Goal-directed behaviour is associated with decreased temporal discounting
Bibliographic record
Abstract
Reinforcement learning models are broadly classified as model-based algorithms, which use an internal model of the environment to plan and carry out goal-directed behaviour, or model-free algorithms, which lack such a model and behave in a more habitual manner. Humans differ in the extent to which their decision making resembles that of model-based or model-free algorithms; the degree of this resemblance is related to individual differences in compulsivity, and is also argued to be related to the ability to imagine and make decisions about the future. Here, we demonstrate that individuals who exhibit more model-based decision making tend to more frequently choose larger rewards later over smaller rewards sooner in an intertemporal choice task. However, surprisingly, model-free decision making was correlated with the specificity of personal future event narratives in an episodic future thinking task. Participants who provided more specific narratives had slower response times across all tasks, and may therefore have been more affected by time pressure in the reinforcement learning task, resulting in increased model-free decision making. We comment on the role of time pressure in behavioural tasks of the type used here. Future self-continuity, a construct conceptually related to imagining and making decisions about the future, was not found to be related to temporal discounting, model-based or model-free decision making, or episodic future thinking.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".