Biology subjects

Ioumpa, K.

Publications and source records attributed to Ioumpa, K..

2 recordsLinked to original sources

bioRxiv2020

Obeying orders reduces vicarious brain activation towards victim's pain

Past historical events and experimental research have shown complying with the orders from an authority has a strong impact on peoples behaviour. However, the mechanisms underlying how obeying orders influences moral behaviours remain largely unknown. Here, we test the hypothesis that when male and female humans inflict a painful stimulation to another individual, their empathic response is reduced when this action complied with the order of an experimenter (coerced condition) in comparison with being free to decide to inflict that pain (free condition). We observed that even if participants knew that the shock intensity delivered to the victim was exactly the same during coerced and free conditions, they rated the shocks as less painful in the coerced condition. MRI results further indicated that obeying orders reduced activity associated with witnessing the shocks to the victim in the ACC, insula/IFG, TPJ, the MTG and dorsal striatum (including the caudate and the putamen) as well as neural signatures of vicarious pain in comparison with being free to decide. We also observed that participants felt less responsible and showed reduced activity in a multivariate neural guilt signature in the coerced than in the free condition, suggesting that this reduction of neural response associated with empathy could be linked to a reduction of felt responsibility and guilt. These results highlight that obeying orders has a measurable influence on how people perceive and process others pain. This may help explain how peoples willingness to perform moral transgressions is altered in coerced situations.

neuroscience↗

bioRxiv2020

Neuro-computational mechanisms of action-outcome learning under moral conflict

Predicting how actions result in conflicting outcomes for self and others is essential for social functioning. We tested whether Reinforcement Learning Theory captures how participants learn to choose between symbols that define a moral conflict between financial self-gain and other-pain. We tested whether choices are better explained by model-free learning (decisions based on combined historical values of past outcomes), or model-based learning (decisions based on the current value of separately expected outcomes) by including trials in which participants know that either self-gain or other-pain will not be delivered. Some participants favored options benefiting themselves, others, preventing other-pain. When removing the favored outcome, participants instantly altered their choices, suggesting model-based learning. Computational modelling confirmed choices were best described by model-based learning in which participants track expected values of self-gain and other-pain separately, with an individual valuation parameter capturing their relative weight. This valuation parameter predicted costly helping in an independent task. The expectations of self-gain and other-pain were also biased: the favoured outcome was associated with more differentiated symbol-outcome probability reports than the less favoured outcome. FMRI helped localize this bias: signals in the pain-observation network covaried with pain prediction errors without linear dependency on individual preferences, while the ventromedial prefrontal cortex contained separable signals covarying with pain prediction errors in ways that did and did not reflected individual preferences.

neuroscience↗