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Soc Neurosci. 2017 Oct;12(5):570-581. doi: 10.1080/17470919.2016.1205131. Epub 2016 Jul 13.

An fMRI and effective connectivity study investigating miss errors during advice utilization from human and machine agents.

Author information

a Molecular Neuroscience Department , George Mason University , Fairfax , VA , USA.
b Department of Psychology , George Mason University , Fairfax , VA , USA.
c Human Factors and UX Research , Perceptronics Solutions, Inc ., Falls Church , VA , USA.
d Board on Human-Systems Integration , National Academies of Sciences, Engineering and Medicine , Washington , DC , USA.
e Auburn University MRI Research Center, Department of Electrical and Computer Engineering , Auburn University , Auburn , AL , USA.
f Department of Psychology , Auburn University , Auburn , AL , USA.
g Alabama Advanced Imaging Consortium , Auburn University and University of Alabama , Birmingham , AL , USA.


As society becomes more reliant on machines and automation, understanding how people utilize advice is a necessary endeavor. Our objective was to reveal the underlying neural associations during advice utilization from expert human and machine agents with fMRI and multivariate Granger causality analysis. During an X-ray luggage-screening task, participants accepted or rejected good or bad advice from either the human or machine agent framed as experts with manipulated reliability (high miss rate). We showed that the machine-agent group decreased their advice utilization compared to the human-agent group and these differences in behaviors during advice utilization could be accounted for by high expectations of reliable advice and changes in attention allocation due to miss errors. Brain areas involved with the salience and mentalizing networks, as well as sensory processing involved with attention, were recruited during the task and the advice utilization network consisted of attentional modulation of sensory information with the lingual gyrus as the driver during the decision phase and the fusiform gyrus as the driver during the feedback phase. Our findings expand on the existing literature by showing that misses degrade advice utilization, which is represented in a neural network involving salience detection and self-processing with perceptual integration.


Expert advice; Granger causality; effective connectivity; errors; functional magnetic resonance imaging (fMRI)

[Indexed for MEDLINE]

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