Brain Activation and Psychophysiologic Interaction in Association with a Phonological Working Memory Task
Bibliographic record
Abstract
Brain activation within, and psychophysiologic interaction between, significantly activated regions in the brain obtained from a phonological working memory experiment on a single participant were studied. Given that working memory and speech processing are key functions of human behaviour, this type of investigation is of fundamental importance to our understanding of brain-behaviour relationships. The study objectives were to determine the areas that respond significantly to a phonological working memory task and to investigate the influence of babble noise on their activation and the psychophysiologic interactions (PPI) between the source region and those activated areas. Three conditions were used during functional magnetic resonance imaging (fMRI) scans which were working memory in quiet (WMQ), working memory in noise (WMN) and listening to babble noise (N). More voxels are activated in the right temporal lobe than in the left during N condition due to the non-speech stimulus. However, a higher mean stimulus efficacy (?) of the point of maximum intensity in the left temporal lobe causes its signal intensity to be higher than in the right temporal lobe. Both the WMQ and WMN conditions resulted in similar activated regions in the brain but with a higher number of activated voxels (NOV) during WMQ for the right hemispheric areas in association with the working memory task. This is due to the sensitivity of those regions in perceiving and performing the phonological working memory task in quiet to a level that actually exceeds the activation enhancement commonly associated with the performance of working memory task in noise. This is supported by the PPI results that performing the working memory task is less influenced by noise for that particular brain region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".