Testing Models of Cognition and Action Using Response Conflict and Multinomial Processing Tree Models
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
Dual-process perspectives have made substantial contributions to our understanding of behavior, but fundamental questions about how and when deliberate and automatic cognition shape action continue to be debated. Among these are whether automatic or deliberate cognition is ultimately in control of behavior, how often each type of cognition controls behavior in practice, and how the answers to each of these questions depends on the individual in question. To answer these questions, sociologists need methodological tools that enable them to directly test competing claims. We argue that this aim will be advanced by (a) using a particular type of data known as response conflict data and (b) analyzing those data using multinomial processing tree models. We illustrate the utility of this approach by reanalyzing three samples of data from Miles et al. (2019) on behaviors related to politics, morality, and race.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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