When the Whole is Less than the Parts: Gestalt Grouping Degrades Depth Magnitude Percepts
Bibliographic record
Abstract
The amount of depth perceived between a vertical line pair is markedly and consistently reduced when horizontal lines connect the pair to form a closed object (Deas et al, VSS, 2013). This phenomenon appears to be related to the operation of Gestalt grouping principles, however their role has not been evaluated systematically. Here we assess the contribution of specific grouping cues (connectedness, proximity and similarity) to modulations of perceived depth in simple line stimuli. In all experiments we presented four equally spaced vertical lines and manipulated the object interpretation of the central test pair. As in our previous work, the baseline comparison consisted of the set of four vertical lines (in isolation) contrasted with a 'closed object' version in which the central pair was connected by horizontal lines. In subsequent conditions we embedded the closed object in an array of equal-length horizontal lines (flankers), positioned above and below the horizontal connecting lines. We varied the colour of the connectors and flankers such that all lines matched or had opposite contrast polarity. In all conditions observers estimated the relative separation in depth of the central pair of vertical lines using a pressure-sensitive sensor. The amount of estimated depth was dependent on the perceived connectedness of the horizontal and vertical lines. Depth percepts were most disrupted when the horizontal connectors and vertical lines matched in colour. Perceived depth increased slightly when the connectors had opposite contrast polarity, but increased dramatically when flankers were added. Thus, as grouping cues were added to counter the interpretation of a closed object, the depth degradation effect was systematically eliminated. Our results confirm that depth perception for simple stimuli is dependent on figural grouping following Gestalt principles. Further, we propose that the modulation of depth from disparity is object specific, occurring within, rather than between, closed objects. Meeting abstract presented at VSS 2014
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".