Classifying Brain State in Sentence Polarity Exposure: An ANN Model for fMRI Data
Bibliographic record
Abstract
Brain being the most complex organ of human body in which millions of neuron are connected to each other, and pass information in processing of thoughts, emotions, motor activities and linguistic phenomenon. With the advent of non-invasive neuro-anatomical analysis methods like PET scan, fMRI it is now easy to measure neuronal changes in brain. This study analyses the neuronal activity in the brain in sentence polarity detection task using multilayer perceptron classification methodology. The whole brain is divided into almost 5000 three-dimensional volume called voxels from which prominent voxels are selected using symmetrical uncertainty based on entropy for the classification of brain state. The proposed method achieved significantly higher accuracy in classifying brain state in the processing of affirmative and negative sentences. The result obtained also shows that certain brain regions like left dorsolateral prefrontal cortex (LDLPFC) and calcarine sulcus (CALC) are prominent areas which are deterministic in classification of affirmative and negative sentences in brain while right posterior pre-central sulcus (RPPREC) and right supramarginal gyrus (RSGA) are less contributing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".