Benefits of Distributional Analyses in Visual Search: \nBounded Exponential Distributions Falsify Dichotomous Architectures of Search
Bibliographic record
Abstract
Visual search is one of the most common paradigms used to study attention, for it allows the effective mimicking of tasks we perform naturally in our environment while maintain a larger amount of control over possible confounding variables. Although the paradigm in of itself has been quite beneficial to the field of attention research, the analyses that accompany it, focused predominantly on mean response times, and their positive slopes through increasing set sizes, have been demonstrated to be severely limited when describing the underlying architecture of search (parallel versus serial). In addition, the omnipresent skew of response times distributions nullify the possible interpretations typically associated with central tendency measures such as means. \nWe investigated how distributional analyses, which assess the entire response time distribution could accurately describe changes in response times through typical manipulations of visual search paradigms (set size, target presence and difficulty). We used the Weibull distribution, a left-bounded distribution to fit the response time data. Results demonstrated that search in of itself is not a dual architecture that changes under search difficulty, but a single mechanism that simply increases the duration of search when the difficulty conditions increased. The Weibull is therefore strongly recommended when analyzing response time data collected in visual search paradigms.
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 | 0.033 | 0.275 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".