The effects of directed and free self-monitoring on goal-directed and habitual decision-making
Bibliographic record
Abstract
Many studies on humans and animals have provided evidence for the contribution of goal-directed and habitual valuation systems in learning and decision-making. However, how the arbitration between these two systems is affected by other cognitive processes is not well known. Here, we study the effects of directed and free self-monitoring of one’s decisions on this arbitration. In our experiments, in a within-subject design, the subjects participated in a control and a two modified versions of the Two-step decision-making task, where we could measure each system’s contribution to decisions. We had two modified tasks. In both, every few trials subjects had to think about what they have experienced in the past trials in one of the two days. In one task, they had to designate which action was better and then report their confidence about this decision (directed self-monitoring task). In the other modification of task, they had to explain what had happened in the past few trials by talking (free self-monitoring task). We hypnotized that in both modified tasks, the behavior of the participants would shift toward goal-directed behavior because they need to think more about the structure of the task. Our experimental results showed that subjects indeed became more goal-directed in the directed self-monitoring task, but in the free self-monitoring task, they became more habitual. We would discuss the underlying reasons for these shifts in the behavior.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".