Parallel GNN-LSTM Model Predicting Working Memory Involvement during Language and Emotion Processing
Bibliographic record
Abstract
Working memory (WM) is a core cognitive system, strongly associated with the prefrontal cortex (PFC), crucial for task-relevant information storage and processing, serving as the main “relay” for higher cognition. Although WM-specific n-back tasks are examined, their measurable involvement in other cognitive processes remains less explored using computational models. Here, we developed and suggested a parallel GNN-LSTM model trained on WM task-fMRI data to predict its involvement in unseen language and emotion tasks. Integrating spatial and temporal information, the proposed GNN-LSTM model effectively learnt WM demand patterns and predicted WM demand when presented with non-WM task-fMRI data. As both the preliminary MLP and the GNN-LSTM models achieved over 90% accuracy, our approach was capable of generating a probability-based output corresponding to task demand and WM involvement, especially in language subtasks, math and story. This demonstration of cross-domain prediction, using WM signatures to create a “sensor” of cognitive demand alongside the model-based insights, offers a potential method for investigating cognitive resource allocation and informs the data-driven verification of WM theories.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".