NEURAL MARKERS OF SELF-OTHER DIFFERENTIATION DURING DYNAMIC JOINT ACTION
Bibliographic record
Abstract
Joint actions, in which two or more people coordinate their actions with each other to achieve a common goal, are ubiquitous in daily life. Examples range from moving furniture with a friend to musical ensemble performance. Despite the ubiquity of joint actions, researchers know relatively little about the underlying neural processes that operate during real-world, dynamic joint action. Furthermore, recent research emphasizes the importance of using one’s own sensorimotor system to represent and simulate others’ contributions to the joint action to facilitate coordination. However, the notion that people represent their own and others’ contributions to a joint action using the same neural resources raises the question of how people nevertheless maintain a distinction between each person’s individual contributions. This dissertation will focus on delineating neural markers of self-other differentiation during dynamic joint action. In four experiments, I employ a joint sequence production paradigm in which pairs of participants take turns producing tones to match a metronome pace. I use electroencephalography (EEG) to examine the time course of neural activity associated with each person’s actions (i.e., taps) and sensory consequences (i.e., tones) as the sequence unfolds. In Chapters 2 and 3 (Experiments 1 and 2), I investigate whether there is a perceptual differentiation in the processing of sensory consequences that result from one’s own vs. others’ actions by measuring auditory event-related potentials (ERPs) elicited by self- and partner-produced tones. Together, the findings from Experiments 1 and 2 indicate that self-specific attenuation of the auditory P2 provides a neural marker of self-other differentiation at a perceptual level. The findings from Experiment 2 also show that orienting processes associated with the coordination requirements of a joint action enhance P2 amplitude for partner-produced tones, suggesting that people direct their attention to their partner’s tone onsets to better coordinate with them. In Chapter 3 (Experiment 3 and 4), I investigate whether there is a differentiation in the motor activity that is associated with each person’s actions by conducting novel analyses of the data previously reported in Experiments 1 and 2 to examine motor-related cortical oscillations during self- and partner-produced taps. Together, the findings from Experiments 3 and 4 indicate that motor-related suppression provides a neural marker of self-other differentiation at a motor level. The findings from Experiment 3 and 4 also show that the coordination requirements of a joint action affect the degree of motor-related suppression for a partner’s actions, suggesting that people simulate their partners action timing to better coordinate with them. Overall, this research suggests that distinct neural activity for one’s own contributions to a joint action is dynamically coupled with periods of neural activity that reflect the integration of a partner’s actions based on the coordination demands of the joint action. Together, the experiments presented in this dissertation provide important and direct implications for theoretical accounts of joint action, as they further our understanding of how people maintain a distinction between their own and their partners’ contributions to a joint action, while also dynamically integrating information about the timing of their partners’ actions and sensory consequences to better coordinate with them. More broadly, these experiments contribute to our understanding of disorders associated with self-other processing deficits, such as schizophrenia, and provide valuable insight into the development of effective paradigms for motor training and rehabilitation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".