Collective dynamics support group drumming, reduce variability, and stabilize tempo drift

Humans are social animals who engage in a variety of collective activities requiring coordinated action. Among these, music is a defining and ancient aspect of human sociality. Social interaction has largely been studied in dyadic contexts. The presence of multiple agents engaged in the same task space creates different constraints and possibilities that have been studied more extensively in nonhuman animal behaviour. We addressed whether collective dynamics play a role in human circle drumming. The task was to synchronize in a group with an initial reference pattern and then maintain synchronization after it was muted. We varied the number of drummers, from solo to dyad, quartet, and octet. The observed lower variability, lack of speeding up, smoother individual dynamics, and leader-less inter-personal coordination indicated that stability increased as group size increased, a sort of temporal wisdom of crowds. We propose a hybrid continuous-discrete Kuramoto model for emergent group synchronization with pulse-based coupling that exhibits a mean field positive feedback loop. This research has theoretical implications about collective intentionality and social cognition.

Do low-confidence individuals decrease group judgments’ accuracy? Investigations in terms of the wisdom of crowds framework

In group judgments in a binary choice task, the judgments of individuals with low confidence (i.e., they feel that the judgment was not correct) may be regarded as unreliable. Previous studies have shown that aggregating individuals’ diverse judgments can lead to high accuracy in group judgments, a phenomenon known as the wisdom of crowds. Therefore, if low-confidence individuals make diverse judgments between individuals and the mean of accuracy of their judgments is above the chance level (.50), it is likely that they will not always decrease the accuracy of group judgments. To investigate this issue, the present study conducted behavioral experiments using binary choice inferential tasks, and computer simulations of group judgments by manipulating group sizes and individuals’ confidence levels. Results revealed that (I) judgment patterns were highly similar between individuals regardless of their confidence levels; (II) the low-confidence group could make judgments as accurate as the high-confidence group, as the group size increased; and (III) even if there were low-confidence individuals in a group, they generally did not inhibit group judgment accuracy. The results suggest the usefulness of low-confidence individuals’ judgments in a group and provide practical implications for real-world group judgments.

Do low-confidence individuals decrease group judgments’ accuracy? Investigations in terms of the wisdom of crowds framework

In group judgments in a binary choice task, the judgments of individuals with low confidence (i.e., they feel that the judgment was not correct) may be regarded as unreliable. Previous studies have shown that aggregating individuals’ diverse judgments can lead to high accuracy in group judgments, a phenomenon known as the wisdom of crowds. Therefore, if low-confidence individuals make diverse judgments between individuals and the mean of accuracy of their judgments is above the chance level (.50), it is likely that they will not always decrease the accuracy of group judgments. To investigate this issue, the present study conducted behavioral experiments using binary choice inferential tasks, and computer simulations of group judgments by manipulating group sizes and individuals’ confidence levels. Results revealed that (I) judgment patterns were highly similar between individuals regardless of their confidence levels; (II) the low-confidence group could make judgments as accurate as the high-confidence group, as the group size increased; and (III) even if there were low-confidence individuals in a group, they generally did not inhibit group judgment accuracy. The results suggest the usefulness of low-confidence individuals’ judgments in a group and provide practical implications for real-world group judgments.

Collaboration to improve cross-race face recognition: Wisdom of the multi-racial crowd?

Collaborative "wisdom-of-crowds" decision making improves face identification accuracy over individuals working alone. We examined whether collaboration improves both own- and other-race face identification. In Experiment 1, participants completed an online face-identification task on their own and with a same-race partner (East Asian dyads, N = 27; Caucasian dyad, N = 31). Collaborative decisions were completed as part of a social dyad (completing the task together) and a non-social dyad (individual scores fused independently). Social and non-social collaboration improved own- and other-race face identification accuracy equally. In Experiment 2, we examined the impact of racial diversity on collaboration for different-race dyads (N = 25), East Asian same-race dyads (N = 25), and Caucasian same-race dyads (N = 28). Performance improved equivalently for same- and different-race dyads. Collaboration can be a valuable tool for improving own- and other-race face identification in social and non-social settings.

Will Humans-in-the-Loop Become Borgs? Merits and Pitfalls of Working with AI

We analyze how advice from an AI affects complementarities between humans and AI, in particular what humans know that an AI does not know: “unique human knowledge.” In a multi-method study consisting of an analytical model, experimental studies, and a simulation study, our main finding is that human choices converge toward similar responses improving individual accuracy. However, as overall individual accuracy of the group of humans improves, the individual unique human knowledge decreases. Based on this finding, we claim that humans interacting with AI behave like “Borgs,” that is, cyborg creatures with strong individual performance but no human individuality. We argue that the loss of unique human knowledge may lead to several undesirable outcomes in a host of human–AI decision environments. We demonstrate this harmful impact on the “wisdom of crowds.” Simulation results based on our experimental data suggest that groups of humans interacting with AI are far less effective as compared to human groups without AI assistance. We suggest mitigation techniques to create environments that can provide the best of both worlds (e.g., by personalizing AI advice). We show that such interventions perform well individually as well as in wisdom of crowds settings.