Testing the Effectiveness of a Gamified Emotional Cognitive Bias Modification Task as an Intervention for Low Mood: Randomized Controlled Trial
Bibliographic record
Abstract
Background: Emotion recognition bias in depression is well-documented and is proposed to play a causal role in depression. A cognitive bias modification (CBM) intervention targeting the bias in emotional expression perception was developed, but despite robust training effects on emotion perception, the effect on mood was unreliable and weak. We propose a new gamified cognitive bias modification (GCBM) to address potential limitations that may attenuate therapeutic effects. Objective: This study aimed to investigate the effectiveness of a single session of GCBM on emotion perception and to assess whether the gamified version of the task would produce the same robust training effects on the interpretation of emotional expressions as the original CBM. The second aim was to compare the effectiveness of a single session of CBM training, CBM control (no training), and GCBM training on immediate mood. Methods: We report a between-subjects fully automated and web-based experimental study that recruited participants via the web from the general population (N=916). We tested the effectiveness of GCBM in changing participants' responses to ambiguous facial expressions. The primary outcome was emotion recognition bias, measured as the increased identification of happy faces. We also compared the effects of a single session of GCBM training (n=397), CBM training (n=400), and CBM control (n=119) conditions on immediate mood, measured using the Immediate Mood Scaler. Results: Results showed that participants in the GCBM intervention condition classified more ambiguous faces as "happy" after the training compared with controls, indicating an increased perception of happiness in ambiguous faces (B=1.57, P<.001). There was also evidence that GCBM training produced more positive changes in immediate mood compared with the CBM control condition (B=-3.64, P=.003) and compared with the CBM training condition (B=1.69, P=.048). Conclusions: GCBM demonstrated promising results in changing participants' emotion recognition bias to ambiguous facial expressions and enhance the immediate mood compared with both CBM and control conditions. These results suggest that GCBM holds promise to be a better alternative to CBM for addressing mood-related cognitive biases. Further exploration of GCBM's long-term effects on mood and its clinical application is needed.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".