Shifting the balance between goals and habits: Five failures in experimental habit induction.
Bibliographic record
Abstract
Habits are repetitive behaviors that become ingrained with practice, routine, and repetition. The more we repeat an action, the stronger our habits become. Behavioral and clinical neuroscientists have become increasingly interested in this topic because habits may contribute to aspects of maladaptive human behavior, such as compulsive behavior in psychiatry. Numerous studies have demonstrated that habits can be induced in otherwise healthy rats by simply overtraining stimulus-response behaviors. However, despite growing interest in this topic and its application to psychiatry, a similar body of work in humans is absent. Only a single study has been published in humans that shows the effect of extensive training on habit expression. Here, we report five failed attempts to demonstrate that overtraining instrumental behavior leads to the development of inflexible habits in humans, using variants of four previously published outcome devaluation paradigms. Extensive training did not lead to greater habits in two versions of an avoidance learning task, in an appetitive slips-of-action task, or in two independent attempts to replicate the original demonstration. The finding that these outcome devaluation procedures may be insensitive to duration of stimulus-response training in humans has implications for prior work in psychiatric populations. Specifically, it converges with the suggestion that the failures in outcome devaluation in compulsive individuals reflect dysfunction in goal-directed control, rather than overactive habit learning. We discuss why habits are difficult to experimentally induce in healthy humans, and the implications of this for future research in healthy and disordered populations. (PsycINFO Database Record
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".