Experiential History as a Tuning Parameter for Attention
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p class="p1">[Peer commentary on “Visual selection: usually fast and automatic; seldom slow and volitional,” by J. Theeuwes]. <em>Journal of Cognition</em>. <p class="p3">In his current opinion piece, Theeuwes emphasizes the role of selection history as a third source of attentional selection, beyond top-down and bottom-up mechanisms, thus challenging traditional dual-process models of attention. While we agree that selection history impacts the allocation of attention, our own work suggests that this terminology may be too restrictive, and propose the simple term <em>history</em> as a better reflection of the impact of learning on our selection biases. Furthermore, we propose that the role of selection/experiential history on attention may not be as a unique third source of attentional selection, but rather as a tuning parameter, allowing certain categories of item to be endowed with greater task-based or feature-driven salience in a context and history dependent manner. This conceptualization presents an alternative to abandoning dual-process models of attention altogether. Rather, we can reimagine how task-based and feature-driven processes may be controlled by past experience in a dynamic and adaptable system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it