Front Hum Neurosci. 2016 Nov 4;10:542. eCollection 2016.

Advice Taking from Humans and Machines: An fMRI and Effective Connectivity Study.

Goodyear K¹, Parasuraman R², Chernyak S³, Madhavan P⁴, Deshpande G⁵, Krueger F⁶.

Author information

1: Center for Alcohol and Addiction Studies, Department of Behavioral and Social Sciences, Brown University, ProvidenceRI, USA; Section on Clinical Psychoneuroendocrinology and Neuropsychopharmacology, National Institute on Alcohol Abuse and Alcoholism and National Institute on Drug Abuse, BethesdaMD, USA.
2: Department of Psychology, George Mason University, Fairfax VA, USA.
3: Molecular Neuroscience Department, George Mason University, Fairfax VA, USA.
4: Institute for Defense Analyses, Alexandria VA, USA.
5: Auburn University MRI Research Center, Department of Electrical & Computer Engineering, Auburn University, AuburnAL, USA; Department of Psychology, Auburn University, AuburnAL, USA; Alabama Advanced Imaging Consortium, Auburn University and University of Alabama, BirminghamAL, USA.
6: Department of Psychology, George Mason University, FairfaxVA, USA; Molecular Neuroscience Department, George Mason University, FairfaxVA, USA.

Abstract

With new technological advances, advice can come from different sources such as machines or humans, but how individuals respond to such advice and the neural correlates involved need to be better understood. We combined functional MRI and multivariate Granger causality analysis with an X-ray luggage-screening task to investigate the neural basis and corresponding effective connectivity involved with advice utilization from agents framed as experts. Participants were asked to accept or reject good or bad advice from a human or machine agent with low reliability (high false alarm rate). We showed that unreliable advice decreased performance overall and participants interacting with the human agent had a greater depreciation of advice utilization during bad advice compared to the machine agent. These differences in advice utilization can be perceivably due to reevaluation of expectations arising from association of dispositional credibility for each agent. We demonstrated that differences in advice utilization engaged brain regions that may be associated with evaluation of personal characteristics and traits (precuneus, posterior cingulate cortex, temporoparietal junction) and interoception (posterior insula). We found that the right posterior insula and left precuneus were the drivers of the advice utilization network that were reciprocally connected to each other and also projected to all other regions. Our behavioral and neuroimaging results have significant implications for society because of progressions in technology and increased interactions with machines.