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The role of brain-to-brain synchrony in collective performance
- 1. The role of brain-to-brain synchrony in collective performance Diego Reinero (NYU) SANS 2019, Data Blitz
- 2. “We looked at 180 teams from all over the company. We had lots of data, but there was nothing showing that a mix of specific personality types or skills or backgrounds made any difference” “At Google, we’re good at finding patterns. There weren’t strong patterns here.” Project Aristotle
- 3. Social? Neuroscience the “dark matter of social neuroscience” – Schilbach (2013) ”hot topics for future study” – Stanley & Adolphs (2013)
- 4. Hyperscanning: Brain-to-brain synchrony Hu et al (2017)Dumas et al (2010) Jahng et al (2017) Hu et al (2018)
- 5. coherence 1-20 Hz entraining stimulus Suzanne Dikker Brain-to-Brain Synchrony Classroom EEG Student-to-group neural synchrony social engaging activity Dikker et al 2017 Type of instruction
- 6. Brain-to-brain synchrony’s role in collective performance
- 7. Does brain-to-brain synchrony predict group performance? Team vs. Individual; Problem Solving Tasks Recruited 44 groups of 4 to come to the lab; each person outfitted with mobile EEG headset Data (N = 174, 44 groups of 4) Groups completed same set of problem solving tasks either as a team (cooperatively) or individually (competitively) Tested if teams outperformed individuals and whether brain-to-brain synchrony predicted group performance - - - - > Study Overview - - - - >
- 8. Methods: Manipulation our Valdesolo & DeSteno (2011) Team Individual
- 9. Generate team/individual name Team Individual
- 10. Computerized problem solving tasks Test of Collective Intelligence; Woolley et al (2010) Matrix – 6min Memory – 2min Typing – 6.5min Brainstorm – 2min Unscramble – 4min Sudoku – 3.5min Winter Survival – 10min baseline baseline baseline baseline Group manipulation
- 11. Computerized problem solving tasks Survey • One-Shot Public Goods • Group Identification • Groupiness • Emotion Perception (RMET) • Demographics
- 12. Group Identification & Cooperation t(171.97) = 4.68, p < .001, d = 0.71 t(169.79) = 2.15, p = .033, d = 0.33 M = $8.54 SD = 2.86 M = $7.54 SD = 3.21 M = 0.68 SD = 0.21 M = 0.53 SD = 0.21
- 13. Problem solving tasks Test of Collective Intelligence; Woolley et al (2010) Matrix – 6min Memory – 2min Typing – 6.5min Brainstorm – 2min Unscramble – 4min Sudoku – 3.5min Winter Survival – 10min
- 14. Average Group Performance Score
- 15. Is neural synchrony higher among teams? Does it predict group performance? t(442.72) = 4.16, p < .001, d = 0.19 Interaction effect: b = .24, SE = .11, p = .030
- 16. Group neural synchrony predicts group performance among teams Interaction effect: b = .24, SE = .11, p = .030
- 17. What about how tightly knit a group was?
- 18. NYU Van Bavel Lab Thank you Jay Van Bavel Suzanne Dikker Ido Davidesco Anna Balchunas Simone Van Taylor Anita Woolley Young Ji Kim John Andrew Chwe Brienna Carter Zoe Kleiman Tessa West Melda Kahraman Henry Valk Randi Garcia David Kenny Pascal Wallisch Dana Bevilacqua
Editor's Notes
- Hi, I’m Diego Reinero from NYU and I think watching this clip on loop is oddly satisfying. If you haven’t noticed it already, every player in white - the Phoenix Suns NBA team - moves in perfect unison with shared attention toward the ball and a common goal in mind: to work together to beat their opponent and score at the other end of the court. This synchronicity might be a key toward their success. For decades, researchers have tried to identify ingredients that make up successful teams, yet for a variety of reasons, scientists still know very little about how the brain supports group interactions. And so today I’m going to talk about the role of brain-to-brain synchrony in collective performance.
- Before I talk about the brain I want to step back a few years to a time when Google embarked on an initiative — code-named Project Aristotle — to study hundreds of Google’s teams and figure out what went into building the perfect team. How frequently people ate together If they had the same hobbies or personality styles If they had similar backgrounds or if gender composition mattered And which traits the best managers had in common BUT no matter how they looked at the data, it was almost impossible to find patterns — or any evidence that the composition of a team made any difference. As one of the Google People Analytics mangers said, [CLICK]‘‘We looked at 180 teams from all over the company. ‘We had lots of data, but there was nothing showing that a mix of specific personality types or skills or backgrounds made any difference.” [CLICK]‘‘At Google, we’re good at finding patterns. There weren’t strong patterns here.’’
- And it’s maybe no surprise! Because our understanding of how the human brain supports things like group interactions and team performance has been somewhat of a black box. Our typical ways of studying the brain through fMRI or EEG often remove the social element, usually having one participant at a time perform a task in a somewhat constrained environment. But there’s a richness to studying people and their brains in real-time as they interact that may be useful to explore. As Schilbach notes, this is the dark matter of social neuroscience and as Stanley & Adolphs noted, it's a hot topic for future study.
- In fact, over the past 10 years or so we’ve seen a flurry of research starting to explore the neural dynamics that occur when people actually interact with one another – and this real time recording of brain activity from multiple people at the same time is called hyperscanning. While the field is still in its infancy, and there’s so much we don't know, work so far suggests that brain-to-brain synchrony is associated with: joint attention & motion motor coordination decision making on economic games and appears consistently when people are face-to-face engaging in real social exchanges Across studies there are various ways of measuring neural synchrony, and one method that we've employed is looking at EEG coherence. Dumas (2010): Hand movement synchrony correlates with an interbrain synchronizing network in alpha–mu (7–12 Hz) frequency. Hu (2017): Interbrain coherence (in LMFC) correlated with mutual prosociality, shared intentionality, and perceived similarity. Jahng (2017): Interbrain phase synchronization in alpha band underlies use of facial cues to predict partner’s intention to cooperate or defect. Hu (2018): The results showed a higher cooperation rate and larger theta/alpha-band inter-brain synchrony in condition H-H than in H-M.
- [CLICK] Our team has recently applied portable low-cost EEG technology in the classroom and showed that brain-to-brain coupling between students, [CLICK] quantified as inter-brain coherence from 1-20 Hz, predicts how much kids liked the class and each other. Particularly, there was higher synchrony found when students learned by watching videos (an entraining stimulus) and group discussion (a socially engaging activity).
- Here we extend this to team performance. If people are part of a team and there is interdependence between them to reach a shared goal, we hypothesized that we might see more neural synchrony and that neural synchrony might predict group performance, again owing to the role it might play in tracking joint attention and social coordination.
- So to test the role of brain synchrony in collective performance and in larger groups where a group identity can form, we recruited 44 groups of 4 to the lab and outfitted each person with a mobile EEG headset. Then we randomly assigned groups to either the team or individual condition, and people would complete problem solving tasks either as a team (cooperatively) or individually (competitively). We included an equal number of groups of individuals in part as controls, as they would be doing the exact same tasks and thus have some entrainment to a similar stimulus, but without any social coordination And then we looked at whether brain synchrony could predict group performance.
- In terms of manipulating groups to be in the team or individual condition, we layered a few things intended to encourage group cohesion among teams while fracturing it among the group of individuals. So we gave everyone a monetary incentive but for teams it was to based on overall group performance (so the spoils would be shared), whereas among individuals it was based on individual performance (so the best person could win it all). We also had teams come up with team names, whereas individuals came up with their own code name. And finally we had teams briefly tap in synchrony to an audio beat whereas individuals tapped to themselves to that same audio beat. This physical tapping was again done to promote feelings of similarity among teammates but not so among individuals. Again one of our goals in having these two conditions was to try and match some of the external environmental similarities between the two conditions, but shift the psychological experience from a cooperative team-based one that built rapport, to a competitive individualistic one.
- Here’s an actual screenshot of groups coming up with team names – you can see they are laughing and bonding – and in the individual condition…not so much.
- Finally after the group manipulation, participants begin working on a number of computerized problem solving tasks. These tasks ranged in terms of the type of task – some require creative thinking like brainstorming creative uses of an object, some require simple execution like retyping text, and some require judgment and negotiation like debating with teammates about how to rank-order the importance of items leftover from a plane that crashed in the middle of nowhere during the winter.
- Afterward, everyone completed a few survey measures, including a one-shot Public Goods Game to capture a measure of cooperation, a measure of group identification and dispositional groupiness, the Reading the Mind in the Eyes Test, and demographics.
- Ok so on to the results: As a note, all plots you’ll see, teams will be the blueish color and individuals in red. [once boxplot goes up, can explain– 25th/75th and whiskers extend 1.5 IQR] As these boxplots show, teammates identified more strongly with one another than individuals, which is a nice manipulation check. In addition, teammates also cooperated more on an economic game than individuals. People on teams gave on average $1 more to the public pot despite the one-shot nature of this Public Good Game, where technically it's in everyone's personal best interest to defect and keep all your money and hope to reap the rewards of generous others. This suggests people on teams were more trusting of the others in their group.
- And when we look at group performance for these various tasks…
- Teams outperformed individuals on most problem-solving tasks except those tasks which are prone to process loss (text retyping). Provides discriminant validity for the tasks (sometimes it's clunky to work with a team) Brainstorm Brick: F(1,42) = 88.51, p < .001 Memory Picture: F(1,42) = 19.92, p < .001 Sudoku: F(1,42) = 49.83, p < .001 Unscramble Words: F(1,42) = 56.73, p < .001 Winter Survival (1, 42) = 3.13, p = .084 Individuals do better on Text Retyping Text Retyping: F(1,42) = 119.5, p < .001
- When we look at whether neural synchrony was higher among teams -- we surprisingly didn’t see this. In fact individuals at the group level were ever so slightly higher. This possibly suggests that entrainment to a common stimulus – the time-locked tasks – likely contributes to higher synchrony and that being distracted by other teammates in non-coordinated ways show some reduced synchrony However, neural synchrony did predict performance among teams
- Speculatively, this suggests that interbrain synchrony reflects a mental synergy or shared engagement that facilitates performance when collaboration is allowed Since this didn’t occur in the individual condition, it suggests something beyond just working on the same task individually, but rather taking into account the actions of others and mutual influence.
- And what's really interesting, and actually ran counter to our initial predictions, was that self-reported group identification did NOT predict performance. So this suggests that neural synchrony may predict successful teams where self-report measures may fail to capture behavior, though more work is needed to understand the precise mechanism
- This is the first of hopefully several studies in this vein so stay tuned as we dig deeper . With that, thanks to my collaborators, our funding, and to you all for listening.