Abstract
Recognizing a person in a crowded environment is a challenging, yet critical, visual-search task for both humans and machine-vision algorithms. This paper explores the possibility of combining a residual neural network (ResNet), brain-computer interfaces (BCIs) and human participants to create “cyborgs” that improve decision making. Human participants and a ResNet undertook the same face-recognition experiment. BCIs were used to decode the decision confidence of humans from their EEG signals. Different types of cyborg groups were created, including either only humans (with or without the BCI) or groups of humans and the ResNet. Cyborg groups decisions were obtained weighing individual decisions by confidence estimates. Results show that groups of cyborgs are significantly more accurate (up to 35%) than the ResNet, the average participant, and equally-sized groups of humans not assisted by technology. These results suggest that melding humans, BCI, and machine-vision technology could significantly improve decision-making in realistic scenarios.
Highlights
Visual search is the process of looking for an item of interest in a scene
This paper explores the possibility of combining a residual neural network (ResNet), brain-computer interfaces (BCIs) and human participants to create “cyborgs” that improve decision making
We developed a series of hybrid Brain-Computer Interfaces to test whether it is possible to decode the decision confidence of single users from their EEG signals, RTs and other physiological measurements
Summary
Visual search is the process of looking for an item of interest in a scene. Everyone engages in visual search many times every day [1]. Even in this real-world situation, groups of humans assisted by our hBCI were significantly more accurate than traditional groups based on standard majority or reported confidence [41]. We used a state-of-the-art residual deep neural network (ResNet), our BCI estimating the decision confidence using only neural features, and human participants to create different types of cyborgs and investigate whether they can improve decision making and why We evaluated these approaches in the context of the same face-recognition experiment as in [41], where participants had to search an image of a crowded, indoor environment and decide whether or not it contained a target face, a more challenging task than the face identification task used in [42]
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