I want to media multitask and I want to do it now: Individual differences in media multitasking predict delay of gratification and system-1 thinking
Bibliographic record
Abstract
Media multitasking, the concurrent use of multiple media forms, has been shown to be related to greater self-reported impulsivity and less self-control. These measures are both hallmarks of the need for immediate gratification which has been associated with fast, intuitive 'system-1' decision making, as opposed to more deliberate and effortful 'system-2' decision making. In Study 1, we used the Cognitive Reflection Task (CRT) to examine whether individuals who engage heavily in media multitasking differ from those who are light media multitaskers in their degree of system-1 versus system-2 thinking. In Study 2 we examined whether heavy and light media multitaskers differ in delay of gratification, using the delay discounting measure which estimates the preference for smaller immediate rewards, relative to larger delayed rewards in a hypothetical monetary choice task. We found that heavy media multitaskers were more likely than light media multitaskers to endorse intuitive, but wrong, decisions on the CRT indicating a greater reliance on 'system-1' thinking. Heavy media multitaskers were also willing to settle for less money immediately relative to light media multitaskers who were more willing to wait for the larger delayed reward. These results suggest that heavy media multitaskers have a reactive decision-making style that promotes current desires (money, ease of processing) at the expense of accuracy and future rewards. These findings highlight the potential for heavy media multitaskers to be at risk for problematic behaviors associated with delay discounting - behaviors such as substance abuse, overeating, problematic gambling, and poor financial management.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".