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Collaborative brain-computer interface for aiding decision-making.

Poli R, Valeriani D, Cinel C - PLoS ONE (2014)

Bottom Line: We then built a composite neuro-behavioural feature which optimally combines the two measures.For group decisions, we uses a majority rule and three rules which weigh the decisions of each observer based on response times and our neural and neuro-behavioural features.Results indicate that the integration of behavioural responses and neural features can significantly improve accuracy when compared with the majority rule.

View Article: PubMed Central - PubMed

Affiliation: Brain-Computer Interfaces Lab, School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom.

ABSTRACT
We look at the possibility of integrating the percepts from multiple non-communicating observers as a means of achieving better joint perception and better group decisions. Our approach involves the combination of a brain-computer interface with human behavioural responses. To test ideas in controlled conditions, we asked observers to perform a simple matching task involving the rapid sequential presentation of pairs of visual patterns and the subsequent decision as whether the two patterns in a pair were the same or different. We recorded the response times of observers as well as a neural feature which predicts incorrect decisions and, thus, indirectly indicates the confidence of the decisions made by the observers. We then built a composite neuro-behavioural feature which optimally combines the two measures. For group decisions, we uses a majority rule and three rules which weigh the decisions of each observer based on response times and our neural and neuro-behavioural features. Results indicate that the integration of behavioural responses and neural features can significantly improve accuracy when compared with the majority rule. An analysis of event-related potentials indicates that substantial differences are present in the proximity of the response for correct and incorrect trials, further corroborating the idea of using hybrids of brain-computer interfaces and traditional strategies for improving decision making.

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Average percentage of errors vs group size for the four methods for group decisions tested in this paper (top) and average time required for groups of each size to make a decision (bottom).The plots also show error-bars representing the standard error of the mean for each group size, except for groups of size 10 for which this cannot be computed as only one measurement is available. Statistical comparisons for the error rates shown in the top plot are detailed in Tables 4 and 5 and are represented graphically in Figure 7.
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pone-0102693-g005: Average percentage of errors vs group size for the four methods for group decisions tested in this paper (top) and average time required for groups of each size to make a decision (bottom).The plots also show error-bars representing the standard error of the mean for each group size, except for groups of size 10 for which this cannot be computed as only one measurement is available. Statistical comparisons for the error rates shown in the top plot are detailed in Tables 4 and 5 and are represented graphically in Figure 7.

Mentions: In Figure 5(top), we report the average percentage of errors as a function of group size for the four methods for group decisions tested in the paper. The data are also reported in numerical form in Table 3. As one can see, in all methods studied except that using majority rule for groups of size 2, group decisions were superior to the decisions of single observers (we will look at the statistical significance of this finding shortly), suggesting that integration of perceptual information across non-communicating observers is possible and beneficial. Also, we see that the straight majority is generally outperformed by the other three methods. This is particularly evident with groups having an even number of members where the coin-tossing required by majority rule in the presence of ties implies that performance is the same as that of groups with one fewer member. The data also show that of the three other methods, the RTnf-based method appears to be the most consistent, being best or second best in 9 out of 10 cases. The data also suggest that with large group sizes (from 7 upward) the performance of majority starts saturating possibly to a worse asymptote than the performance of the methods based on confidence correlates.


Collaborative brain-computer interface for aiding decision-making.

Poli R, Valeriani D, Cinel C - PLoS ONE (2014)

Average percentage of errors vs group size for the four methods for group decisions tested in this paper (top) and average time required for groups of each size to make a decision (bottom).The plots also show error-bars representing the standard error of the mean for each group size, except for groups of size 10 for which this cannot be computed as only one measurement is available. Statistical comparisons for the error rates shown in the top plot are detailed in Tables 4 and 5 and are represented graphically in Figure 7.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4114490&req=5

pone-0102693-g005: Average percentage of errors vs group size for the four methods for group decisions tested in this paper (top) and average time required for groups of each size to make a decision (bottom).The plots also show error-bars representing the standard error of the mean for each group size, except for groups of size 10 for which this cannot be computed as only one measurement is available. Statistical comparisons for the error rates shown in the top plot are detailed in Tables 4 and 5 and are represented graphically in Figure 7.
Mentions: In Figure 5(top), we report the average percentage of errors as a function of group size for the four methods for group decisions tested in the paper. The data are also reported in numerical form in Table 3. As one can see, in all methods studied except that using majority rule for groups of size 2, group decisions were superior to the decisions of single observers (we will look at the statistical significance of this finding shortly), suggesting that integration of perceptual information across non-communicating observers is possible and beneficial. Also, we see that the straight majority is generally outperformed by the other three methods. This is particularly evident with groups having an even number of members where the coin-tossing required by majority rule in the presence of ties implies that performance is the same as that of groups with one fewer member. The data also show that of the three other methods, the RTnf-based method appears to be the most consistent, being best or second best in 9 out of 10 cases. The data also suggest that with large group sizes (from 7 upward) the performance of majority starts saturating possibly to a worse asymptote than the performance of the methods based on confidence correlates.

Bottom Line: We then built a composite neuro-behavioural feature which optimally combines the two measures.For group decisions, we uses a majority rule and three rules which weigh the decisions of each observer based on response times and our neural and neuro-behavioural features.Results indicate that the integration of behavioural responses and neural features can significantly improve accuracy when compared with the majority rule.

View Article: PubMed Central - PubMed

Affiliation: Brain-Computer Interfaces Lab, School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom.

ABSTRACT
We look at the possibility of integrating the percepts from multiple non-communicating observers as a means of achieving better joint perception and better group decisions. Our approach involves the combination of a brain-computer interface with human behavioural responses. To test ideas in controlled conditions, we asked observers to perform a simple matching task involving the rapid sequential presentation of pairs of visual patterns and the subsequent decision as whether the two patterns in a pair were the same or different. We recorded the response times of observers as well as a neural feature which predicts incorrect decisions and, thus, indirectly indicates the confidence of the decisions made by the observers. We then built a composite neuro-behavioural feature which optimally combines the two measures. For group decisions, we uses a majority rule and three rules which weigh the decisions of each observer based on response times and our neural and neuro-behavioural features. Results indicate that the integration of behavioural responses and neural features can significantly improve accuracy when compared with the majority rule. An analysis of event-related potentials indicates that substantial differences are present in the proximity of the response for correct and incorrect trials, further corroborating the idea of using hybrids of brain-computer interfaces and traditional strategies for improving decision making.

Show MeSH