Reviewer #3 (Public review): How attention simplifies mental representations for planning
Bibliographic record
Abstract
Human planning is efficient—it frugally deploys limited cognitive resources to accomplish difficult tasks—and flexible—adapting to novel problems and environments. Computational approaches suggest that people construct simplified mental representations of their environment, balancing the complexity of a task representation with its utility. These models imply a nested optimisation in which planning shapes perception, and perception shapes planning - but the perceptual and attentional mechanisms governing how this interaction unfolds remain unknown. Here, we harness virtual maze navigation to characterise how spatial attention controls which aspects of a task representation enter subjective awareness and are available for planning. We find that spatial proximity governs which aspects of a maze are available for planning, and that when task-relevant information follows natural (lateralised) contours of attention, people can more easily construct simplified and useful maze representations. This influence of attention varies considerably across individuals, explaining differences in people’s task representations and behaviour. Inspired by the ‘spotlight of attention’ analogy, we incorporate the effects of visuospatial attention into existing computational accounts of value-guided construal. Together, our work bridges computational perspectives on perception and decision-making to better understand how individuals represent their environments in aid of planning.Humans have an impressive ability to plan. Theoretical models in computer science propose that instead of using all the available information in a scene, a decision-maker should form a simplified mental representation of their environment over which they plan. However, little is known about how perceptual and attentional processes shape the planning process in humans. We find that people form simplified mental representations in line with the natural contours of spatial attention, whereby information limited to a visual hemifield is more readily available for planning, like a spotlight illuminating a part of the environment. We develop a novel computational model of the effects of attention on planning and characterise systematic variation between individuals in how they simplify their mental representations.
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.
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 | 0.043 | 0.216 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.021 | 0.010 |
| Insufficient payload (model declined to judge) | 0.068 | 0.038 |
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".