Abstract
Groups have increased sensing and cognition capabilities that typically allow them to make better decisions. However, factors such as communication biases and time constraints can lead to less-than-optimal group decisions. In this study, we use a hybrid Brain-Computer Interface (hBCI) to improve the performance of groups undertaking a realistic visual-search task. Our hBCI extracts neural information from EEG signals and combines it with response times to build an estimate of the decision confidence. This is used to weigh individual responses, resulting in improved group decisions. We compare the performance of hBCI-assisted groups with the performance of non-BCI groups using standard majority voting, and non-BCI groups using weighted voting based on reported decision confidence. We also investigate the impact on group performance of a computer-mediated form of communication between members. Results across three experiments suggest that the hBCI provides significant advantages over non-BCI decision methods in all cases. We also found that our form of communication increases individual error rates by almost 50% compared to non-communicating observers, which also results in worse group performance. Communication also makes reported confidence uncorrelated with the decision correctness, thereby nullifying its value in weighing votes. In summary, best decisions are achieved by hBCI-assisted, non-communicating groups.
Highlights
Given the negative impact that the interaction of group members can have on decisions in some circumstances, one may wonder whether we could use technology to obtain the advantages of groups without member interactions
We recently developed a hybrid BCI that used a combination of EEG neural signals and response times (RTs) to estimate the decision confidence of group members[14], and, the accuracy of each response
In odd-sized groups, hybrid BCI (hBCI) was able to achieve significant superior performance over all other methods for m = 3 (WSR p < 0.003) and it was performing on par with majority-based groups for m = 5,7,9
Summary
Given the negative impact that the interaction of group members can have on decisions in some circumstances, one may wonder whether we could use technology to obtain the advantages of groups without member interactions. It is plausible to think, that group decisions could be partly based on the integration of neural activity of the group’s members, in circumstances that require rapid decisions This idea has recently been explored with collaborative Brain-Computer Interfaces (cBCIs) by Eckstein et al.[12], where the brain activity of up to 20 group members asked to discriminate between rapidly presented pictures of cars and faces was aggregated to obtain group decisions. CBCI-assisted groups were always inferior to corresponding non-BCI groups To address this limitation, we recently developed a hybrid BCI (hBCI) that used a combination of EEG neural signals and response times (RTs) to estimate the decision confidence of group members[14], and, the accuracy of each response. Similar results were later achieved with a more demanding visual-search task[21] While these results are very encouraging, they are not surprising given that the hBCI estimates and uses the decision confidence to weigh individual decisions. Research has shown that humans do not always report high values of confidence where their decisions are more likely to be correct and vice versa[22], e.g., overconfident people may report high values of confidence when they are likely to be wrong[23, 24]
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