Analysis of goal, feedback and rewards on sustained attention via machine learning
IntroductionSustaining attention is a notoriously difficult task as shown in a recent experiment where reaction times (RTs) and pupillometry data were recorded from 350 subjects in a 30-min vigilance task. Subjects were also presented with different types of goal, feedback, and reward.MethodsIn this...
Saved in:
| Main Authors: | Nethali Fernando, Matthew Robison, Pedro D. Maia |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-12-01
|
| Series: | Frontiers in Behavioral Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbeh.2024.1386723/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Predicting long-term memory via pupillometry
by: Oria Pitem, et al.
Published: (2025-07-01) -
The Connection Between Goals and Rewards in a Social Entrepreneurship
by: Supriyanto, et al.
Published: (2025-03-01) -
Evidence of physiological changes associated with single-session pre-frontal tDCS: a pilot study
by: Hannah N. Rembrandt, et al.
Published: (2025-02-01) -
Reward in Cash or Coupon? Joint Optimization of Referral Reward and Pricing
by: Fenfen Jiang, et al.
Published: (2025-02-01) -
Reward preferences to attract and retain Generation Z
by: Calvin Mabaso
Published: (2025-06-01)