Positive autobiographical memory recall does not influence temporal discounting: An internal meta-analysis of experimental studies
Bibliographic record
Abstract
People tend to discount the value of future rewards as the delay to receiving them increases. This phenomenon, known as temporal discounting, has been proposed to underlie many impulsive behaviors, such as drug abuse and overeating. Furthermore, steep temporal discounting has been observed in people with addiction and other impulsive disorders. Given the potential role of temporal discounting in maladaptive behaviors, there have been many efforts to find experimental manipulations that reduce temporal discounting, with the hope that these manipulations can be adapted into interventions with clinical utility. One class of manipulations that has held some promise involves recalling positive autobiographical memories prior to making intertemporal choices. Just as imagining positive personal events in the future has been shown to reduce temporal discounting, a few studies have shown that recalling positive events from the past reduces temporal discounting, especially if memory retrieval evokes positive affective states, such as gratitude and nostalgia. However, we failed to replicate these findings. Here we present a meta-analysis combining data from 14 studies done by the authors (n = 603) that involved within-subjects positive memory recall-based manipulations. In each study, temporal discounting was assessed using a monetary intertemporal choice task. The average effect size was small and not significantly different from zero. This finding helps elucidate the neurocognitive mechanisms of temporal discounting; whereas engaging the episodic memory system to imagine future events might promote more patience, engaging the episodic memory system to imagine past events does not.
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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 | 0.023 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.018 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".