The effect of feedback on 3D multiple object tracking performance and its transferability to other attentional tasks
Bibliographic record
Abstract
Attentional processes play an integral role in learning, affecting performance on most cognitive tasks. In addition, feedback - instant information delivered to the individual that guides their subsequent behavior in relevant situations - plays a critical role in the efficiency and quality of learning. However, its effects are not often empirically assessed. Multiple Object Tracking (MOT) tasks were developed to objectively assess real world attention, and have been used as cognitive training paradigms geared at improving attentional abilities. With training, there is a significant improvement in MOT performance; however, little is known about the transferability of attentional capacities from MOT tasks to similar cognitive tasks. The goal of this study was thus to assess whether performance on attentional capacities acquired during training on a 3D MOT task are transferrable to other measures of attention. The role of feedback was also investigated to determine whether performance, and its subsequent transferability to other measures, is affected by feedback. Forty typically developing adults participated in 4 testing sessions on consecutive days. On day 1, intellectual and attentional abilities were assessed along with a baseline measure of MOT without feedback. Participants were split into 2 experimental groups and assessed for three subsequent days (days 2 through 4): one group received feedback during the MOT task trials; the other group received no feedback. On day 4, all participants were re-assessed on the same attentional measures as well as the MOT to determine improvements from day 1. MOT performance resulted significantly higher for the feedback group, as defined by an increased speed threshold for tracking 4 out of 8 items. The feedback group also revealed better transferability to other cognitive tasks. The results indicate that feedback is an important component during a learning regiment and that it may affect transferability of cognitive abilities. Meeting abstract presented at VSS 2014
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".