emotional interactions in human decision-making using eeg hyperscanning

Emotional Interaction in Human Decision Making using EEG Hyperscanning

Kyongsik Yun1, Dongil Chung1, Jaeseung Jeong1, 2, *

1Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST),

Daejeon 305-701, Republic of Korea 2Department of Psychiatry, Columbia College of Physicians and Surgeons, New York, NY 10032 USA

* Correspondence should be addressed to [email protected]

Abstract We used the Ultimatum Game to investigate the social decision making process between two. The Ultimatum Game offers an experimental model to estimate fairness, mind reading, conflict between emotion and cognition, and strategic behavior. We recorded the simultaneous activity from an EEG using 64 scalp electrodes in 26 healthy participants (13 pairs of proposers and responders). The time-frequency analysis and nonlinear interdependence between the two players’ brain regions were then estimated. We found that the face-to-face interactions modulate the Ultimatum Game behavior compared with previous studies. We also found that the high frequency oscillations of the frontocentral region of the brain are closely related to the social interaction. Furthermore, the information flow among the frontocentral areas between the brains is stronger than that of other regions. This is the first study to use to EEGs to analyze temporal dynamics and functional connectivity in human social decision making.

Introduction Interactive social decision making is ubiquitous

in everyday life. Interactive decision making involves goal-directed behavior using cognitive skills such as working memory and executive function. Moreover, this requires the abilities of the mind reading and social cognition.

To investigate social interaction in experimental settings, we used the Ultimatum Game. In the Ultimatum Game, two players, a proposer and a responder, are offered a certain sum of money. The proposer suggests how to split the sum with the responder, and the responder can accept or reject the deal. If the responder accepts the offer, the sum is split as accordingly between the two players. However, if the deal is rejected, neither player receives the money. The rational and optimal solution, as suggested by game theory, is

that the proposer should offer the smallest amount possible and the responder should accept any amount offered. This is not the usual case in human subjects in the empirical settings: the proposer offers what they consider is a fair amount and the responder accepts that they think is a fair amount. Thus, the Ultimatum Game is an apt tool to investigate human emotional interaction, especially fairness monitoring and can focus on irrational behavior that ensues from fairness monitoring.

The purpose of this study is to find and confirm the location of the electrophysiological basis of emotion and cognition (fairness and reason) through the Ultimatum Game. Furthermore, we aim to investigate the social and emotional interaction between the two players by simultaneously recording the players’ EEGs (EEG hyperscanning) (Montague et al. 2002), and then assessing the underlying dynamics and functional connectivity between the brain regions of the two players.

Methods We recorded the simultaneous EEG activities

using 64 scalp electrodes (Quik-cap, Compumedics Neuroscan, USA) in 26 healthy participants (13 pairs of proposers and responders). The electrode positions included the standard 10-20 system locations and additional intermediate positions. The electrode impedance was below 5 kOhm. The EEG was continuously recorded and digitized at a rate of 1000 Hz with a linked mastoids reference. The signal was amplified using SynAmps2 (Compumedics Neuroscan, USA), band-pass filtered at 0.1-300 Hz. The EEG hyperscanning system setup is shown in Figure 1.

ICCS 2008 Proceedings July 27 - 29 ㆍSeoul, korea

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Figure 1: High resolution EEG hyperscanning (simultaneous recording) system.

Subsequently, 60 Hz and 120 Hz notch filters

were applied to minimize line noise artifacts. Trials with strong eye movements or other movement artifacts were manually removed after inspection. The ocular artifact reduction and baseline correction were performed using Scan 4.4 (Compumedics Neuroscan, USA). We selected -2 ~ 2 sec of each proposer offer period and responder decision period.

The reformatted data were then processed using a windowed Fourier transform (window length: 192 ms; step: 20 ms; window overlap: 90%). The signal windows were zero-padded to 512 points to obtain an interpolated frequency resolution of ~1 Hz/frequency bin.

The nonlinear interdependence was also estimated. This is a nonlinear method used to characterize the dynamical interdependence that includes additional information of the strength and direction of the functional connectivity for a bivariate time series regardless of the frequency bands (Breakspear & Terry, 2002).

The correlations between the current offer amount and the next offer amount were determined using the Pearson correlation coefficients. A probability of 0.05 or less was accepted as being significant. A statistical software package (SPSS 11.0.1, SPSS Inc., Chicago, IL, USA) was used.

Results Compared with a previous report (Oosterbeek et

al. 2003) (Figure 2), we found that the amount of money offered by the proposer is decreased. 63.75% of offers were 5:5. Other offer rates (6:4 - 8.75%; 7:3 - 3.75%; 8:2 - 5%; 9:1 - 3.75%) were

significantly decreased compared with the 5:5 offer.

5:5 6:4 7:3 8:2 9:10

10

20

30

40

50

60

70

80

90

100

Offe

r rat

es (%

)

Offer

Figure 2: Distribution of offers according to

each ratio. In the single trial Ultimatum Game, the

responder accepts 5:5 offers at a rate of 100% and 7:3 offers at 55.56%. The acceptance rates of unfair offers such as 8:2 and 9:1 were both 5.56%. Unfair offers were significantly rejected by the responder. However, in series trials of the Ultimatum Game, which require more strategic decision making than the single trials game, the acceptance rates significantly decreased in 7:3 offers and increased in 8:2 offers compared with the single trial game, as shown in figure 3.

5:5 7:3 8:2 9:10

20

40

60

80

100

Acc

epta

nce

rate

s (%

)

Offer

a single trial series trials

Figure 3: Behavioral results from the Ultimatum

Game. These are the offer acceptance rates of the responders averaged over all trials. Each of the 13 responders played both a single trial game and 10

sequential games with one proposer.


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In the sequential interaction, the current offers and the next offers of the proposer have a significant correlation when the current offer was accepted (Figure 4). No significant correlation was found in the current offer that was rejected. The slope of the linear regression is less than one (slope: 0.86), indicating that the proposer tended to offer less money in the next offer compared with the current offer.

0 1 2 3 4 50

1

2

3

4

5

Nex

t offe

r

Current offer

Figure 4: Correlations between the current offer and the next offer in the sequential Ultimatum

Game when the current offer was accepted (Pearson correlation; slope: 0.86, r = 0.73,

p < 0.0001). The time frequency spectrograms were

estimated in the timing of the proposer offer and the timing of the responder decision (Figure 5). The results found that the beta and gamma oscillations were significantly increased in the timing of the decision in both players, especially in the right frontocentral regions (p < 0.01).

The nonlinear interdependence prediction error was calculated for the interaction in the proposer offer. A smaller prediction error from channel X to Y indicates a stronger dependency from X to Y, meaning that the stronger information flow is from Y to X. Figure 6 shows the information flow both within and between each player’s brains. The prediction errors of all 128 EEG channels (each player’s 64 EEG channels) were estimated and only significantly correlated channels were indicated (Table 1). The right frontocentral regions of the proposer and the left frontocentral regions

of the responder are the main regions of information flow in the social interaction.

Proposer’s brain

Responder’s brain

Proposer offer

Responder decision

Figure 5: Time-frequency analysis of the proposer’s and responder’s brain in the right

frontocentral region. Timing of the proposer offer and the responder decision are indicated by the red

dashed lines.

Figure 6: Information flow in the social

interaction in the Ultimatum Game using a nonlinear interdependence index.

Discussion In this study, using the Ultimatum Game, we

found that interpersonal behavior was modulated by face-to-face interactions when compared with previous studies (Oosterbeek et al. 2003; Sanfey et al. 2003). We also found that the high frequency oscillations of the frontocentral regions of the brain were closely related to the social interaction. Furthermore, the information flow among the frontocentral areas between the brains was stronger than that of other regions.

The behavioral results suggest that face-to-face interactions in the Ultimatum Game can affect the players’ social interactions and fairness


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evaluations. Moreover, the single trial game and the sequential games showed different decision behaviors from the responder. The results reflect the different strategies related to the evaluation of reasoning and fairness. In the single trial game, the responder placed more value on the fairness, while the responder in the sequential game thought that reasoning was more important.

Table 1: Information flow between the brain

regions (p < 0.01)

Proposer Responder from to from to

FC4 FC3

FC4 CP1

FC4 P1

CP6 FP1

CP6 C6

C6 CP6

CP6 P6

F5 FC3

FC3 F5

FC3 C1

C1 FC3

FC3 CP1

CP1 FC3

FC3 P1

P1 FC3

The synchronized high oscillations in the right

frontocentral regions of both players indicate that these regions are closely related to social decision making in the Ultimatum Game. The results are consistent with a previous study that proposed that the right dorsolateral prefrontal cortex is the center of the cognitive reasonable decision making process in the Ultimatum Game (Koenigs & Tranel, 2007, Sanfey et al. 2003).

The information flow also showed that the right frontocentral regions were highly correlated with decision making in the two person interaction. The left frontocentral regions of the responder’s brain

sent an information signal to the right frontocentral regions of the proposer’s brain. Further nonlinear interdependence analyses of each offer rate and timing are needed to draw definitive conclusions about the information transmission.

Since there was a strong social interaction between the players, the rejection rate was very low and more than 60% of the proposers offered a fair amount of money (5:5). Moreover, the relatively small size of the subjects and the small rate of rejections limited the statistical power of this study.

The aim of this study was to understand the neural processes in a two person social interaction by measuring the simultaneous electrophysiological activity of the two brains. This is the first study to analyze the temporal dynamics and social interactions in human decision-making using simultaneous EEG recordings. The reported findings provide evidence for behavioral and electrophysiological approaches in social cognition and decision making that stress the fundamental role of the frontal areas in neural networks that support deliberative and emotional processes in human social decision making.

References Breakspear M, Terry JR (2002) Detection and

description of non-linear interdependence in normal multichannel human EEG data. Clinical Neurophysiology 113:735-753.

Koenigs M, Tranel D (2007): Irrational Economic Decision-Making after Ventromedial Prefrontal Damage: Evidence from the Ultimatum Game. Journal of Neuroscience 27:951.

Montague PR, Berns GS, Cohen JD, McClure SM, Pagnoni G, Dhamala M, et al (2002): Hyperscanning: Simultaneous fMRI during Linked Social Interactions. Neuroimage 16:1159-1164.

Oosterbeek, H., Sloof, R., Van de Kuilen, G., (2003) Cultural differences in Ultimatum Game experiments: evidence from a meta-analysis. Experimental Economics 7, 171-188.

Sanfey, A. G., Rilling, J. K., Aronson, J. A., Nystrom, L. E. & Cohen, J. D. (2003) The neural basis of economic decision-making in the Ultimatum Game. Science 300, 1755-8.


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