Extraction Dissonance: Not All Ensembles are Created Equal
Bibliographic record
Abstract
Our visual system is extremely proficient at extracting statistical information, such as the average size and density of objects. But what are the mechanisms supporting this? Here, we present a new methodology for exploring this issue, and use it to show that not all ensembles are treated alike by the visual system. To investigate the perceptual organization of ensemble structures, we began by examining the perception of Pearson correlation r in scatterplots. For plots with single data populations, discrimination performance is described simply: just noticeable differences (JNDs) are proportional to the distance from r=1. Our study expanded this to consider the case where each scatterplot not only contained a "target" population of black dots, but also an irrelevant "distractor" population of red dots (Fig. 1). Observers viewed two such scatterplots side-by-side (each containing both target and distractor populations), and were asked to identify the plot with the higher target correlation. There were 100 target dots in every condition. Interference from several distractor correlation values was determined by measuring JNDs for plots with various distractor dot numerosities: 100, 50, and 25 dots. Results showed a surprising effect: for target correlations of .3 with 100 and 50 distractor dots, discrimination declined considerably when the distractor correlation changed from .9 to .999. Meanwhile, for distractors of 25 dots, JNDs were low for both .9 and .999 distractor correlations (Fig. 2). This extraction dissonance, where discrimination is considerably different for ensembles with highly similar correlations, suggests that denser .999 populations may be represented similarly to a holistic unit, rather than an ensemble. More generally, our methodology provides exciting potential for exploring the level at which set of items can be perceived as a population, versus a single visual object. Meeting abstract presented at VSS 2016
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".