Title
Real-time human-robot interaction underlying neurorobotic trust and intent recognition.
Abstract
In the past three decades, the interest in trust has grown significantly due to its important role in our modern society. Everyday social experience involves “confidence” among people, which can be interpreted at the neurological level of a human brain. Recent studies suggest that oxytocin is a centrally-acting neurotransmitter important in the development and alteration of trust. Its administration in humans seems to increase trust and reduce fear, in part by directly inhibiting the amygdala. However, the cerebral microcircuitry underlying this mechanism is still unknown. We propose the first biologically realistic model for trust, simulating spiking neurons in the cortex in a real-time human–robot interaction simulation. At the physiological level, oxytocin cells were modeled with triple apical dendrites characteristic of their structure in the paraventricular nucleus of the hypothalamus. As trust was established in the simulation, this architecture had a direct inhibitory effect on the amygdala tonic firing, which resulted in a willingness to exchange an object from the trustor (virtual neurorobot) to the trustee (human actor). Our software and hardware enhancements allowed the simulation of almost 100,000 neurons in real time and the incorporation of a sophisticated Gabor mechanism as a visual filter. Our brain was functional and our robotic system was robust in that it trusted or distrusted a human actor based on movement imitation.
Year
DOI
Venue
2012
10.1016/j.neunet.2012.02.029
Neural Networks
Keywords
Field
DocType
Real-time computing,Virtual neurorobotics,Learning,Trust and intent recognition
Robotic systems,Tonic (music),Computer security,Cognitive science,Amygdala,Human brain,Artificial intelligence,Imitation,Accident prevention,Mathematics,Machine learning,Human–robot interaction
Journal
Volume
Issue
ISSN
32
1
0893-6080
Citations 
PageRank 
References 
0
0.34
13
Authors
8