Encouraging the Voluntary Mobilization of Mental Resources by Manipulating Task Design: Explorative Study
Bibliographic record
Abstract
Background: Cognitive training is increasingly being considered and proposed as a solution for several pathologies, particularly those associated with aging. However, trainees need to be willing to invest enough mental effort to succeed and make progress. Objective: In this study, we explore how gamification in a narrative context (ie, the addition of visual game-like elements [GLEs] embedded in real-world contexts) could contribute increase in perceived playfulness (PP) and voluntary mental effort allocated to a cognitive task. In such context, narrative elements and GLEs can be designed to align with a commonly relatable scenario (like simulating fishing or gardening activity) to ground the task in familiar, real-world contexts. We also consider if the supposed effect of GLEs on PP and voluntary mental effort could endure while manipulating an intrinsic variable of the task (ie, by increasing cognitive solicitation). Methods: In total, 20 participants (average age 33.6, SD 8.6 y) took part in 3 cognitive tasks proposed in a numerical format: a classic version of the Corsi test (Classic Corsi, a spatial memory task), a playful version of the Classic Corsi test (Playful Corsi), with added visual GLEs in a narrative context, and a playful version of the Classic Corsi test with added cognitive solicitation, that is, mental motor inhibition (Playful Corsi Multi). We assessed the impact of visual GLEs and cognitive solicitation on PP (1 question) and mental workload (MWL) using NASA-Task Load Index (NASA-TLX) and workload profile (WP) questionnaires. Results: Results showed that PP was not influenced by interface's playful characteristics (Classic Corsi [mean 62.4, SD 8.8] vs Playful Corsi [mean 66, SD 8.8]; W=77; P=.30) but decreased the time necessary to complete the task (Classic Corsi [mean 10.7, SD 2.1 s] vs Playful Corsi [mean 6.8, SD 1.6 s]; W=209; P<.001) as well as performance (Classic Corsi [mean 92.4, SD 9.1] vs Playful Corsi [mean 88.2, SD 11.3]; W=140.5; P=.02). So, possibly, visual GLEs could raise the stakes of the task slightly and implicitly encourage people to go a bit faster. Furthermore, visual GLEs increased MWL regarding attentional resources (assessed by WP: Classic Corsi [mean 52.4, SD 10.9] vs Playful Corsi [mean 65.8, SD 10.9]; W=27.5; P=.04), while manipulating cognitive solicitation impacted MWL when linked to task requirements (assessed by NASA-TLX: Playful Corsi [mean 54.2, SD 9.4] vs Playful Corsi Multi [mean 67.5, SD 9.4]; W=35.5; P=.01) without impacting the performance to the task (Playful Corsi [mean 83.8, SD 13.9] vs Playful Corsi Multi [mean 94, SD 5.5]; W=27; P=.007). Thus, working on the way cognitive functions are solicited would be wiser than adding visual GLEs to improve users' voluntary mental effort while preserving performance. Conclusions: These results offer valuable insights to improve users' experience during gamified cognitive tasks and serious games.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".