Decoded Neurofeedback Toward Bias and Racism Mitigation (2022-2023)

Background

Racism is a complex issue faced worldwide fueled by generations of social, cultural and systemic norms. Racial prejudice – the tendency to discriminate against individuals of different races based on preconceived opinions – hinders access to healthcare, dampens educational outcomes, prevents fair economic opportunities and fuels employment inequality. 

While people demand change at a societal level, reducing racism at the level of individuals is a complex issue to navigate. Most current strategies aim to reduce bias, such as appealing to multiculturalism, priming prominent minority figures and advocating for intergroup contact utilizing behavioral techniques for a limited duration. Given racism’s destructive impact on minority groups, it is essential to devise effective methods capable of reducing implicit bias on both neural and behavioral levels.

Project Description

This is a joint project between Duke and Duke Kunshan University. Building on the work of the 2021-2022 team, this project team will develop a decoded neurofeedback protocol and work to consolidate a comprehensive literature review on racial bias and neurofeedback. 

Team members will also investigate how the brain mediates racial biases and the implication in real-world decision-making; decode biased and unbiased neural representations of faces from functional magnetic resonance imaging (fMRI) data; and explore social applications of decoded neurofeedback and investigate predictors of real-time fMRI decoded neurofeedback performance.

To illuminate the intricate relationship between race processing systems and their unique contribution to persisting bias, the team will apply decoded neurofeedback in two studies:

  • Study 1: The team will test whether altering the perceptual representation of faces reduces racial bias. Team members will apply hyperalignment to construct the desired brain representation of viewing out-group members for biased individuals as if they were bias-free from unbiased persons’ brain imaging data. By consistently associating this unbiased brain state with reward in the subsequent training, the hypothesis is that individuals will grow to favor the unbiased representation over time, and such a shift in preference should be significantly correlated to behavioral changes.
  • Study 2: The team will explore whether modulating cognitive control influences racial bias with the expectation that a higher level of cognitive control correlates to reduced biases. Team members will construct a classifier to decode high cognitive control state as the target state of decoded neurofeedback training. This hypothesis is that such changes are transferable to measures of racial bias measured by both behavioral and neural indicators.

Anticipated Outputs

Report publication; conference paper(s)

Timing

Summer 2022 – Spring 2023

  • Summer 2022 (optional): Consolidate literature review, recruit participants for pilot behavioral testing; optimize neural imaging parameters
  • Fall 2022: Finalize preregistered report; collect imaging data
  • Spring 2023: Organize symposia; submit conference abstract

See earlier related team, Decoded Neurofeedback Toward Bias and Racism Mitigation (2021-2022).

 

Image: Duke Kunshan University in March 2016, by Chris Hildreth

Duke Kunshan University.

Team Leaders

  • Jing Cai, Duke Kunshan University
  • Yudian Cai, Duke Kunshan University
  • Sze chai Kwok, Duke Kunshan University
  • James Moody, Arts & Sciences-Sociology

/graduate Team Members

  • Joseph Quinn, Sociology-PHD, Sociology-PHD

/undergraduate Team Members

  • Jenny Green
  • Chenjun Ji, DKU Interdisciplinary Studies (BS)
  • Yiyang Liu, DKU Interdisciplinary Studies (BS)
  • Victoria Midkiff
  • Kavya Ramamurthy
  • Olivia Shamberger, Neuroscience (BS)
  • Aleksandra Stryjska
  • Kidest Wolde, Philosophy (AB)
  • Anqi Xie
  • Jiayi Xu, Neuroscience (BS)
  • Ruochen Zhang, DKU Interdisciplinary Studies (BS)

/zcommunity Team Members

  • Shanghai Key Laboratory of Magnetic Resonance
  • Advanced Telecommunications Research Institute International