Habits and behavioral complexity – dynamic and distinct constructs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Japanese term Mendokusai (めんどくさい) is used to describe situations where you just can’t be bothered. For example, it’s perfect for if you want to get a snack, but you are so comfy in your pajamas, lying on the couch, with your pet on your lap, and this episode of the series you’re binging is soooo good, and you should pause it but the remote is like all the way on the other side of the couch … so forget the snack – Mendokusai. Sometimes, even basic tasks can feel really complex. Phillips and Mullan (Citation2022) make the compelling case that both simple and complex behaviors can become habitual. We agree that some things we do are more complex than others – and few would dispute that, but where we do dispute their arguments is their operationalization and conceptualization of behavioral complexity and what it means for habit science. Phillips and Mullan (Citation2022) operationalize behavioral complexity as the product of the number of sub-actions or steps within a behavior and the proximity of reward from the behavior. We argue that 1 – complexity should be considered distinct from proximity of reward and habit and 2 – the complexity of a behavior is not a static attribute of a behavior in isolation, but rather a dynamic process dependent on context, task and actor.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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 it