Reward Feedback Mechanism in Virtual Reality Serious Games in Interventions for Children With Attention Deficits: Pre- and Posttest Experimental Control Group Study
Bibliographic record
Abstract
Background Virtual reality (VR) serious games, due to their high level of freedom and realism, influence the rehabilitation training of inhibitory control abilities in children with attention-deficit/hyperactivity disorder (ADHD). Although reward feedback has a motivating effect on improving inhibitory control, the effectiveness and differences between various forms of rewards lack empirical research. Objective This study aimed to investigate the effectiveness of different forms of reward feedback on the inhibitory control abilities of children with attention deficits in a VR serious game environment. Methods This study focuses on children who meet the diagnostic criteria for ADHD tendencies, using a 2 (material rewards: coin reward and token reward) × 2 (psychological rewards: verbal encouragement and badge reward) factorial between-subject design (N=84), with a control group (n=15) for pre- and posttest experiments. The experimental group received VR feedback reinforcement training, while the control group underwent conventional VR training without feedback. The training period lasted 0.5 months, with each intervention session lasting 25 minutes, occurring twice daily with an interval of at least 5 hours for 28 sessions. Before and after training, the Swanson, Nolan, and Pelham, Version IV Scale (SNAP-IV) Scale, stop signal task, inhibition conflict task, and Simon task were administered to assess the hyperactivity index and the 3 components of inhibitory control ability. The pretest included the SNAP-IV Scale and 3 task tests to obtain baseline data; the posttest involved repeating the above tests after completing all training. Data were entered and analyzed using SPSS (IBM) software. Independent sample t tests were performed on the experimental and control groups’ pre- and posttest task results to determine whether significant differences existed between group means. Paired sample t tests were also conducted on the SNAP-IV Scale’s pre- and posttest results to assess the intervention effect’s significance. Results Reward feedback was more effective than no reward feedback in improving behaviors related to attention deficits in children. Material rewards showed significant effects in the Stop-Signal Task (F1=13.04, P=.001), Inhibition Conflict Task (F1=7.34, P=.008), and SNAP-IV test (F1=69.23, P<.001); mental rewards showed significant effects in the Stop-Signal Task (F1=38.54, P<.001) and SNAP-IV test (F1=70.78, P<.001); the interaction between the 2 showed significant effects in the Stop-Signal Task (F1=4.47, P=.04) and SNAP-IV test (F1=23.85, P<.001). Conclusions Combining material and psychological rewards within a VR platform can effectively improve attention-deficit behaviors in children with ADHD, enhancing their inhibitory control abilities. Among these, coin rewards are more effective than token rewards, and verbal encouragement outperforms badge rewards. The combined feedback of coin rewards and verbal encouragement yields the most significant improvement in inhibitory control abilities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".