Escalating frustration - A replication attempt and extension of Yu et al. (2014)
Bibliographic record
Abstract
<ns3:p>Background Failures to obtain a desired reward, such as losing money in gambling, can lead to frustration. In gambling, this frustration has been shown to take the form of faster responses after losses compared with wins and non-gambling trials. In addition, reward omission or blockage can lead to more forceful responses. Yu and colleagues (2014) showed that the proximity to a reward and the effort already expended to acquire the reward increased participants’ response force and their retrospective self-reported frustration when the reward was blocked. Methods In this study, we attempted to replicate the findings of Yu and colleagues (2014) using the same experimental procedure. In each schedule, participants (N = 32) needed to complete an arrow direction task for varying numbers of times to win a reward but could be blocked at any stage. The response time (RT) and force of confirming the outcomes were used as indicators of ‘frustration’. In addition, to obtain a more real-time and objective measure of (negative) emotion, we measured facial electromyographic (EMG) activity over the corrugator supercilii (frowning muscle) and the zygomaticus (smiling muscle). Results Due to technical problems, our data on response force were invalid. In line with the original study, both goal proximity and exerted effort increased participants’ self-reported motivation in the task and frustration after being blocked. An exploratory analysis showed that unexpectedly participants were slower in confirming an outcome when they were blocked closer to the reward, while exerted effort did not influence the time taken to confirm the outcome. These RT data were consistent with self-reported surprise ratings, suggesting an orienting response. In the facial EMG data, we observed no difference between wins and losses in activity over the corrugator or the zygomaticus. Conclusion Taken together, these data suggest that reward blockage does not necessarily lead to behavioral or psychophysiological expressions of negative emotions such as frustration.</ns3:p>
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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 | 0.021 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".