Fading enhancement? Exploring the impact of touch timing on target enhancement in multiple-object tracking (MOT)
Bibliographic record
Abstract
Many everyday tasks such as driving a car or playing team sports require keeping track of the positions of several independently moving items among others. This ability to select and keep track of the locations of multiple targets among identical non-targets (distractors) is called multiple-object tracking (MOT) and is thought to provide critical location information for performing actions toward the tracked targets (e.g., Pylyshyn, 2001). In support of this, our lab found reduced MOT performance when participants had to touch targets in MOT while tracking as compared to touching distractors. Though the theoretical framework supporting MOT is debated, several studies have found support for attentional enhancement of target locations during MOT. In the present study, we sought to investigate the impact of touch on the attentional enhancement of MOT targets by modulating what item was touched (target, distractor in MOT) and when the touch occurred in the trial (early, late, or both early and late). We hypothesized that if the attentional enhancement of targets decreased over time and was impacted by touch, touches that occurred later in the trial would have a more considerable impact on tracking performance relative to those that occurred earlier in the trial. In support of this, error rates were significantly lower for targets that were touched later in the trial compared to earlier. Interestingly, the impact of touch timing differed based on the item in MOT that was touched. For touched distractors, error rates did not differ based on when the distractor was touched, but instead RTs to decide if the touched distractor was a target were slower when it was touched later in the trial. Taken together, these findings provide evidence of a shared mechanism employed in MOT and visually guided touch that may be differentially impacted the item touched and the timing.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".