Classification of Cognitive Strategies by the underlying processing stages using Hidden semi-Markov Models
Bibliographic record
Abstract
When doing a cognitive task, people can employ different cognitive strategies. A strategy consists of different cognitive processing stages, which is how different strategies can be differentiated. A novel machine learning method developed by Anderson et al., 2016 is able to model cognitive processing stages in EEG, MEG, and fMRI as a hidden semi-Markov model, calling it Hidden semi-Markov Model Multivariate Pattern Analysis (HsMM-MVPA). This method works across subjects, so among other things it seems to be able to deal with the inter-subject variability of EEG data. This leads to the hypothesis that HsMM-MVPA could potentially be used to predict what cognitive strategy someone used in new, unseen data. To test this hypothesis, EEG data collected from a group of subjects who performed\na multiplication task with self-reported cognitive strategies was used. Subjects reported either knowing the answer to a multiplication problem from memory ("retrieval"), or had to compute the answer ("procedural"). We estimated hidden semi-Markov models on some of\nthe subjects and tested how well these models could predict what strategy was used on the other subjects. The models are able to correctly identify retrieval-strategies, but tend to be less sensitive to the procedural-class. This seems to be because the retrieval-strategy is more consistent. HsMM-MVPA can be used for classification, but might fare better with more consistent cognitive strategies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".