz, M., Tyndall, I., & Gavin, A. (2012). The Function Acquisition Speed Test (FAST): A behavior analytic implicit test for assessing stimulus relations. The Psychological Record, 62(3), 507-528.
- Eagly, A. H., Nater, C., Miller, D. I., Kaufmann, M., & Sczesny, S. (2019). Gender stereotypes have changed: A cross-temporal meta-analysis of US public opinion polls from 1946 to 2018. American psychologist.