Normative Data for Forty, Morphing, Line Drawn Picture Sets
Bibliographic record
Abstract
The updating of perceptual representations is important to a number of areas of psychology including the areas of set shifting, perserveration, theory of mind, perceptual learning, and our own interest in mental model updating. Many tasks that are used to detect such updating use simple stimuli such as motor sequences. When more complex stimuli are used it is often difficult to determine the importance of shifts, because normative data are not available. To better characterize how and when people update perceptual representations of ambiguous stimuli, we measured how people change their reports of percepts of line drawings that gradually morph (over 15 iterations) from one object to another. Here we present normative data for forty picture series that morphed from an animate to an inanimate object (or vice versa if shown in reverse order) or morphed within the animate and inanimate classes. When a participant goes from labeling an image sequence by the first label to a new label, an update to their perceptual representation can be inferred. The number of first image labels was used to measure of how long it takes participants to update. 178 participants labeled the pictures in our sets. Each set was rated by an average of 45 people (min =35, max = 65). On average participants updated from the first representation after 7 (± 0.91) pictures (min = 4.8, max = 9.7). Naming consistency for individual images ranged from 9 percent to 95 percent with a mean of 64 (± 21) percent. These picture sets are easy to administer and have been used within vastly different participant populations (3 and 5 year old children, healthy seniors, brain damaged persons). Given the perceptual simplicity these stimuli are also useful for EEG and fMRI studies. 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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".