Dual Task Costs in Surround Motion Integration
Bibliographic record
Abstract
In a traditional dual-task paradigm, performance typically suffers in both tasks compared to performance on either task alone. However, paradigms in which dual task performance exceeds single task performance have gained a lot of attention in recent years. In one such example, Motoyoshi et al. (2014), found dual-task enhancement in a direction discrimination task that used Random Dot Kinematograms (RDKs) when a digit-identification RSVP task was administered simultaneously. They attributed this finding to a reduction of surround suppression that was caused by performing the dual task. We sought to replicate this finding. We used a staircase procedure to measure dot coherence thresholds in three conditions: 1) RDK with simultaneous RSVP stream, where participants reported the two numbers in the RSVP stream and then reported RDK direction; 2) RDK with simultaneous ignored RSVP stream, where participants reported only RDK direction; and 3) RDK presented alone, where participants reported the direction of the RDK. The first two conditions were similar to those used by Motoyoshi et al. (2014); condition 3 was added to determine the impact of including an ignored, centrally-fixated RSVP stream. Each participant completed all three conditions in a randomized order. In contrast to the pattern of results found by Motoyoshi and colleagues, coherence thresholds in the dual-task condition were significantly higher than the two single task conditions (which did not significantly differ from one another).The reasons for the contradictory findings are unclear, but our findings suggest that it may be premature to conclude that sensitivity to RDK direction is enhanced by dividing attention between two tasks. 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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".