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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".