Fast and slow temporal integration in visual word recognition: A demonstration of the Presentation of Parts in Noise (POPiN) paradigm
Bibliographic record
Abstract
The visual system is constantly bombarded with continuous streams of information. As such, a primary challenge in visual perception is determining which stimuli should be integrated or segregated across time. Take the example of a film being presented at the standard rate of 48Hz. Subjectively, we perceive the individually presented frames as a continuous stream of information. However, if that film strip is slowed to a presentation rate of 1Hz, we then perceive the frames as individual units. Critically, there seems to be a threshold presentation rate that characterizes a shift in the perception of the film as continuous stream of information to the perception of segregated frames. This study focused on the temporal integration of visual word recognition. The experiments employed a novel word identification paradigm wherein target words were broken down into parts and presented, with noise, along a rapid serial visual presentation (RSVP) stream (e.g., HXMX, XOXE, XOMX, HXXE, would be a typical presentation stream for the target word ‘HOME’). Experiment 1 demonstrated that changing the presentation rate (43Hz, 22Hz and 11Hz) of the RSVP stream led to qualitatively different approaches to target integration. At fast rates, all target letters were integrated automatically. However, at slow rates, target identification necessitated a conscious, ‘string building’ strategy. Experiment 2 validated the claim for qualitatively different approaches to integration; performance on a concurrent working memory task was impaired only at the slow presentation rates. Together, the results suggest that temporal integration in visual work recognition works according to ~100ms integration windows. Integration beyond that window is still possible, but necessitates top-down working memory influences. Meeting abstract presented at VSS 2013
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".