Interpersonal alignment of neural evidence accumulation to social exchange of confidence
- Department of General Psychology and Education, Ludwig Maximillian University, Germany
- Faculty of Computer Engineering, Shahid Rajaee Teacher Training University, Islamic Republic of Iran
- Graduate School of Systemic Neurosciences, Ludwig Maximilian University Munich, Germany
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Islamic Republic of Iran
- Wellcome Centre for Human Neuroimaging, University College London, United Kingdom
- Max Planck UCL Centre for Computational Psychiatry and Aging Research, University College London, United Kingdom
- Institute for Convergent Science and Technology, Sharif University of Technology, Islamic Republic of Iran
- Centre for Adaptive Rationality, Max Planck Institute for Human Development, Germany
Figures
Figure 1 with 4 supplements
Experiment paradigm and behavioral results.
(a) Timeline of trials in isolated (top) and social (bottom) conditions. After stimulus presentation, subjects reported their decision and confidence simultaneously by clicking on 1 of the 12 …
Figure 1—figure supplement 1
Accuracy and confidence of the computer generated partners (CGPs).
Confidence is plotted in blue and accuracy is plotted in red. (a) Study 1 – HAHC: high accuracy and high confidence. HALC: high accuracy and low confidence. LAHC: low accuracy and high confidence. …
Figure 1—figure supplement 2
Statistical analysis of the confidence matching effect.
(a) Top: Permutation test. The empirically observed difference in mean confidence (red line) is significantly different from the distribution of the expected mean (black curve and dotted line) under …
Figure 1—figure supplement 3
Examination of the hypothesis that the partner’s confidence at trial t modulates the participant behavior at trial t+1.
Probability correct: First row, confidence: Second row and reaction time (RT): last row. Study 1: first column. Study 2: second column. We used a generalized linear mixed model (GLMM) similar to …
Figure 1—figure supplement 4
Summary of debriefing results of the second study.
(a) Participants felt that their partner was more confident when facing with a high confidence agent (HCA). This means our manipulation indeed worked. (b) Similar to (a) but for accuracy. Here, …
Figure 2 with 2 supplements
Pupil size during inter-trial interval (ITI) under pairing conditions in the social context when participant was paired with a high (HCA) or low confidence (LCA) agent.
Normalized pupil diameter aligned to start of ITI period (t=0). Vertical dashed lines show average ITI duration. The shaded areas are one standard deviation of ITI period in each condition. Inset …
Figure 2—figure supplement 1
Pupil size correlates with participant’s own confidence in the isolated condition.
(a) In study 1, when confidence was lowest (i.e. rated 1) pupil size was larger (orange curve) than highest confidence (rated 6, magenta curve). The shaded area indicates the average inter-trial …
Figure 2—figure supplement 2
Time series analysis of pupil size during inter-trial interval.
For each study, we employed a generalized linear mixed model (GLMM) Pupil(t)=b0+b1*socialCondition where socialCondition is high confidence agent (HCA) = 1 or low confidence agent (LCA) = 2. The …
Figure 3 with 8 supplements
Neural attractor model.
(a) Left: A common top-down (Wx) current drives both populations, each selective for a different choice alternative. Right: A schematic illustration of the impact of a positive top-down drive on …
Figure 3—figure supplement 1
Confidence matching without removing the correlation with the shared stimulus coherence.
Figure 3—figure supplement 2
The effect of top-down current on the attractor network.
The results of model simulations with a specific value of top-down current (Wx = 0.003). This plot shows the average accumulated evidence of the model in 1000 repetitions with (solid lines) and …
Figure 3—figure supplement 3
Model performance regarding different confidence representations.
(a) Confidence representation based on Equation 15. (b) Same as (a) but here confidence is calculated as the absolute difference of the winner and loser signal but only at the end of the stimulus …
Figure 3—figure supplement 4
Model comparison.
We fit each model to the data from high confidence agent (HCA) and low confidence agent (LCA) blocks of each subject (N=15 (subjects) * 2 (blocks)=30). (a) Pie chart indicates the distribution of …
Figure 3—figure supplement 5
Model vs data.
(a–c) Correspondence between behavioral data (black circles, 40 trials per coherence level in the isolated session) and the model fits (red curves, simulation with 1000 trials per coherence level) …
Figure 3—figure supplement 6
The speed of confidence matching.
(a) Empirical data depicting the time course of confidence matching. The difference between the subject’s confidence and that of the agent (y-axis) are averaged within a three-trial time window and …
Figure 3—figure supplement 7
Model falsification.
(a) Top-down model. We simulated two versions of the model (2000 trials per coherence level) in which only top-down current was different between conditions (TD in high confidence agent [HCA] is …
Figure 3—figure supplement 8
Model predictions for confidence matching are not sensitive to linearity assumptions.
(a) Replication of Figure 3b of the main text in which two models are coupled linearly (see Equation 18) and show confidence matching. (b) Same as (a) but with quadratic coupling. In order to show …
Figure 4 with 5 supplements
Coupling of neural evidence accumulation to social exchange of information.
(a) Centroparietal positivity (CPP) component in the isolated condition: event-related potentials are time-locked to stimulus onset, binned for high and low levels of coherency (for study 1, low: …
Figure 4—figure supplement 1
Electrode placement in each study.
Figure 4—figure supplement 2
Relation of EEG signals from centropartial area of the brain to coherence levels and social conditions.
Top-left, ramping activities of the signals (calculated by a linear regression of signals amplitudes and the time windows of 0–500 ms) is modulated by coherence levels (generalized linear mixed …
Figure 4—figure supplement 3
Simulated slope of the accumulator activity in our computational model in low confidence agent (LCA) and high confidence agent (HCA) conditions.
(a) Slope of the winning accumulator (time window: 0–500 ms; shaded area, insets) at each coherence level for LCA and HCA condition. (b) Same as panel (a) but here for the difference in accumulator …
Figure 4—figure supplement 4
Response-locked EEG signal separated for high vs low coherence levels.
(a) As expected from previous studies (Kelly and O’Connell, 2013; Loughnane et al., 2018; O’Connell et al., 2018; Vafaei Shooshtari et al., 2019) centropareital positivity (CPP) signals to high vs …
Figure 4—figure supplement 5
Power calculation (Monte Carlo simulation) for EEG slope effect (Figure 4 in the main manuscript).
Our power calculator suggests we need 17 participants. EEG slope effect was the only effect that was not statistically significant in the first study.
Author response image 1
Calibration plot for the experimental setup.
Average pupil size (arbitrary units from eyelink device) is plotted against display luminance. The plot is obtained by presenting the participant with uniform full screen displays with 10 different …
Tables
Table 1
Details of statistical results in behavioral data (Figure 1).
| Response | Regressors | Estimate | SE | CI | t-Stat | p-Value | Total number | |
|---|---|---|---|---|---|---|---|---|
| Study 1 | Accuracy (HC vs LC) | Coherency | 0.007 | 0.0006 | [0.006 0.008] | 11.57 | <0.001 | 9600 |
| Condition | –0.002 | 0.021 | [–0.045 0.04] | –0.1 | 0.92 | 9600 | ||
| Confidence (HC vs LC) | Coherency | 0.0475 | 0.0008 | [0.046 0.049] | 56.5 | <0.001 | 9600 | |
| Condition | 1.361 | 0.03 | [1.31 1.42] | 46.4 | <0.001 | 9600 | ||
| RT (HC vs LC) | Coherency | –0.005 | 0.0001 | [–0.005 –0.004] | –44.4 | <0.001 | 9600 | |
| Condition | 0.029 | 0.004 | [–0.035 –0.021] | 7.85 | <0.001 | 9600 | ||
| Study 2 | Accuracy (HC vs LC) | Coherency | 0.0209 | 0.0016 | [0.017 0.024] | 13.23 | <0.001 | 6000 |
| Condition | –0.0092 | 0.0296 | [–0.067 0.049] | –0.31 | 0.76 | 6000 | ||
| Confidence (HC vs LC) | Coherency | 0.1011 | 0.1011 | [0.097 0.106] | 47.47 | <0.001 | 6000 | |
| Condition | 0.496 | 0.037 | [0.42 0.56] | 13.32 | <0.001 | 6000 | ||
| RT (HC vs LC) | Coherency | –0.009 | 0.0003 | [–0.01 –0.008] | –26.22 | <0.001 | 6000 | |
| Condition | 0.0363 | 0.006 | [0.024 0.048] | 6.12 | <0.001 | 6000 |
Table 2
Details of statistical results in pupil data (Figure 2).
| Response | Regressors | Estimate | SE | CI | t-Stat | p-Value | Total number | |
|---|---|---|---|---|---|---|---|---|
| Study 1 | Pupil | Condition | –0.038 | 0.011 | [–0.06 –0.01] | –3.30 | <0.001 | 8390 |
| Study 2 | Pupil | Condition | –0.066 | 0.015 | [–0.09 –0.04] | –4.37 | <0.001 | 5842 |
Table 3
Details of statistical results in EEG data (Figure 4).
| Response | Regressors | Estimate | SE | CI | t-Stat | p-Value | Total number | |
|---|---|---|---|---|---|---|---|---|
| Study 1 | EEG slope | Coherency | 0.62 | 0.065 | [0.49. 074] | 9.64 | <0.001 | 6492 |
| Condition | 0.2 | 0.14 | [-0.07 0.49] | 1.42 | 0.15 | 6492 | ||
| Study 2 | EEG slope | Coherency | 0.8 | 0.29 | [0.24 1.37] | 2.8 | <0.01 | 5367 |
| Condition | 1.52 | 0.63 | [0.27 2.77] | 2.39 | 0.017 | 5367 |
Table 4
Details of statistical results in EEG data (Figure 4—figure supplement 2 top row).
| Response | Regressors | Estimate | SE | CI | t-Stat | p-Value | Total number | |
|---|---|---|---|---|---|---|---|---|
| Study 1 | EEG slope | Coherency | 0.02 | 0.005 | [0.01 0.03] | 4.48 | <0.001 | 1523 |
| Study 2 | EEG slope | Coherency | 0.06 | 0.02 | [0.01 0.11] | 2.54 | <0.01 | 2822 |
Table 5
Details of statistical results for the impact of previous trial (Figure 1—figure supplement 3).
| Response | Regressors | Estimate | SE | CI | t-Stat | p-Value | Total number | |
|---|---|---|---|---|---|---|---|---|
| Study 1 | Accuracy (HC vs LC) | Coherency | 0.007 | 0.0006 | [0.006 0.008] | 11.58 | <0.001 | 9600 |
| Conf (t–1) | –0.0017 | 0.005 | [–0.01 0.01] | –0.28 | 0.77 | 9600 | ||
| Confidence (HC vs LC) | Coherency | 0.047 | 0.001 | [0.045, 0.049] | 54.7 | <0.001 | 9600 | |
| Conf (t–1) | 0.32 | 0.008 | [0.3 0.33] | 38.31 | <0.001 | 9600 | ||
| RT (HC vs LC) | Coherency | –0.005 | 0.0001 | [–0.0048 0.0044] | –44.36 | <0.001 | 9600 | |
| Conf (t–1) | –0.0055 | 0.001 | [–0.007 –0.003] | –5.44 | <0.001 | 9600 | ||
| Study 2 | Accuracy (HC vs LC) | Coherency | 0.02 | 0.002 | [0.02 0.024] | 13.23 | <0.001 | 6000 |
| Conf (t–1) | 0.003 | 0.008 | [–0.012 0.018] | 0.37 | 0.7 | 6000 | ||
| Confidence (HC vs LC) | Coherency | 0.1 | 0.002 | [0.097 0.0106] | 47.2 | <0.001 | 6000 | |
| Conf (t–1) | 0.09 | 0.01 | [0.07 0.11] | 8.6 | <0.001 | 6000 | ||
| RT (HC vs LC) | Coherency | –0.009 | 0.0003 | [–0.001 –0.008] | –26.2 | <0.001 | 6000 | |
| Condition | 0.005 | 0.001 | [0.001 0.008] | 2.98 | <0.01 | 6000 |
Table 6
The rate of trial rejection of eye tracking (only data of social) and EEG data (visual inspection) per participant.
| Participants | Eye tracking rejection % (social) | EEG trial rejection % (visual) | |
|---|---|---|---|
| Study 1 (Discovery) | 1 | 12.25 | 4.6 |
| 2 | 12.87 | 31.1 | |
| 3 | 0.5 | 22.1 | |
| 4 | 4 | 14.8 | |
| 5 | 1.37 | 34.4 | |
| 6 | 0 | 4.6 | |
| 7 | 7.75 | 8.8 | |
| 8 | 0.37 | 24.4 | |
| 9 | 6.37 | 7.6 | |
| 10 | 0 | 46 | |
| 11 | 0.12 | NA | |
| 12 | NA | NA | |
| Study 2 (Replication) | 1 | 0 | 4 |
| 2 | 1.25 | 1 | |
| 3 | 5.75 | 8.5 | |
| 4 | 0.5 | 3 | |
| 5 | 1 | 16 | |
| 6 | 1.5 | 2.5 | |
| 7 | 0 | 0.5 | |
| 8 | 1.5 | 9 | |
| 9 | 0 | 2 | |
| 10 | 1 | 4 | |
| 11 | 1 | 7.5 | |
| 12 | 0.5 | 0 | |
| 13 | 0.75 | 10.5 | |
| 14 | 2.5 | 12 | |
| 15 | 14.75 | 4.5 |
Table 7
Generalized linear mixed model (GLMM) including interaction terms (p-values are reported).
| Response | Coherence | Condition (LC vs HC) | Condition* coherence | |
|---|---|---|---|---|
| Study 1 | Accuracy | p<0.001 | p=0.92 | p=0.96 |
| Confidence | p<0.001 | p<0.001 | p<0.001 | |
| RT | p<0.001 | p<0.001 | p<0.05 | |
| Pupil | p=0.43 | p=0.20 | p=0.31 | |
| EEG slope | p<0.01 | p=0.15 | p=0.91 | |
| Study 2 | Accuracy | p<0.001 | p=0.75 | p=0.87 |
| Confidence | p<0.001 | p<0.001 | p<0.001 | |
| RT | p<0.001 | p<0.001 | p=0.34 | |
| Pupil | p=0.35 | p=0.06 | p=0.17 | |
| EEG slope | p=0.62 | p<0.05 | p=0.68 |
Table 8
Attractor model’s parameters.
| Parameter | Parameter value | Reference, remarks |
|---|---|---|
| JN,ii | 0.3157 nA | Calibrated based on pool of isolated data, also fitted on individual subjects’ data |
| JN,ij | 0.0646 nA | Calibrated based on pool of isolated data, also fitted on individual subjects’ data |
| µ0 | 45.8 Hz | Calibrated based on pool of isolated data, also fitted on individual subjects’ data |
| NDT | 0.27 s | Calibrated based on pool of isolated data, also fitted on individual subjects’ data |
| Bound | 0.32 nA | Calibrated based on pool of isolated data, also fitted on individual subjects’ data |
| a (Equation 15) | –0.99 | Calibrated based on pool of isolated data, also fitted on individual subjects’ data |
| b0 (Equation 15) | 1.32 | Calibrated based on pool of isolated data, also fitted on individual subjects’ data |
| b1 (Equation 15) | –0.165 | Calibrated based on pool of isolated data, also fitted on individual subjects’ data |
| k (Equation 15) | 5.9 | Calibrated based on pool of isolated data, also fitted on individual subjects’ data |
| I0 | 0.3255 nA | From Wang, 2002; Wong and Wang, 2006 |
| JA.ext | 0.00022 nA Hz–1 | From Wang, 2002; Wong and Wang, 2006 |
| τs | 0.1 s | From Wang, 2002; Wong and Wang, 2006 |
| dt | 0.0005 s | From Wang, 2002; Wong and Wang, 2006 |
| a (Equation 13) | 270 (V nC)–1 | From Wang, 2002; Wong and Wang, 2006 |
| b (Equation 13) | 108 Hz | From Wang, 2002; Wong and Wang, 2006 |
| d (Equation 13) | 0.154 s | From Wang, 2002; Wong and Wang, 2006 |
| γ | 0.641 | From Wang, 2002; Wong and Wang, 2006 |
| Noise_std | 0.025 | From Wang, 2002; Wong and Wang, 2006 |
| I_noise | 0.02 | From Wang, 2002; Wong and Wang, 2006 |
Additional files
-
MDAR checklist
- https://cdn.elifesciences.org/articles/83722/elife-83722-mdarchecklist1-v1.pdf
-
Supplementary file 1
This file contains supplementary tables that contains details of statistical analysis.
- https://cdn.elifesciences.org/articles/83722/elife-83722-supp1-v1.docx
