Dec 13, 2023
The neurobiology of unconscious bias
by Robert Blum
In the 3rd EO online meeting, the CRC ReTune discussed neurobiological aspects of unconscious bias. To start the discussion, Prof. Robert Blum, member of the EO committee, gave a short presentation about the neurobiology of unconscious memories. First, he defined the unconscious bias as an unintentional and automatic mental association based on stereotypes (gender / race) stemming from traditions, norms, values, culture, and experience. This raises the question whether these associative memory traces in our brain are‚ hardwired emotions or were established by associative learning processes. Robert showed that early research on unconscious bias was very much influenced by research on the neurobiology of emotions, defense behavior and implicit memory.
The discovery of conscious and unconscious emotional learning in the human ‘fear center’, the amygdala, motivated researchers to use functional brain imaging techniques to look for a neurobiological correlate of the unconscious bias. A highly cited study showed that amygdala activation was correlated with implicit association tasks for race. Furthermore, brain responses could be correlated with biological read outs for implicit memory processing such as the startle reaction. Such studies identified the amygdala as a brain area where memory elements of the implicit prejudice are processed. Robert pointed out that the role of the amygdala in prejudice and other social processes is still not clear. For instance, many studies conclude that amygdala activation during the presentation of racial or gender-associated stimuli is unlikely to reflect the processing of negative or biased information. Furthermore, Robert was referring to the field of emotion research. Here, researchers were asked to better distinguish between processes that give rise to conscious feelings (e.g. negative emotions) and non-conscious processes that control defense responses elicited by threats These processes interact but are not the same (see recent articles by Joseph LeDoux). Robert summed up that a predictive neurologic signature pattern or intergroup social cognition underlying an unconscious bias brain activity (gender / race / others) has not been found.
The brain evokes social cognitive processes, which can result in biased social behaviors in intergroup contexts. Thus, the question is whether this is malleable. Indeed, a few studies showed that learning against implicit automatic biases is possible. From a neurobiological perspective, Robert showed that prejudice reduction learning against automatic biases is an extinction learning-like process. During extinction learning, a memory is not ‘extinguished’ or ‘erased’ but modified by a newly learned memory trace. Such acquired extinction memories are typically context-dependent. Literature evidence was presented showing that especially contextual manipulations produce reductions in implicit manifestations of prejudice and stereotyping. In summary, the talk was concluded with some statements: 1) The neurobiology of unconscious biases is poorly understood. 2) Bias processing seem to be largely context-dependent, fast, automatic, and implicit. 3) Neural circuits activated during unconscious bias processing are not predictive and are likely to reflect social cognition of in-group or out-group members. 4) Unconscious biases are malleable. Effective seem to be the modulation of existing biases by context-changes. However, such context-dependent memories might not be stable. In summary, a stable gender-equal context might help to reduce the unconscious gender bias and might help to improve gender equality. This hypothesis can be tested with gender equality measures. How such a gender equal context must look like needs to be investigated with scientific methods.
Amodio, D.M. (2014). The neuroscience of prejudice and stereotyping. Nature reviews. Neuroscience 15, 670-682. 10.1038/nrn3800.
Coe, I.R., Wiley, R., and Bekker, L.G. (2019). Organisational best practices towards gender equality in science and medicine. Lancet 393, 587-593. 10.1016/S0140-6736(18)33188-X.
Haynes, J.D., and Rees, G. (2006). Decoding mental states from brain activity in humans. Nature reviews. Neuroscience 7, 523-534. 10.1038/nrn1931.
Izuma, K., Aoki, R., Shibata, K., and Nakahara, K. (2019). Neural signals in amygdala predict implicit prejudice toward an ethnic outgroup. NeuroImage 189, 341-352. 10.1016/j.neuroimage.2019.01.019.
LeDoux, J., and Daw, N.D. (2018). Surviving threats: neural circuit and computational implications of a new taxonomy of defensive behaviour. Nature reviews. Neuroscience 19, 269-282. 10.1038/nrn.2018.22.
LeDoux, J.E. (2014). Coming to terms with fear. Proceedings of the National Academy of Sciences of the United States of America 111, 2871-2878. 10.1073/pnas.1400335111.
Merritt, C.C., MacCormack, J.K., Stein, A.G., Lindquist, K.A., and Muscatell, K.A. (2021). The neural underpinnings of intergroup social cognition: an fMRI meta-analysis. Soc Cogn Affect Neurosci 16, 903-914. 10.1093/scan/nsab034.
Molenberghs, P. (2013). The neuroscience of in-group bias. Neurosci Biobehav Rev 37, 1530-1536. 10.1016/j.neubiorev.2013.06.002.
Morris, J.S., Ohman, A., and Dolan, R.J. (1998). Conscious and unconscious emotional learning in the human amygdala. Nature 393, 467-470. 10.1038/30976.
Phelps, E.A., Delgado, M.R., Nearing, K.I., and LeDoux, J.E. (2004). Extinction learning in humans: role of the amygdala and vmPFC. Neuron 43, 897-905. 10.1016/j.neuron.2004.08.042.
Phelps, E.A., O’Connor, K.J., Cunningham, W.A., Funayama, E.S., Gatenby, J.C., Gore, J.C., and Banaji, M.R. (2000). Performance on indirect measures of race evaluation predicts amygdala activation. Journal of cognitive neuroscience 12, 729-738. 10.1162/089892900562552.
Rosler, I.K., and Amodio, D.M. (2022). Neural Basis of Prejudice and Prejudice Reduction. Biol Psychiatry Cogn Neurosci Neuroimaging 7, 1200-1208. 10.1016/j.bpsc.2022.10.008.
Saarinen, A., Jaaskelainen, I.P., Harjunen, V., Keltikangas-Jarvinen, L., Jasinskaja-Lahti, I., and Ravaja, N. (2021). Neural basis of in-group bias and prejudices: A systematic meta-analysis. Neurosci Biobehav Rev 131, 1214-1227. 10.1016/j.neubiorev.2021.10.027.
Tovote, P., Fadok, J.P., and Luthi, A. (2015). Neuronal circuits for fear and anxiety. Nature reviews. Neuroscience 16, 317-331. 10.1038/nrn3945.
Van Bavel, J.J., Packer, D.J., and Cunningham, W.A. (2008). The neural substrates of in-group bias: a functional magnetic resonance imaging investigation. Psychol Sci 19, 1131-1139. 10.1111/j.1467-9280.2008.02214.x.