A Framework on Polarization, Cognitive Inflexibility, and Rigid Cognitive Specialization
Bibliographic record
Abstract
Polarization is pervasive in the current sociopolitical discourse. Polarization tends to increase cognitive inflexibility where people become less capable of updating their beliefs upon new information or switching between different ways of thinking. Cognitive inflexibility can in turn increase polarization. We propose that this positive feedback loop between polarization and cognitive inflexibility is a form of threat response that has benefited humans throughout their evolutionary history. This feedback loop, which can be driven by conflict mindset, group conformity, and simplification of information, facilitates the formation of strong bonds within a group that are able to eliminate threats and increase individual fitness. Although cognitive inflexibility is conventionally seen as maladaptive, here we argue that cognitive inflexibility may be an adaptation under polarization. That is, in a highly polarized society most people only interact with members of their own social group, without having to confront perspectives from another group or interacting with out-group members. In this context, cognitive inflexibility creates rigid cognitive specialization, a set of cognitive traits that allow people to operate efficiently within their social circles but not outside of it. Although rigid cognitive specialization benefits individuals in the short term, it may lead to more polarization over the long run, and thus produce more conflict between groups. We call on future research to examine the link between cognitive inflexibility and rigid cognitive specialization.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".