Bibliographic record
Abstract
Our visual experience of the world is one of diverse objects and events, each with particular colors, shapes, and motions. This experience is so coherent, so immediate, and so effortless that it seems to result from a single system that lets us experience everything in our field of view. But however appealing, this belief is mistaken: there are severe limits on what can be visually experienced. For example, in a display for air-traffic control it is important to track all moving items. For a single item, this can be done without problem. Three or four can also be tracked, although some degree of effort may be needed. As the number is increased further, accurate tracking becomes more and more difficult—and eventually, impossible. Performance is evidently affected by a factor within the observer which enables certain kinds of perception to occur, but is limited in some way. This factor is generally referred to as attention. At various times, attention has been associated with clarity of perception, intensity of perception, consciousness, selection, or the allocation of a limited “resource” enabling various operations (see Hatfield, 1998). During the past several decades, considerable progress has been achieved by focusing on the idea of selection (Broadbent, 1982). In particular, attention can be productively viewed as contingently selective processing. This can be embodied in various ways by various processes—there need not be a single quantity identified with all forms of attention, or a single site where it operates (Allport, 1993; Tsotsos, 2011). Although “paying attention” is often considered to be a unitary operation, it may simply refer to the control of one or more selective processes, ideally in a co-ordinated way. While this view has some cost in terms of conceptual simplicity, it can help make sense of a large set of phenomena. This article surveys several of the major issues in our understanding of attention and how it relates to perception. It focuses on vision, since many—if not all—considerations are similar for all sensory modalities, and the level of understanding achieved in this domain is currently the most advanced.
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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) | 0.000 | 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 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".