Bibliographic record
Abstract
Spatial memory is an important facet of human cognition – it allows users to learn the locations of items over time and retrieve them with little effort. In human-computer interfaces, a strong knowledge of the spatial location of controls can enable a user to interact fluidly and efficiently, without needing to visually search for relevant controls. Computer interfaces should therefore be designed to provide support for developing the user’s spatial memory,and they should allow the user to exploit it for rapid interaction whenever possible. However, existing systems offer varying support for spatial memory. Many modern interfaces break the user’s ability to remember spatial locations, by moving or re-arranging items; others leave spatial memory underutilised, requiring slow sequences of mechanical actions to select items rather than exploiting users’ strong ability to index items and controls by their on-screen locations. The aim of this paper is to highlight the importance of designing for spatial memory in HCI. To do this, we examine the literature using an abstract-to-concrete approach. First, we identify important psychological models that underpin our understanding of spatial memory, and differentiate between navigation and object-location memory (with this review focusing on the latter). We then summarise empirical results on spatial memory from both the psychology and HCI domains, identifying a set of observable properties of spatial memory that can be used to inform design. Finally, we analyse existing interfaces in the HCI literature that support or disrupt spatial memory, including space-multiplexed displays for command and navigation interfaces, different techniques for dealing with large spatial data sets, and the effects of spatial distortion. We intend for this paper to be useful to user interface designers, as well as other HCI researchers interested in spatial memory. Throughout the text, we therefore emphasise important design guidelines derived from the work reviewed, as well as methodological issues and topics for future research.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".