Quantify how social observation modulates performance-monitoring ERPs—error-related negativity (ERN) and correct-related negativity (CRN)—at short (within-block) and long (between-block) timescales using single-trial mixed-effects modeling in a two-session social flanker paradigm, and release a fully reproducible analysis workflow.
ERN and CRN index performance monitoring following error and correct responses, respectively. Prior work shows the ERN is sensitive to the motivational significance of errors, which increases under social observation; however, most studies rely on trial-averaged ERPs, potentially obscuring meaningful temporal dynamics. In this project, participants completed a flanker task twice (once under social observation, once alone). We extracted single-trial ERN/CRN amplitudes and used mixed-effects models to test whether social observation alters their trajectories over short (within blocks) and long (across blocks) timescales. Social observation selectively shaped short-timescale dynamics: in observed blocks, ERN magnitudes increased across trials while CRN magnitudes decreased; these trends were not evident when participants performed the task alone. Over longer timescales, both ERN and CRN declined across blocks regardless of social context, consistent with a vigilance decrement. To our knowledge, this is the first demonstration that social observation influences performance-monitoring trajectories over short timescales. These results underscore the value of single-trial, time-resolved analyses (beyond trial averages) and lay the groundwork for testing whether social observation interacts with individual differences in motivation/affect to shape performance-monitoring dynamics.
A visual roadmap of planned releases will live at docs/roadmap.drawio (edited via diagrams.net with GitHub integration so changes are tracked as commits).
Below is a brief guide to what is currently published on main:
Preprocessing code for EEG using the MADE pipeline.
Citation: Debnath, R., Buzzell, G. A., Morales, S., Bowers, M. E., Leach, S. C., & Fox, N. A. (2020). The Maryland analysis of developmental EEG (MADE) pipeline. Psychophysiology, 57(6), e13580.
Scripts to tabulate MADE outputs into analysis-ready CSV spreadsheets (e.g., 2_trial_level_erp_compute_control_n200Andn100.m).
R scripts for primary analyses:
1_basic_behavior.R— Subject-level behavioral data summaries.2_LMM_trial_erp_400200..._4trialCountNewData_final.R— Main ERP models: trial-level ERN/CRN as a function of observation condition, trial, and block.4_LMM_trial_erp_400200..._forTestACC.Rand4_LMM_trial_erp_400200..._forTestRT.R— Trial-level behavioral outcomes as a function of accuracy, observation, block number, and trial number.
PsychoPy task code for the social flanker (see flanker-basic-v5/, task spreadsheets, and image assets).
Watch for our first public release (tagged on
main) with frozen code, data dictionaries, and figures.
This main branch contains completed releases for this project. For all work-in-progress, please switch over to the dev branches.
| Role | Name |
|---|---|
| Co-first authors / Analysis | Yanbin Niu*, Kianoosh Hosseini* |
| Project contribution | Andy Peña, Carlos Rodriguez |
| PI / Advisor | George A. Buzzell |
*Equal contribution.
If you are interested in contributing, please read our CONTRIBUTING.md file.