Excessive social media users demonstrate impaired decision making in the Iowa Gambling Task
Bibliographic record
Abstract
BACKGROUND AND AIMS: Online social networking sites (SNSs) like Facebook provide users with myriad social rewards. These social rewards bring users back to SNSs repeatedly, with some users displaying maladaptive, excessive SNS use. Symptoms of this excessive SNS use are similar to symptoms of substance use and behavioral addictive disorders. Importantly, individuals with substance use and behavioral addictive disorders have difficulty making value-based decisions, as demonstrated with paradigms like the Iowa Gambling Task (IGT); however, it is currently unknown if excessive SNS users display the same decision-making deficits. Therefore, in this study, we aimed to investigate the relationship between excessive SNS use and IGT performance. METHODS: We administered the Bergen Facebook Addiction Scale (BFAS) to 71 participants to assess their maladaptive use of the Facebook SNS. We next had them perform 100 trials of the IGT to assess their value-based decision making. RESULTS: We found a negative correlation between BFAS score and performance in the IGT across participants, specifically over the last block of 20 trials. There were no correlations between BFAS score and IGT performance in earlier blocks of trials. DISCUSSION: Our results demonstrate that more severe, excessive SNS use is associated with more deficient value-based decision making. In particular, our results indicate that excessive SNS users may make more risky decisions during the IGT task. CONCLUSION: This result further supports a parallel between individuals with problematic, excessive SNS use, and individuals with substance use and behavioral addictive disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".