Using the P300 and ERN brain potentials to understand the effects of prism adaptation on attention.
Bibliographic record
Abstract
During prism adaptation (PA) a participant is asked to reach to targets while wearing glasses that shift their vision in one direction. Initially, the participant misses in the direction of the prism shift. However, after a number of reaches the participant recalibrates their movements in the opposite direction to compensate for the visual shift. Rightward PA has been used for many years as a method of rehabilitating patients with left visuospatial neglect (VSN), a leftward spatial attentional disorder, by shifting the patient's egocentric reference frame leftward, towards their neglect field. Similarly, left prisms, which result in a rightward shift in the egocentric reference frame, can be used to induce neglect-like behavior in healthy adults. Prior research involving event-related potentials (ERPs) has shown that, during PA, there are two brain potentials that are elicited: the P300, and the error-related negativity (ERN). The P300 is thought to index the role of the posterior parietal cortex (PPC) in context updating and learning, whereas the ERN is suggested to occur as part of an error processing system in the medial-frontal cortex (MFC). In the proposed study we will examine the roles of these brain potentials in creating the beneficial effects of PA on attention through recording ERPs while healthy adults adapt to varying degrees of leftward shifting prisms. The magnitude of these ERPs will then be correlated with behavioural changes on attention tasks that are induced following leftward PA. Ultimately, these findings may help us better understand how PA influences patients with VSN. Discipline: Psychology Honours Faculty Mentor: Dr. Christopher Striemer
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".