A computational model for task inference in visual search
Bibliographic record
Abstract
We develop a probabilistic framework to infer the ongoing task in visual search by revealing what the subject is looking for during a search process. Based on the level of difficulty, two types of tasks, easy and difficult, are investigated in this work, and individual models are customized for them according to their specific dynamics. We use Hidden Markov Models (HMMs) to serve as a model for the human cognitive process that is responsible for directing the center of gaze (COG) according to the task at hand during visual search and generating task-dependent eye trajectories. This generative model, then, is used to estimate the likelihood term in a Bayesian inference formulation to infer the task given the eye trajectory. In the easy task, focus of attention (FOA) often lands on targets, whereas in the difficult one, in addition to the on-target foci of attention, deployment of attention on nontarget objects happens very often. Therefore, we suggest a single-state and a multi-state HMM to serve as the cognitive process model of attention for the easy and difficult tasks, respectively.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".