Estimating Latent Attentional States Based on Simultaneous Binary and Continuous Behavioral Measures
Bibliographic record
Abstract
Cognition is a complex and dynamic process. It is an essential goal to estimate latent attentional states based on behavioral measures in many sequences of behavioral tasks. Here, we propose a probabilistic modeling and inference framework for estimating the attentional state using simultaneous binary and continuous behavioral measures. The proposed model extends the standard hidden Markov model (HMM) by explicitly modeling the state duration distribution, which yields a special example of the hidden semi-Markov model (HSMM). We validate our methods using computer simulations and experimental data. In computer simulations, we systematically investigate the impacts of model mismatch and the latency distribution. For the experimental data collected from a rodent visual detection task, we validate the results with predictive log-likelihood. Our work is useful for many behavioral neuroscience experiments, where the common goal is to infer the discrete (binary or multinomial) state sequences from multiple behavioral measures.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".