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SUMMARY:Neural Adaptation Underlies Escalation in Dishonesty - Neil Garret
 t\, University College London
DTSTART:20160125T141500Z
DTEND:20160125T151500Z
UID:TALK63989@talks.cam.ac.uk
CONTACT:Paula Kaanders
DESCRIPTION:Neural adaptation is a change over time in the responsiveness 
 of neurons to a stimulus or situation. It has been extensively studied in 
 sensory circuits\, yet its consequences to social behaviour and choice is 
 relatively unknown. Here we demonstrate how this fundamental property of t
 he human brain explains a phenomenon with overreaching societal impact –
  escalation of corrupt behaviour. We combined brain imaging with a behavio
 ural task in which individuals can repeatedly and voluntarily act dishones
 tly without being required to admit so. Specifically\, on each trial parti
 cipants advised a second participant regarding the amount of British Penni
 es in a glass jar. On some trials dishonesty was advantageous for the part
 icipant\, while on others honesty was. The specific task structure enabled
  us to estimate the amount by which a participant was lying on each and ev
 ery trial. Under this controlled laboratory setting we demonstrate that th
 e extent to which participants engaged in dishonesty grows over time. Usin
 g fMRI we show that BOLD signal reduction in the amygdala is sensitive to 
 the history of dishonest choices. Critically\, we provide evidence that su
 ch adaptation acts as a teaching signal\, predicting subsequent escalation
 s of corruption. Real world examples in which corruption escalates are ple
 ntiful\; from scientific misconduct to financial fraud\, plagiarism and in
 fidelity. Our findings suggest that despite being trivial at the outset\, 
 engagement in small immoral acts can trigger a biological process that gen
 erates a "slippery slope": what begins as small acts escalate into larger 
 instances with harmful consequences for those at the receiving end.
LOCATION:Kenneth Craik Room\, Craik Marshall Building\, Downing Site\, Cam
 bridge
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