Authors’ Reply: Is the Pinball Machine a Blind Spot in Serious Games Research?
Bibliographic record
Abstract
We appreciate the insightful comments and reflections regarding our study on the impact of serious games on executive functions and their application in neurodiverse populations [1]. The suggestion to consider pinball machines as a tool within the serious games paradigm presents an interesting avenue for further exploration. At the time of our study, our focus was primarily on conventional and emerging digital technologies, such as virtual reality, mobile devices, and sensor-based interactions. However, we acknowledge that pinball, with its unique combination of physical and digital interactions, may offer valuable cognitive and therapeutic benefits, particularly in the context of executive function training. The references provided in the letter highlight historical and recent research supporting its potential applications in various populations, reinforcing the idea that this arcade technology could play a role in future serious game developments. Given the evidence presented on pinball’s ability to engage attention, impulse control, cognitive flexibility, and problem-solving skills, we recognize its potential as a tool to enhance executive function training. Future work in this area could explore the adaptation of pinball mechanics within digital serious games or investigate its direct application as a therapeutic tool in controlled settings. Additionally, we acknowledge that the development of assistive technologies for neurodiverse populations often encounters blind spots, where certain tools or approaches are overlooked. Our intention with the published article is to provide a roadmap for researchers, highlighting that there remains substantial work to be done in this area. By identifying these gaps, we aim to offer a starting point for ongoing and future investigations. Several studies have underscored the challenges and opportunities in designing technologies for neurodiverse users. For instance, Frauenberger et al. discuss the importance of involving neurodiverse children in the technology design process to ensure that their unique needs are met [2]. Similarly, Benton and Johnson highlight lessons from neurodiverse communities, emphasizing the necessity of tailored technological interventions [3]. These perspectives reinforce the need for comprehensive research and development efforts to address the diverse requirements of neurodiverse populations. We thank the authors of the letter for broadening the discussion on serious game technologies. Their insights open the door to new interdisciplinary research possibilities that could further enrich this field.
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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 | 0.009 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.032 | 0.036 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".