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Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958).
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Decoding agency attribution using single trial error-related brain potentials.

Alba Gomez-Andres1,2, Xim Cerda-Company1,2,3, David Cucurell1,2

  • 1Cognition and Brain Plasticity Group [Bellvitge Biomedical Research Institute-IDIBELL], Barcelona, Spain.

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Summary
This summary is machine-generated.

Researchers can now differentiate self-made errors from externally caused ones using electroencephalography (EEG) signals. This breakthrough enhances brain-computer interface (BCI) applications by decoding brain activity related to agency errors.

Keywords:
EEGERPsdecodingerror-related potentialssense of agencysupport vector machine

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Biomedical Engineering

Background:

  • Distinguishing self-generated actions from external ones is crucial for learning and adaptation.
  • Error-related potentials (ErrPs), including the error-related negativity (ERN), are detectable in electroencephalography (EEG) signals upon error perception.
  • ErrPs are increasingly explored for brain-computer interface (BCI) applications.

Purpose of the Study:

  • To investigate the feasibility of using a support vector machine (SVM) classifier to differentiate self-agency errors from other-agency errors using EEG data at a single-trial level.
  • To analyze the temporal and spatial characteristics of error-related potentials (ErrPs) for agency attribution.

Main Methods:

  • Utilized electroencephalography (EEG) to record brain activity from 23 participants.
  • Employed a support vector machine (SVM) classifier to distinguish between self-agency and other-agency errors.
  • Analyzed key ErrP components, specifically the error-related negativity (ERN) and P600, focusing on their latency and topographical distribution.

Main Results:

  • Successfully demonstrated the ability to accurately distinguish between self/internal and other/external agency attributions of errors from EEG signals.
  • Confirmed that the classification accuracy is influenced by the latency and spatial distribution of ErrP features like ERN and P600.
  • Showcased the viability of single-trial classification for agency error detection.

Conclusions:

  • The study confirms the potential of EEG signal analysis, particularly ErrP features, for distinguishing self-generated from externally-generated errors.
  • These findings provide a novel approach for enhancing BCI systems by enabling more accurate decoding of user intent and error attribution.
  • The results open new avenues for developing adaptive learning systems and assistive technologies.